A companion to digital humanities (2001)(649s).pdf - FTP Directory ...

Notes on Contributors 

Howard Besser is Director of the Moving Image Archive and Preservation Program at 

New York University's Tisch School of the Arts. He has been extensively involved in the 

movement to construct digital libraries and museums, and has taught, researched, and 

published extensively in the areas of technological change, and the social and cultural 

impact of new information environments. 

John Bradley was the original author and designer of TACT from 1985 until 1992. He 

now works within the Centre for Computing in the Humanities at King's College London, 

where he is involved in both teaching and research. His research interests focus on 

alternative approaches to computer-assisted textual analysis, and on issues that arise from 

the modeling of historical data in computing systems. He has played an important role in 

the design and implementation of systems behind the Prosopographies of the Byzantine 

Empire/World and of Anglo-Saxon England, of the Clergy of the Church of England, of 

the Corpus Vitrearum Medii Aevi and the Corpus of Romanesque Sculpture in Britain 

and Ireland, of the Stellenbibliographie zum "Parzival" Wolframs von Eschen-bach, of 

the Modern Poetry in Translation website, and a number of other research projects. 

John Burrows is Emeritus Professor of English at the University of Newcastle, NSW, 

Australia, and was the Foundation Director of the Centre for Literary and Linguistic 

Computing in that university. His many publications in the field of computational 

stylistics include Computation into Criticism (1987). In 2001 he became the second 

recipient of the Roberto Busa Award for Humanities Computing. The lecture he gave on 

that occasion appeared in Computers and the Humanities (February 2003). 

Roberto A. Busa entered the Jesuit order in 1933, and was ordained priest on May 20, 

1940. He is Professor of Philosophy at Aloisianum's Department of Philosophy in 

Gallarate, at Gregorian Pontifical University in Rome, and at the Catholic University in 

Milan. He is internationally recognized as the pioneer of computational linguistics. 

Hugh Craig is Director of the Centre for Literary and Linguistic Computing at the 

University of Newcastle, NSW, Australia. He also teaches English in the School of 

Language and Media where he is currently Head of School. His research in recent years 

has been in computational stylistics, applying evidence from the frequencies of very 

common words to authorial and stylistic problems in Early Modern English literature. 

Greg Crane is Winnick Family Chair of Technology and Entrepreneurship, Professor of 

Classics and Director of the Perseus Project at Tufts University. Originally trained as a 

classicist, his current interests focus more generally on the application of information 

technology to the humanities. 

Marilyn Deegan has a PhD in medieval studies: her specialism is Anglo-Saxon medical 

texts and herbals and she has published and lectured widely in medieval studies, digital 

library research, and humanities computing. She is Director of Research Development, 

Centre for Computing in the Humanities, King's College London, and was formerly

Director of Forced Migration Online at the Refugee Studies Centre at Oxford University, 

a major digital library and portal for materials concerned with all aspects of refugee 

studies. She is Editor-in-Chief of Literary and Linguistic Computing, the Journal of the 

Association for Literary and Linguistic Computing, and Director of Publications for the 

Office for Humanities Communication based at King's College London. Dr. Deegan has 

recently published a book, Digital Futures: Strategies for the Information Age, with 

Simon Tanner. 

Johanna Drucker is currently the Robertson Professor of Media Studies at the 

University of Virginia, where she is Professor in the Department of English and Director 

of Media Studies. She helped establish the Speculative Computing Laboratory in 2000 to 

explore experimental projects in humanities computing. She is well known for her work 

in the history of written forms, typography, design, and visual poetics. Her scholarly 

books include: Theorizing Modernism (1994), The Visible Word: Experimental 

Typography and Modern Art (1994); The Alphabetic Labyrinth (1995), and The Century 

of Artists' Books (1995). Her most recent collection, Figuring the Word, was published in 

November 1998. 

Harrison Eiteljorg, II, is a classical archaeologist who specializes in the architecture of 

classical Greece. He has worked to use computer technology in his own work and to 

explore how that technology can best be applied to the work of archaeologists generally. 

Charles Ess is Distinguished Research Professor, Interdisciplinary Studies, Drury 

University (Springfield, Missouri) and Professor II in the Applied Ethics Programme, 

Norwegian University of Science and Technology (Trondheim). He researches, lectures, 

and publishes on computer-mediated communication (CMC) and Information Ethics, 

especially from cross-cultural perspectives; with Fay Sudweeks, organizes conferences 

and edits publications on cultural attitudes toward technology and CMC. 

Ichiro Fujinaga is an Assistant Professor at the Faculty of Music at McGill University 

and the Chair of the Music Technology Area. He has degrees in music/percussion and 

mathematics from the University of Alberta, and a Master's degree in music theory, and a 

PhD in music technology from McGill University. 

Michael Greenhalgh has been interested in computing applications in the humanities 

since playing with a Commodore PET-2001 in 1977 at Leicester University, England. 

Graduating to large Cyber machines, PDF 11s and then VAXes, and writing books on 

them, he started teaching humanities computing courses in 1980. Elected to the Sir 

William Dobell Foundation Chair in Art History at the Australian National University in 

1987, he introduced similar courses there and, using powerful graphics machines, was 

ready with over 300 digitized images to start his web-server "ArtServe" 

(http://rubens.anu.edu.au) in January 1994. This server now offers over 190,000 images, 

and receives about 1.3 million hits per week. Since 1997, his lecturing and seminar work 

has been done exclusively over the network from servers, via a video projector.

Jan Hajič is an Associate Professor of Computational Linguistics at Charles University, 

Prague, Czech Republic. His interests range from morphology of inflective languages to 

syntax and treebanking to machine translation, using extensively statistical methods in 

natural language processing. He has previously worked at Johns Hopkins University 

(Maryland), and at IBM Research (New York). 

Susan Hockey was Professor of Library and Information Studies and Director of the 

School of Library, Archive, and Information Studies at University College London until 

July 31, 2004. She was Chair of the Association for Literary and Linguistic Computing 

(1984–97) and a member, twice Chair, of the Steering Committee for the Text Encoding 

Initiative. Her current interests are markup technologies for the humanities and the 

history of humanities computing. 

Nancy Ide is Professor and Chair of Computer Science at Vassar College in 

Poughkeepsie, New York. She has been involved in humanities computing and 

computational linguistics for 20 years. She was president of the Association for 

Computers and the Humanities from 1985 to 1995, and is currently co-editor of the 

journal Computers and the Humanities. She is also co-editor of the Kluwer book series 

Text, Speech, and Language Technology, and has co-directed the past four EUROLAN 

summer schools on various topics in computational linguistics. In 1987 she spearheaded 

the Text Encoding Initiative and served on its steering committee until 1997. She has 

published numerous papers on the application of statistical methods to language analysis, 

including computational lexicography, word sense disambiguation, and discourse 

analysis. Most recently she has been involved in developing standards for the 

representation of linguistically annotated resources, the creation of the American 

National Corpus, the development of an "intelligently searchable" corpus for historical 

research comprising materials from the Franklin D. Roosevelt Library, and adapting 

language processing practices to the Semantic Web. 

Michael Jensen was recently appointed Director of Web Communications for the 

National Academies: the National Academy of Sciences, the National Research Council, 

the Institute of Medicine, and the National Academy of Engineering. He remains Director 

of Publishing Technologies at the National Academies Press, which makes more than 

2,800 books (more than 500,000 pages) fully searchable and browsable online for free. In 

the mid-1990s, he helped establish Project Muse, the pioneering online journals project of 

Johns Hopkins University Press. For the University of Nebraska Press, he produced the 

first searchable publisher's catalogue available on the Internet, via Telnet, in 1990. 

Matthew G. Kirschenbaum is Assistant Professor of English and Digital Studies at the 

University of Maryland, College Park. He does both theoretical and applied work in the 

digital humanities, participating in projects from the William Blake Archive to the 

Electronic Literature Organization. He has long-standing interests in images, interface, 

and visualization, and is co-developer of a software tool called the Virtual Lightbox. His 

book-in-progress, entitled Mechanisms: New Media and the New Textuality, is 

forthcoming from the MIT Press.

Robert Kolker is the author of a number of books in cinema studies, including the third 

edition of A Cinema of Loneliness: Penn, Stone, Kubrick, Scorsese, and Altman (2000). 

His textbook, Film, Form, and Culture (2nd edn., 2002) is an introduction to film with an 

accompanying interactive CD-ROM, containing moving image clips. Kolker was one of 

the first film scholars to put moving images on the Web, in a Postmodern Culture essay, 

"The Moving Image Reclaimed." He will soon publish a Casebook of essays on 

Hitchcock's Psycho and a critical work, Dreadful Landscapes in the Spaces of Modernity; 

Welles, Kubrick and the Imagination of the Visible. He has been Professor of English at 

the University of Maryland, and Chair of the School of Literature, Communication, and 

Culture at Georgia Tech. 

Ian Lancashire, Professor of English at the University of Toronto, founded the Center 

for Computing in the Humanities there in 1986 and co-developed TACT (Text Analysis 

Computing Tools), with which he does research on Chaucer and Shakespeare. He now 

edits Representative Poetry Online, teaches a fully online course in reading poetry, 

develops the Early Modern English Dictionaries (LEME) database, and is a member of 

the Inter-PARES2 project. From 1992 to 2003 he presided over a Canadian learned 

society, the Consortium for Computers in the Humanities/Consortium pour ordinateurs en 

sciences humaines (COCH/COSH). 

Andrea Laue is a PhD candidate at the University of Virginia. She is a technical editor 

for the William Blake Archive, a researcher in the Speculative Computing Lab, and a 

graduate instructor in the media studies program. Her research interests include narratology, 

cognitive poetics, textual studies, digital media, and information visualization. In her 

dissertation, she investigates narrative structuring as manifested in the emergent text, the 

nexus produced by the interactions of an interpreter and a literary artifact. 

Andrew Mactavish is an Assistant Professor of Multimedia in the School of the Arts at 

McMaster University. He has published and presented papers in the areas of computer 

games, humanities computing, and multimedia. He currently holds a research grant from 

the Social Sciences and Humanities Research Council of Canada (SSHRC) to study the 

cultural politics of computer game play. He is also a member of collaborative research 

projects, including Globalization and Autonomy (SSHRC-MCRI) and two projects 

funded by the Canada Foundation for Innovation: TAPoR (Text Analysis Portal for 

Research) and IRIS (Infrastructure for Research on Internet Streaming). 

Willard McCarty is Senior Lecturer in Humanities Computing, King's College London, 

and editor of Humanist. Recently his research has centered on modeling and more 

broadly explored the intellectual integrity of humanities computing by probing and 

interrelating its disciplinary kinships. He is currently finishing a book on the field, for 

which the chapter included here is a preview. His primary research on modeling has 

drawn heavily on his Analytical Onomasticon to the Metamorphoses of Ovid, 

forthcoming when a sufficiently adventurous publisher can be found. Details at 

.

Jerome McGann is the John Stewart Bryan University Professor, University of Virginia 

and Adjunct Professor at Royal Holloway, University of London. His recent book 

Radiant Textuality: Literature after the World Wide Web, was awarded the Modern 

Language Association's James Russell Lowell Award (2002). He is currently developing 

digital resources for the interpretation of literary works. These include the collaborative 

environment IVANHOE and the 'Patacritical Demon. 

Bethany Nowviskie is a doctoral candidate at the University of Virginia. She serves as 

design editor for the Rossetti Archive, and is the lead designer and manager of the 

Temporal Modeling Project. Her other SpecLab projects include Biblioludica, the 

Ivanhoe Game, and the 'Patacritical Demon. Nowviskie's dissertation theorizes and 

describes the production of digital environments that both promote humanistic 

interpretation and emerge from the interpretative acts of their users. 

Carole L. Palmer is an Associate Professor at the Graduate School of Library and 

Information Science at the University of Illinois at Urbana-Champaign. Her research 

explores how information systems and services can best support the work of researchers. 

She is engaged in projects to develop information technologies that support 

interdisciplinary inquiry, discovery, and collaboration in the humanities and the sciences. 

Her recent publications include Scholarly Work in the Humanities and the Evolving 

Information Environment and Work at the Boundaries of Science: Information and the 

Interdisciplinary Research Process. 

Daniel V. Pitti is project director at the Institute for Advanced Technology in the 

Humanities (IATH) at the University of Virginia. As project director, he is responsible 

for project design in general, and Extensible Markup Language (XML) and objectrelational 

databases design and development in particular. Before coming to IATH in 

1997, he was Librarian for Advanced Technologies Projects at the University of 

California at Berkeley Library. 

Stephen Ramsay worked as a programmer and software engineer for the Institute for 

Advanced Technology in the Humanities at the University of Virginia before becoming 

an Assistant Professor of English at the University of Georgia. He edits the online version 

of the journal TEXT Technology, and has lectured widely on subjects related to 

humanities computing, public policy, and software design. 

Allen H. Renear is an Associate Professor in the Graduate School of Library and 

Information Science (GSLIS) at the University of Illinois, Urbana-Champaign, where he 

teaches courses in knowledge representation and document modeling, and, as head of the 

Electronic Publishing Research Group, leads research on XML semantics and document 

ontology. He has been involved in humanities-oriented electronic publishing, standards 

development, and research for over twenty years. Currently Chair of the Open eBook 

Publication Structure Working Group, he has served as President of the Association for 

Computers and the Humanities, on the Advisory Board of the Text Encoding Initiative, 

and prior to joining GSLIS, was Director of the Scholarly Technology Group at Brown 

University.

Geoffrey Rockwell is an Associate Professor of Humanities Computing and Multimedia 

in the School of the Arts at McMaster University. He received a BA in philosophy from 

Haverford College, an MA and PhD in philosophy from the University of Toronto and 

worked at the University of Toronto as a Senior Instructional Technology Specialist. He 

has published and presented papers in the area of textual visualization and analysis, 

humanities computing, instructional technology, computer games, and multimedia. With 

colleagues at McMaster University he set up an honors Multimedia program. He is 

currently the project leader for the CFI (Canada Foundation for Innovation) funded 

project TAPoR, a Text Analysis Portal for Research, which is developing a text tool 

portal for researchers who work with electronic texts. He recently published a book, 

Defining Dialogue: From Socrates to the Internet (2003). 

Thomas Rommel is Professor of English at International University Bremen (IUB). He 

is a member of the executive committee of the Association for Literary and Linguistic 

Computing (ALLC). His publications include a book on Byron's poetry (1995), Anglistik 

im Internet (1996), a study of Adam Smith (1999), and an edition of essays on literary 

hypertexts (forthcoming). He is co-editor of Prolepsis, The Heidelberg Review of English 

Studies. His research interests include theories of electronic text and the methodological 

implications of computer-assisted studies of literature. 

Marie-Laure Ryan is a native of Geneva, Switzerland, and is currently an independent 

scholar based in Colorado. She is the author of Possible Worlds, Artificial Intelligence 

and Narrative Theory (1991), and of Narrative as Virtual Reality: Immersion and 

Interactivity in Literature and Electronic Media (2001), which received the Jeanne and 

Aldo Scaglione Prize for Comparative Literature from the Modern Language 

Association. She is also the editor of two collections of essays, Cyberspace Textuality 

(1999) and Narrative Across Media (2004). 

David Z. Saltz is Associate Professor of Drama at the University of Georgia. He is 

Principal Investigator of Virtual Vaudeville: A Live Performance Simulation System, 

funded by the NSF, and has published essays about performance theory and interactive 

media in scholarly books and journals including Theatre Research International, 

Performance Research, and the Journal of Aesthetics and Art Criticism. He is also a 

practicing director and installation artist whose work focuses on the interaction between 

digital media and live performance. 

Susan Schreibman is General Editor and Project Manager of the MacGreevy Archive, 

and editor of Collected Poems of Thomas MacGreevy: An Annotated Edition (1991). She 

is the founder and editor of the web-based Irish Resources in the Humanities. She is 

currently serving a two-year term on the TEI Council. Dr. Schreibman is Assistant 

Director of the Maryland Institute for Technology in the Humanities (MITH), and an 

Affiliate Faculty member in the Department of English. Previously she was an Assistant 

Professor of Professional and Technical Communication at New Jersey Institute of 

Technology (2000–1), and the Semester in Irish Studies Newman Fellow at University 

College Dublin (1997–2000).

Ray Siemens is Canada Research Chair in Humanities Computing and Associate 

Professor of English at the University of Victoria; formerly, he was Professor of English 

at Malaspina University-College (1999–2004). He is President (English) of the 

Consortium for Computers in the Humanities/Consortium pour ordinateurs en sciences 

humaines and, in 2003, was Visiting Senior Research Fellow at the Centre for Computing 

in the Humanities at King's College London. Director of the Digital 

Humanities/Humanities Computing Summer Institute, founder of Malaspina University- 

College's Center for Digital Humanities Innovation, and founding editor of the electronic 

scholarly journal Early Modern Literary Studies, he is also author of works chiefly 

focusing on areas where literary studies and computational methods intersect, is editor of 

several Renaissance texts, and is co-editor of several book collections on humanities 

computing topics. 

Abby Smith is Director of Programs at the Council on Library and Information 

Resources, where her work focuses on the development and preservation of research 

collections in all formats and genres. She worked as program specialist at the Library of 

Congress, and taught intellectual and Russian history at Harvard and Johns Hopkins. Her 

recent publications include: New-Model Scholarship: How Will It Survive? (2003); The 

Evidence in Hand: Report of the Task Force on the Artifact in Library Collections 

(2001); Strategies for Building Digitized Collections (2001); Building and Sustaining 

Digital Collections: Models for Libraries and Museums (2001); and Collections, Content, 

and the Web (2000). 

Martha Nell Smith is Professor of English and Director of the Maryland Institute for 

Technology in the Humanities (MITH) at the University of Maryland. Her numerous 

publications include three award-winning books – Open Me Carefully: Emily Dickinson's 

Intimate Letters to Susan Dickinson, co-authored with Ellen Louise Hart (1998), Comic 

Power in Emily Dickinson, co-authored with Cristanne Miller and Suzanne Juhasz 

(1993), Rowing in Eden: Rereading Emily Dickinson (1992) – and more than thirty 

articles in such journals as American Literature, Studies in the Literary Imagination, 

South Atlantic Quarterly, Women's Studies Quarterly, Profils Americains, San Jose 

Studies, and The Emily Dickinson Journal. With Mary Loeffelholz, she is editing the 

Blackwell Companion to Emily Dickinson (forthcoming in 2005). The recipient of 

numerous awards from the National Endowment for the Humanities (NEH), the 

American Council of Learned Societies (ACLS), and the Fund for the Improvement of 

Postsecondary Education (FIPSE) for her work on Dickinson and in new media, Smith is 

also Coordinator and General Editor of the Dickinson Electronic Archives projects at the 

Institute for Advanced Technology in the Humanities (IATH) at the University of 

Virginia. With Lara Vetter, Smith is a general editor of Emily Dickinson's 

Correspondences, forthcoming from the Mellon-sponsored University of Virginia Press 

Electronic Imprint. 

C. M. Sperberg-McQueen is a member of the technical staff of the World Wide Web 

Consortium, an international membership organization responsible for developing Web 

standards. He co-edited the XML 1.0 specification and the Guidelines of the Text 

Encoding Initiative.

Simon Tanner is the Director of Digital Consultancy Services at King's College London 

(KDCS). He has an international reputation as a consultant and has consulted for 

prestigious digitization projects in Europe and America. Tanner has a library and 

information science background and prior to KDCS was Senior Consultant with HEDS 

Digitization Services and held librarian and systems posts at Loughborough University, 

Rolls Royce, and IBM. He recently authored a book, Digital Futures: Strategies for the 

Information Age, with Marilyn Deegan; he co-edits the Digital Futures series of books 

from Facet Publishing and is a guest editor for the Journal of Digital Information. 

William G. Thomas, III, is an Assistant Professor of History and Director of the 

Virginia Center for Digital History at the University of Virginia. He is the co-author with 

Edward L. Ayers of "The Difference Slavery Made: A Close Analysis of Two American 

Communities", a fully electronic journal article for the American Historical Review. He is 

the author of Lawyeringfor the Railroad: Business Law and Power in the New South 

(1999) and co-author of the Emmy nominee documentary film Massive Resistance. He is 

currently researching and producing a digital project on the environmental and social 

history of the Chesapeake Bay. 

John Unsworth served from 1993 to 2003 as the first Director of the Institute for 

Advanced Technology in the Humanities and as faculty in the Department of English at 

the University of Virginia. As the Institute's Director, he oversaw research projects across 

the disciplines in the humanities and published widely on electronic scholarship, 

humanities computing, and other topics. In 2003, he was appointed Dean of the Graduate 

School of Library and Information Science (GSLIS) at the University of Illinois, Urbana- 

Champaign, with appointments as Professor in GSLIS, in the Department of English, and 

on the Library faculty. 

Claire Warwick is a lecturer at the School of Library Archive and Information Studies, 

University College London, where she is programme director of the MA in Electronic 

Communication and Publishing. She is also a research supervisor for Cambridge 

University's MSt in International Relations. She has previously worked at Sheffield 

University, Department of Information Studies, and Oxford University English Faculty 

and Humanities Computing Unit, and at Chadwyck-Healey Ltd. Her research interests 

center on the study of the use and impact of computing on humanities scholarship, and on 

the societal effects of electronic publishing. She is a member of the advisory panel for the 

Portsmouth Historical Records Series, e-press and the Digital Egypt for Universities 

project and is a visiting lecturer at City College Thessaloniki, Greece. 

Susan Forscher Weiss holds a joint appointment in the Departments of Musicology and 

Romance Languages and Literature at the Johns Hopkins University. Her numerous 

publications include Bologna Q 18: l-Bologna, Civico Museo Bibliografico Musicale, 

Ms.BolC Q 18 (olim 143), Introduction and Facsimile Edition (1998), chapters, articles, 

reviews, and entries in numerous scholarly publications. She has collaborated in webbased 

programming, exhibits, CD-ROMs, and audio tours. She has also been the recipient 

of numerous awards and fellowships for teaching and research including grants from the

National Endowment for the Humanities, the American Council of Learned Societies, 

Harvard University, the Johns Hopkins University, and the Folger Shakespeare Library. 

Perry Willett was appointed the Head of the Digital Library Production Service at the 

University of Michigan in 2004. He served as the Assistant Director of the Digital 

Library Program at Indiana University from 2001, and was a bibliographer at the Main 

Library of Indiana University from 1992 to 2001. He is the general editor of the Wright 

American Fiction project and the Victorian Women Writers Project, and is a member of 

the Text Encoding Initiative Consortium Council. He has written on electronic text and 

digital libraries. 

William Winder is Assistant Professor of French at the University of British Columbia's 

French, Hispanic, and Italian Studies Department. He is on the board of directors of the 

Consortium for Computers in the Humanities and the editorial board of TEXT 

Technology, and he co-edits Computing in the Humanities Working Papers. His interests 

lie in computational and formalist approaches to the semantics of language and literature. 

See his website (http://www.fhis.ubc.ca/winder) for recent publications and research. 

Russon Wooldridge is Professor in the Department of French, University of Toronto. He 

is a teacher/researcher in French language, French lexicography, translation, and corpus 

linguistics of the Web. Research details and content at: 

http://www.chass.utoronto.ca/~wulfric. 

Foreword: Perspectives on the Digital Humanities 

Roberto A. Busa 

During World War II, between 1941 and 1946, I began to look for machines for the 

automation of the linguistic analysis of written texts. I found them, in 1949, at IBM in 

New York City. Today, as an aged patriarch (born in 1913) I am full of amazement at the 

developments since then; they are enormously greater and better than I could then 

imagine. Digitus Dei est hic! The finger of God is here! 

I consider it a great honor to be asked to write the Foreword to this fine book. It 

continues, underlines, completes, and recalls the previous Survey of Antonio Zampolli 

(produced in Pisa, 1997), who died before his time on August 22, 2003. In fact, the book 

gives a panoramic vision of the status artis. It is just like a satellite map of the points to 

which the wind of the ingenuity of the sons of God moves and develops the contents of 

computational linguistics, i.e., the computer in the humanities. 

Humanities computing is precisely the automation of every possible analysis of human 

expression (therefore, it is exquisitely a "humanistic" activity), in the widest sense of the 

word, from music to the theater, from design and painting to phonetics, but whose 

nucleus remains the discourse of written texts.

In the course of the past sixty years I have added to the teaching of scholastic philosophy, 

the processing of more than 22 million words in 23 languages and 9 alphabets, registering 

and classifying them with my teams of assistants. Half of those words, the main work, are 

in Latin. I will summarize the three different perspectives that I have seen and 

experienced in these sixty years. 

Technological "Miniaturization" 

According to the perspective of technological miniaturization, the first perspective I will 

treat, the Index Thomisticus went through three phases. The first one lasted less than 10 

years. I began, in 1949, with only electro-countable machines with punched cards. My 

goal was to have a file of 13 million of these cards, one for each word, with a context of 

12 lines stamped on the back. The file would have been 90 meters long, 1.20 m in height, 

1 m in depth, and would have weighed 500 tonnes. 

In His mercy, around 1955, God led men to invent magnetic tapes. The first were the 

steel ones by Remington, closely followed by the plastic ones of IBM. Until 1980, I was 

working on 1,800 tapes, each one 2,400 feet long, and their combined length was 1,500 

km, the distance from Paris to Lisbon, or from Milan to Palermo. I used all the 

generations of the dinosaur computers of IBM at that time. I finished in 1980 (before 

personal computers came in) with 20 final and conclusive tapes, and with these and the 

automatic photocompositor of IBM, I prepared for offset the 20 million lines which filled 

the 65,000 pages of the 56 volumes in encyclopedia format which make up the Index 

Thomisticus on paper. 

The third phase began in 1987 with the preparations to transfer the data onto CD-ROM. 

The first edition came out in 1992, and now we are on the threshold of the third. The 

work now consists of 1.36 GB of data, compressed with the Huffman method, on one 

single disk. 

Textual Informatics 

The second perspective is textual informatics, and it has branched into three different 

currents. Today the two greater and richer ones must be clearly distinguished from the 

third, the smallest and poorest. I must say that many people still do not realize this. 

I call the first current "documentaristic" or "documentary", in memory of the American 

Documentation Society, and of the Deutsche Gesellschaft für Dokumentation in the 

1950s. It includes databanks, the Internet, and the World Wide Web, which today are the 

infrastructures of telecommunications and are in continuous ferment. The second current 

I call "editorial." This is represented by CDs and their successors, including the 

multimedia ones, a new form of reproduction of a book, with audio-visual additions. Both 

these, albeit in different ways, provide for the multiplication, distribution, and swift 

traceability of both information and of a text. Both are recognizable by the fact that they 

substantially transfer and present, on an electronic support system, words and punctuation

plus some operative commands. Because they provide a service and so have a quick 

return on investment, both have grown abundantly – the first, despite significant 

obstacles, more solidly and with fewer disappointments than the second. 

I call the third current "hermeneutic" or interpretative, that informatics most associated 

with linguistic analysis and which I would describe as follows. In the electronic Index 

Thomisticus each of the 11 million words is encapsulated in a record of 152 bytes. Some 

22 are reserved for the word, and 130 contain 300 alternating "internal hypertexts", which 

specify the values within the levels of the morphology. 

At the moment, I am trying to get another project under way, which will obviously be 

posthumous, the first steps of which will consist in adding to the morphological encoding 

of each single separate word of the Thomistic lexicon (in all there are 150,000, including 

all the particles, such as et, non, etc.), the codes that express its syntax (i.e., its direct 

elementary syntactic correlations) within each single phrase in which it occurs. This 

project is called Lessico Tomistico Biculturale (LTB). Only a computer census of the 

syntactic correlations can document what concepts the author wanted to express with that 

word. Of a list of syntactic correlations, the "conceptual" translation can thus be given in 

modern languages. I have already published, mainly in the series of the Lessico 

Intellectuale Europeo (directed by T. Gregory of the University of Rome), the results of 

such syntactical analysis of a dozen words in their more than 500,000 context lines. To 

give one example, in the mind of St Thomas ratio seminalis meant then what today we 

call genetic programme. Obviously, St Thomas did not know of either DNA or genes, 

because at the time microscopes did not exist, but he had well understood that something 

had to perform their functions. 

Hermeneutic Informatics 

This third sort of informatics was the first to come into being, with the Index Thomisticus 

project, in 1949. It brought the following facts to my attention. First, everyone knows 

how to use his own mother tongue, but no one can know "how", i.e., no one can explain 

the rules and no one can list all the words of the lexicon that he uses (the active lexicon) 

nor of that which he understands but never uses (the passive lexicon). 

What scholar could answer the following questions? How many verbs does he know at 

least passively? Which and how many of them are always and only transitive? Which and 

how many of them are always and only intransitive? Which and how many of them are 

sometimes the one, and sometimes the other, and what is the percentage of each? Lastly, 

which contextual situations characteristically mark the transitive or intransitive use of the 

latter? 

Second, there is still no scientific grammar of any language that gives, in a systematized 

form, all the information necessary to program a computer for operations of artificial 

intelligence that may be currently used on vast quantities of natural texts, at least, e.g., for 

indexing the key words under which to archive or summarize these texts to achieve 

"automatic indexing – automatic abstracting."

Third, it is thus necessary for the use of informatics to reformulate the traditional 

morphology, syntax, and lexicon of every language. In fact all grammars have been 

formed over the centuries by nothing more than sampling. They are not to be 

revolutionized, abandoned, or destroyed, but subjected to a re-elaboration that is 

progressive in extent and depth. 

Schematically, this implies that, with integral censuses of a great mass of natural texts in 

every language, in synchrony with the discovered data, methods of observation used in 

the natural sciences should be applied together with the apparatus of the exact and 

statistical sciences, so as to extract categories and types and, thus, to organize texts in a 

general lexicological system, each and all with their probability index, whether great or 

small. 

Hermeneutic informatics hinges on the Alpac Report (Washington, DC, 1966) and, now, 

this perspective is perhaps awaiting its own globalization. I have already said that 

hermeneutic informatics was the first to come into existence. Shortly afterwards, in the 

early 1950s, if I am correct, the move toward automatic translation started. The magazine 

MT – Mechanical Translation was started at MIT, launched, I think, by Professor Billy 

Locke and others. The Pentagon financed various centers. I was involved in this. I 

connected the Anglo-Russian project of Leon Dostert and Peter Toma (of Georgetown 

University, Washington, DC) with the Computing Center of the Euratom of Ispra, which 

is on Lake Maggiore in Lombardy. My contributions were on an exchange basis. I 

supplied them, from my laboratory at Gallarate, with Russian abstracts of biochemistry 

and biophysics in Cyrillic script on punched cards, a million words. These were 

translated with the Georgetown programs. The translation was sufficient for an expert on 

the subject to be able to evaluate the merits of a more accurate translation done by hand, 

i.e., by a person's brain. 

Unfortunately, in 1966, as a result of the Alpac Report, the Pentagon cut off all funding. 

This was not because computers at that time did not have sufficient memory capability or 

speed of access, but precisely because the information on the categories and their 

linguistic correspondences furnished by the various branches of philology were not 

sufficient for the purpose. The "machine" required greater depth and more complex 

information about our ways of thinking and modes of expression! 

Future Perspectives 

In certain respects this was a boon. In fact, as this volume, too, documents, the number 

and devotion of those few volunteers, who in almost every part of the world have a 

passion for computational informatics, increased. They are not an organized army, but 

hunters who range freely, and this produces some obvious side effects. 

It also provokes a valuable consideration. Namely, it makes us realize that no single 

research center ever seems to have been able to answer, alone and completely, the 

linguistic challenge of globalized telematics. It seems that the answer to globalization, at 

least in principle, should be global as well, i.e., collective, or rather undertaken by one or

more supranational organizations, this for the obvious reason of the commitments 

required. Speaking is an interface between infinity and the cosmos, between time and 

eternity, and is evidence of the thirst for knowledge, understanding, possession, and 

manipulation of everything, according to one's own personal freedom, but on track with a 

common ballast of logic and beauty. Speaking must thus be taken seriously; it is sacred, 

as is every human person. We are far from having exhausted the precept inscribed on 

Apollo's temple at Delphi, "Know thyself." It seems, therefore, that the problem must be 

attacked: in its totality – with comprehensive, i.e., global, research; collectively – by 

exploiting informatics with its enormous intrinsic possibilities, and not by rushing, just to 

save a few hours, into doing the same things which had been done before, more or less in 

the same way as they were done before. 

It seems that the attack on the Twin Towers of New York City on September 11, 2001, 

has brought in an unforeseen season of lean kine. In Italy, as everywhere else, this has 

meant reductions in public funds for research. This period will pass, as surely as all the 

others we have experienced. Such reductions were also in evidence at the Exploratory 

Workshop on Computer Texts, which the European Science Foundation of the European 

Union held at Strasbourg on June 14 and 15, 2002. On the one hand, these cutbacks in 

finance are certainly worrying for the many operators of computational linguistics, which 

today is fragmented, but on the other hand, it could also lead to the according of priority 

to a definitive solution of the linguistic problem, like that which could facilitate the 

fulfillment of the globalization of economic exchange. 

A Proposal 

I should like to summarize the formula of a global solution to the linguistic challenge that 

I presented at the above-mentioned conference at Strasburg, much as if it were my 

spiritual testament, although I am uncertain whether to call it prophecy or Utopia. 

I suggest that – care of, for example, the European Union – for every principal language 

A, B, C, D, etc., from each of the principal university textbooks in present use in the 

various disciplines (for these represent the present state of what is knowable) there should 

be extracted its integral "lexicological system" (with the help of the instruments tested in 

the Index Thomisticus). In it, two "hemispheres" of each lexicon should be distinguished, 

the few words of very high frequency present in every argument which express the logic, 

and which are sometimes called "grammatical", and the very many words of relatively 

minor frequency that specify messages and arguments. The systems of each text of each 

discipline in each language should be integrally compared with each other by isolating 

and specifying both the coincidences and divergences with their quantities and 

percentages, including those of hapax, extracting from them, i.e., mixing them, in one 

single system of the same language, a system which totals statistically and with 

percentages both how much they have in common and their co-respective divergences. 

Then these statistical summaries of the various languages A, B, C, D, etc., should be 

compared with each other, with the same method of reporting their convergences and 

divergences, with quantity and percentage in a single system. (One has only to think of

how much data could be published a latere as valid and useful documents, although to be 

constantly updated, for example for contrastive grammars, etc.) 

Thus there would be on the computer a common interlingual system consisting solely of 

strings of bits and bytes with correspondence links both between convergences and 

divergences in themselves and between each other. It would be a sort of universal 

language, in binary alphabet, "antiBabel", still in virtual reality. From this, going in the 

reverse direction, there could be extracted in the respective alphabets of the individual 

languages A, B, C, D, etc., the number of words and expressions, situations of 

morphology and syntax, of each language which have found correspondence in other 

languages, and the number of those which have not. The number of such correspondences 

thus extracted (lexicon and grammar) would be a set of "disciplined" basic languages, to 

be adopted for the telematic use of the computer, to be also printed and then updated 

according to experience. 

In input, therefore, everybody could use their own native disciplined language and have 

the desired translations in output. The addressee could even receive the message both in 

their own language, in that of the sender, and in others. 

In addition, the problems of keys and privacy will have to be solved, as (one step at a 

time!) will those of the phonetic version of both input and output. 

These thoughts have formed gradually in my mind over the years, starting from the 

realization that my programs for Latin, which I always wanted broken up for 

monofunctional use, could be applied with the same operative philosophy to more than 

twenty other languages (all in a phonetic script), even those that do not descend from 

Latin, such as Arabic and Hebrew, which are written from right to left. I had only to 

transfer elements from one table to another, changing the length of fields, or adding a 

field. (However, I cannot say anything about languages written in ideograms or 

pictograms.) 

Conclusion 

In conclusion, I will therefore summarize the third perspective, that of textual 

hermeneutic informatics, as follows. The first period began with my Index Thomisticus 

and ended, though not for me, with the Alpac Report. The second, after the Alpac Report, 

is the parcelization in progress of free research. The third would begin if and when 

comparative global informatics begins in the principal languages, more or less in the 

sense I have tried to sketch here. Those who live long enough will see whether these 

roses (and thorns), which today are merely thoughts, will come to flower, and so will be 

able to tell whether they were prophecy or dream. 

The Digital Humanities and Humanities Computing: 

An Introduction

Susan Schreibman, Ray Siemens, and John Unsworth 

History 

This collection marks a turning point in the field of digital humanities: for the first time, a 

wide range of theorists and practitioners, those who have been active in the field for 

decades, and those recently involved, disciplinary experts, computer scientists, and 

library and information studies specialists, have been brought together to consider digital 

humanities as a discipline in its own right, as well as to reflect on how it relates to areas 

of traditional humanities scholarship. 

This collection has its origins in the research carried out over the past half a century in 

textually focused computing in the humanities, as Susan Hockey notes in this volume, 

found in Father Roberto Busa's adaptation of early computing to textual location, 

comparison, and counting as part of his work in creating the Index Thomisticus, a 

concordance to the works of St Thomas Aquinas. Yet even a cursory glance at this 

Companion's table of contents reveals how broadly the field now defines itself. It remains 

deeply interested in text, but as advances in technology have made it first possible, then 

trivial to capture, manipulate, and process other media, the field has redefined itself to 

embrace the full range of multimedia. Especially since the 1990s, with the advent of the 

World Wide Web, digital humanities has broadened its reach, yet it has remained in touch 

with the goals that have animated it from the outset: using information technology to 

illuminate the human record, and bringing an understanding of the human record to bear 

on the development and use of information technology. 

The first section of the Companion addresses the field of digital humanities from 

disciplinary perspectives. Although the breadth of fields covered is wide, what is revealed 

is how computing has cut across disciplines to provide not only tools, but methodological 

focal points. There is, for example, a shared focus on preserving physical artifacts 

(written, painted, carved, or otherwise created), that which is left to us by chance (ruin, 

and other debris of human activity), or that which has been near-impossible to capture in 

its intended form (music, performance, and event). Yet many disciplines have gone 

beyond simply wishing to preserve these artifacts, what we might now call early forms of 

data management, to re-represent and manipulate them to reveal properties and traits not 

evident when the artifact was in its native form. Moreover, digital humanities now also 

concerns itself with the creation of new artifacts which are born digital and require 

rigorous study and understanding in their own right. 

Eiteljorg notes that archaeologists, like most other early adopters in the arts and 

humanities, first used the computer for record making and record keeping, in the 

knowledge that data in this form would see more flexible utilization, particularly in 

computer-assisted statistical analysis. More recent applications derive from the 

introduction of global data-recording standards, allowing large corpora of archaeological 

data to be navigated, as well as the integration of global information systems (GlS) – 

derived data to represent standard locational information across these corpora. Art 

historians, as described by Greenhalgh, use computers to image, order, sort, interrogate,

and analyze data about artworks, and increasingly use the Internet as the carrier for 

multimedia research or teaching/learning projects. Classical studies has always been a 

data-intensive enterprise, Crane demonstrates, and has seen the development of lexica, 

encyclopedias, commentaries, critical editions, and other elements of scholarly 

infrastructure that are well suited to an electronic environment which, ultimately, reflects 

a natural impulse toward systematic knowledge management and engineering within the 

field. So, too, is this impulse essential to the understanding of computing's role in literary 

studies (as noted by Rommel), linguistics (as discussed by Hajic), and lexicography (as 

charted by Wooldridge). In discussing the discipline of musicology, Fujinaga and Weiss 

note that the Internet has revolutionized not only the distribution potential for the artifacts 

that lie at the heart of their consideration but, also, other more analytical applications 

pertinent to the future of the field. Thomas documents the intense methodological debates 

sparked by the introduction of computing in history, debates which computing ultimately 

lost (in the United States, at least), after which it took a generation for historians to 

reconsider the usefulness of the computer to their discipline. The rhetoric of revolution 

proved more predictive in other disciplines, though – for example, in philosophy and 

religion. Today, one hears less and less of it, perhaps because (as Ess notes) the 

revolution has succeeded: in almost all disciplines, the power of computers, and even 

their potential, no longer seem revolutionary at all. While this may be true of a number of 

disciplines, in fields such as the performing arts, as discussed by Saltz, and new media 

studies, by Rockwell and Mactavish, there is an inherent kinship between the everevolving 

developments in computing and their performative and analytical potentials. 

Principles, Applications, and Dissemination 

The digital humanities, then, and their interdisciplinary core found in the field of 

humanities computing, have a long and dynamic history best illustrated by examination 

of the locations at which specific disciplinary practices intersect with computation. Even 

so, just as the various fields that make up the humanities share a focus on the examination 

of artifactual evidence of that which makes us human, so, too, do these fields share a 

number of commonly held assumptions about the way in which such examination is 

carried out, both with and without the assistance of the computer. Widely spread through 

the digital humanities community is the notion that there is a clear and direct relationship 

between the interpretative strategies that humanists employ and the tools that facilitate 

exploration of original artifacts based on those interpretative strategies; or, more simply 

put, those working in the digital humanities have long held the view that application is as 

important as theory. Thus, exemplary tasks traditionally associated with humanities 

computing hold the digital representation of archival materials on a par with analysis or 

critical inquiry, as well as theories of analysis or critical inquiry originating in the study 

of those materials. The field also places great importance on the means of disseminating 

the results of these activities and, as Pitti discusses, recognizes that project conception 

and management can be as important pragmatic concerns as others which are more 

traditionally associated with disciplinary pursuits. 

The representation of archival material involves the use of computer-assisted means to 

describe and express print-, visual-, and audio-based material in tagged and searchable

electronic form, as discussed by Deegan and Tanner. This representation is a critical and 

self-conscious activity, from the choice of what to represent to the reproduction of 

primary materials, for example, in the preparation of an electronic edition or digital 

facsimile (as discussed by Smith) and the exploration of the relationship between digital 

surrogates to legacy data (Warwick). Related to the process of representation is the 

necessity of understanding the tools being used, as Laue outlines, and the implications of 

decisions that we make in the use of those tools and the impact they have on analytical 

processes (McGann). The growing field of knowledge representation, which draws on the 

field of artificial intelligence and seeks to "produce models of human understanding that 

are tractable to computation" (Unsworth 2001), provides a lens through which we might 

understand such implications. This is especially true in issues related to archival 

representation and textual editing, high-level interpretative theory and criticism, and 

protocols of knowledge transfer – as modeled with computational techniques (discussed 

by McCarty), and captured via encoding and classification systems (Renear; Sperberg- 

McQueen) and represented in data structures (Ramsay), some of which have great impact 

on the ways in which we associate human information (Ryan) and interpret the ways in 

which it has influence upon us (Drucker). 

In the digital humanities, critical inquiry involves the application of algorithmically 

facilitated search, retrieval, and critical processes that, originating in humanities-based 

work, have been demonstrated to have application far beyond. Associated with critical 

theory, this area is typified by interpretative studies that assist in our intellectual and 

aesthetic understanding of humanistic works. It also involves the application (and 

applicability) of critical and interpretative tools and analytic algorithms, as discussed by 

Bradley, on those artifacts produced through processes associated with archival 

representation made available via resources associated with processes of publishing and 

the communication of results. Manifested in the analysis techniques that Burrows and Ide 

each discuss – and seeing utility in a wide-ranging array of applications, from authorship 

attribution (Craig) to cognitive stylistics (Lancashire) – the basis of such analysis is the 

encoded and digitally stored corpora governed by strategies of knowledge representation 

that, themselves, are capable of possessing what we might term a "poetics" (Winder). So, 

too, with digital media such as film (Kolker), and with issues of interface and usability 

that are, as Kirschenbaum discusses, integral to all materials in electronic form and our 

interaction with them. Further, efforts toward dissemination have their roots, ultimately, 

in issues related to re-presentation but are themselves manifested in concerns pertinent to 

the nature of computer-facilitated communities (Willett): preservation in the electronic 

medium (discussed by Abby Smith), professional electronic publication (treated by 

Jensen's chapter, and addressed further by Palmer), and the unique array of challenges 

and opportunities that arise with the emergence of digital libraries, as outlined by Besser. 

Conclusion 

The editors intended this collection to serve as a historical record of the field, capturing a 

sense of the digital humanities as they have evolved over the past half century, and as 

they exist at the moment. Yet, if one looks at the issues that lie at the heart of nearly all 

contributions to this volume, one will see that these contributions reflect a relatively clear

view of the future of the digital humanities. In addition to charting areas in which past 

advances have been made, and in which innovation is currently taking place, this volume 

reveals that digital humanities is addressing many of the most basic research paradigms 

and methods in the disciplines, to focus our attention on important questions to be asked 

and answered, in addition to important new ways of asking and answering that are 

enabled by our interaction with the computer. 

What this collection also reveals is that there are central concerns among digital 

humanists which cross disciplinary boundaries. This is nowhere more evident than in the 

representation of knowledge-bearing artifacts. The process of such representation – 

especially so when done with the attention to detail and the consistency demanded by the 

computing environment – requires humanists to make explicit what they know about their 

material and to understand the ways in which that material exceeds or escapes 

representation. Ultimately, in computer-assisted analysis of large amounts of material that 

has been encoded and processed according to a rigorous, well thought-out system of 

knowledge representation, one is afforded opportunities for perceiving and analyzing 

patterns, conjunctions, connections, and absences that a human being, unaided by the 

computer, would not be likely to find. 

The process that one goes through in order to develop, apply, and compute these 

knowledge representations is unlike anything that humanities scholars, outside of 

philosophy, have ever been required to do. This method, or perhaps we should call it a 

heuristic, discovers a new horizon for humanities scholarship, a paradigm as powerful as 

any that has arisen in any humanities discipline in the past – and, indeed, maybe more 

powerful, because the rigor it requires will bring to our attention undocumented features 

of our own ideation. Coupled with enormous storage capacity and computational power, 

this heuristic presents us with patterns and connections in the human record that we 

would never otherwise have found or examined. 

Acknowledgments 

The editors would like to thank Emma Bennett, Andrew McNeillie, and Karen Wilson at 

Blackwell for their assistance, encouragement, and support. Ray Siemens would also like 

to acknowledge the assistance of Karin Armstrong and Barbara Bond with some 

materials present in this volume, and the Malaspina Research and Scholarly Activity 

Committee, for their support. 

McCarty, Willard. What is Humanities Computing? Toward a Definition of the Field. 

URL: http://www.cch.kcl.ac.uk/legacy/staff/wlm/essays/what/ 

Schreibman, Susan (2002). Computer-mediated Discourse: Reception Theory and 

Versioning. Computers and the Humanities 36, 3: 283–93. 

Siemens, R. G. (2002). A New Computer-assisted Literary Criticism? Introduction to A 

New Computer-assisted Literary Criticism?, ed. R. G. Siemens. [A special issue of] 

Computers and the Humanities 363: 259–67.

Unsworth, John (2001). Knowledge Representation in Humanities Computing. Inaugural 

E-humanities Lecture at the National Endowment for the Humanities (April 3). URL: 

http://www.iath.virginia.edu/~jmu2m/KR/. 

1. 

The History of Humanities Computing 

Susan Hockey 

Introduction 

Tracing the history of any interdisciplinary academic area of activity raises a number of 

basic questions. What should be the scope of the area? Is there overlap with related areas, 

which has impacted on the development of the activity? What has been the impact on 

other, perhaps more traditional, disciplines? Does a straightforward chronological 

account do justice to the development of the activity? Might there be digressions from 

this, which could lead us into hitherto unexplored avenues? Each of these questions could 

form the basis of an essay in itself but within the space and context available here, the 

approach taken is to present a chronological account which traces the development of 

humanities computing. Within this, the emphasis is on highlighting landmarks where 

significant intellectual progress has been made or where work done within humanities 

computing has been adopted, developed or drawn on substantially within other 

disciplines. 

It is not the place of this essay to define what is meant by humanities computing. The 

range of topics within this Companion indeed sends plenty of signals about this. Suffice it 

to say that we are concerned with the applications of computing to research and teaching 

within subjects that are loosely defined as "the humanities", or in British English "the 

arts." Applications involving textual sources have taken center stage within the 

development of humanities computing as defined by its major publications and thus it is 

inevitable that this essay concentrates on this area. Nor is it the place here to attempt to 

define "interdisciplinarity", but by its very nature, humanities computing has had to 

embrace "the two cultures", to bring the rigor and systematic unambiguous procedural 

methodologies characteristic of the sciences to address problems within the humanities 

that had hitherto been most often treated in a serendipitous fashion. 

Beginnings: 1949 to early 1970s 

Unlike many other interdisciplinary experiments, humanities computing has a very wellknown 

beginning. In 1949, an Italian Jesuit priest, Father Roberto Busa, began what even 

to this day is a monumental task: to make an index verborum of all the words in the 

works of St Thomas Aquinas and related authors, totaling some 11 million words of 

medieval Latin. Father Busa imagined that a machine might be able to help him, and,

having heard of computers, went to visit Thomas J. Watson at IBM in the United States 

in search of support (Busa 1980). Some assistance was forthcoming and Busa began his 

work. The entire texts were gradually transferred to punched cards and a concordance 

program written for the project. The intention was to produce printed volumes, of which 

the first was published in 1974 (Busa 1974). 

A purely mechanical concordance program, where words are alphabetized according to 

their graphic forms (sequences of letters), could have produced a result in much less time, 

but Busa would not be satisfied with this. He wanted to produce a "lemmatized" 

concordance where words are listed under their dictionary headings, not under their 

simple forms. His team attempted to write some computer software to deal with this and, 

eventually, the lemmatization of all 11 million words was completed in a semiautomatic 

way with human beings dealing with word forms that the program could not handle. Busa 

set very high standards for his work. His volumes are elegantly typeset and he would not 

compromise on any levels of scholarship in order to get the work done faster. He has 

continued to have a profound influence on humanities computing, with a vision and 

imagination that reach beyond the horizons of many of the current generation of 

practitioners who have been brought up with the Internet. A CD-ROM of the Aquinas 

material appeared in 1992 that incorporated some hypertextual features ("cum 

hypertextibus") (Busa 1992) and was accompanied by a user guide in Latin, English, and 

Italian. Father Busa himself was the first recipient of the Busa award in recognition of 

outstanding achievements in the application of information technology to humanistic 

research, and in his award lecture in Debrecen, Hungary, in 1998 he reflected on the 

potential of the World Wide Web to deliver multimedia scholarly material accompanied 

by sophisticated analysis tools (Busa 1999). 

By the 1960s, other researchers had begun to see the benefits of working with 

concordances. A series of four articles by Dolores Burton in the journal Computers and 

the Humanities in 1981–2 attempted to bring these together, beginning with a discussion 

of the 1950s (Burton 1981a, 1981b, 1981c, 1982). Some of these researchers were 

individual scholars whose interests concentrated on one set of texts or authors. In the UK, 

Roy Wisbey produced a series of indexes to Early Middle High German texts (Wisbey 

1963). In the USA Stephen Parrish's concordances to the poems of Matthew Arnold and 

W B. Yeats introduced the series of concordances published by Cornell University Press 

(Parrish 1962). This period also saw the establishment of computing facilities in some 

major language academies in Europe, principally to assist with the compilation of 

dictionaries. Examples include the Trésor de la Langue Française (Gorcy 1983), which 

was established in Nancy to build up an archive of French literary material, and the 

Institute of Dutch Lexicology in Leiden (De Tollenaere 1973). 

Although much activity at this time was concentrated on the production of concordances 

as ends in themselves, one application of these tools began to take on a life of its own. 

The use of quantitative approaches to style and authorship studies predates computing. 

For example, Augustus de Morgan in a letter written in 1851 proposed a quantitative 

study of vocabulary as a means of investigating the authorship of the Pauline Epistles 

(Lord 1958) and T. C. Mendenhall, writing at the end of the nineteenth century, described

his counting machine, whereby two ladies computed the number of words of two letters, 

three, and so on in Shakespeare, Marlowe, Bacon, and many other authors in an attempt 

to determine who wrote Shakespeare (Mendenhall 1901). But the advent of computers 

made it possible to record word frequencies in much greater numbers and much more 

accurately than any human being can. In 1963, a Scottish clergyman, Andrew Morton, 

published an article in a British newspaper claiming that, according to the computer, St 

Paul only wrote four of his epistles. Morton based his claim on word counts of common 

words in the Greek text, plus some elementary statistics. He continued to examine a 

variety of Greek texts producing more papers and books concentrating on an examination 

of the frequencies of common words (usually particles) and also on sentence lengths, 

although it can be argued that the punctuation which identifies sentences was added to the 

Greek texts by modern editors (Morton 1965; Morton and Winspear 1971). 

It is believed that the first use of computers in a disputed authorship study was carried out 

on the Junius Letters by Alvar Ellegard. Published in 1962, this study did not use a 

computer to make the word counts, but did use machine calculations which helped 

Ellegard get an overall picture of the vocabulary from hand counts (Ellegard 1962). What 

is probably the most influential computer-based authorship investigation was also carried 

out in the early 1960s. This was the study by Mosteller and Wallace of the Federalist 

Papers in an attempt to identify the authorship of the twelve disputed papers (Mosteller 

and Wallace 1964). With so much material by both authorship candidates on the same 

subject matter as the disputed papers, this study presented an ideal situation for 

comparative work. Mosteller and Wallace were primarily interested in the statistical 

methods they employed, but they were able to show that Madison was very likely to have 

been the author of the disputed papers. Their conclusions generally have been accepted, 

to the extent that the Federalist Papers have been used as a test for new methods of 

authorship discrimination (Holmes and Forsyth 1995; Tweedie et al. 1996). 

At this time much attention was paid to the limitations of the technology. Data to be 

analyzed were either texts or numbers. They were input laboriously by hand either on 

punched cards, with each card holding up to eighty characters or one line of text 

(uppercase letters only), or on paper tape, where lower-case letters were perhaps possible 

but which could not be read in any way at all by a human being. Father Busa has stories 

of truckloads of punched cards being transported from one center to another in Italy. All 

computing was carried out as batch processing, where the user could not see the results at 

all until printout appeared when the job had run. Character-set representation was soon 

recognized as a substantial problem and one that has only just begun to be solved now 

with the advent of Unicode, although not for every kind of humanities material. Various 

methods were devised to represent upper- and lower-case letters on punched cards, most 

often by inserting an asterisk or similar character before a true upper-case letter. Accents 

and other non-standard characters had to be treated in a similar way and non-Roman 

alphabets were represented entirely in transliteration. 

Most large-scale datasets were stored on magnetic tape, which can only be processed 

serially. It took about four minutes for a full-size tape to wind from one end to the other 

and so software was designed to minimize the amount of tape movement. Random access

to data such as happens on a disk was not possible. Data had therefore to be stored in a 

serial fashion. This was not so problematic for textual data, but for historical material it 

could mean the simplification of data, which represented several aspects of one object 

(forming several tables in relational database technology), into a single linear stream. 

This in itself was enough to deter historians from embarking on computer-based projects. 

Representation problems extended far beyond specific characters. For concordance and 

retrieval programs there was a need to identify citations by their location within the text. 

The methods used by conventional document retrieval systems were inadequate because 

they tended to assume document structures similar to those of journal articles and were 

unable to cope with the structures found in poetry or drama, or in manuscript sources 

where the lineation is important. Various methods of defining document structures were 

proposed, but the most sophisticated one developed at this time was that used by the 

COCOA concordance program (Russell 1967). Modeled on a format developed by Paul 

Bratley for an Archive of Older Scottish texts (Hamilton-Smith 1971), COCOA enables 

the user to define a specification for the document structure which matches the particular 

set of documents. It also enables the markup of overlapping structures, making it 

possible, for example, to encode a citation system for a printed version in parallel with 

that for the manuscript source of the material. COCOA is also economical of file space, 

but is perhaps less readable for the human. 

The other widely used citation scheme was more dependent on punched card format. In 

this scheme, often called "fixed format", every line began with a coded sequence of 

characters giving citation information. Each unit within the citation was positioned in 

specific columns across the line, for example the title in columns 1–3, verse number in 

columns 5–6, and line number in columns 7–9. The entry of this information was speeded 

up by functions on the punched card machine, but the information also occupied more 

space within the computer file. 

The legacy of these citation schemes can still be found in electronic texts created some 

time ago. COCOA, particularly, was very influential and other schemes were derived 

from it. COCOA cannot easily handle the markup of small features within the content 

such as names, dates, and abbreviations, but its ability to deal with overlapping structures 

outstrips that of almost all modern markup schemes. 

This period also saw the first opportunities for those interested in humanities computing 

to get together to share ideas and problems. In 1964, IBM organized a conference at 

Yorktown Heights. The subsequent publication, Literary Data Processing Conference 

Proceedings, edited by Jess Bessinger and Stephen Parrish (1965), almost reads like 

something from twenty or so years later, except for the reliance on punched cards for 

input. Papers discuss complex questions in encoding manuscript material and also in 

automated sorting for concordances where both variant spellings and the lack of 

lemmatization are noted as serious impediments. 

As far as can be ascertained, the Yorktown Heights conference was a one-off event. The 

first of a regular series of conferences on literary and linguistic computing and the

precursor of what became the Association for Literary and Linguistic 

Computing/Association for Computers and the Humanities (ALLC/ACH) conferences 

was organized by Roy Wisbey and Michael Farringdon at the University of Cambridge in 

March, 1970. This was a truly international event with good representation from both 

sides of the Atlantic as well as from Australia. The proceedings, meticulously edited by 

Wisbey (1971), set the standard for subsequent publications. A glance through them 

indicates the emphasis of interest on input, output, and programming as well as 

lexicography, textual editing, language teaching, and stylistics. Even at this time the need 

for a methodology for archiving and maintaining electronic texts was fully recognized. 

Another indication of an embryonic subject area is the founding of a new journal. 

Computers and the Humanities began publication in 1966 under the editorship of Joseph 

Raben. With characteristic energy, Raben nurtured the new journal and during its first 

years, at least until the regular series of conferences and associations that developed from 

them got going, it became the main vehicle for dissemination of information about 

humanities computing. Raben recognized the need just to know what is going on and the 

journal's Directory of Scholars Active was the first point of call for people who were 

thinking about starting a project. Other informal newsletters also served specific 

communities, notably Calculi for computers and classics, edited by Stephen Waite. 

The 1960s also saw the establishment of some centers dedicated to the use of computers 

in the humanities. Wisbey founded the Centre for Literary and Linguistic Computing in 

Cambridge in 1963 as support for his work with Early Middle High German Texts. In 

Tübingen, Wilhelm Ott established a group which began to develop the suite of programs 

for text analysis, particularly for the production of critical editions. The TuStep software 

modules are in use to this day and set very high standards of scholarship in dealing with 

all phases from data entry and collation to the production of complex print volumes. 

Work in this early period is often characterized as being hampered by technology, where 

technology is taken to mean character sets, input/output devices and the slow turnaround 

of batch processing systems. However, researchers did find ways of dealing with some of 

these problems, albeit in a cumbersome way. What is more characteristic is that key 

problems which they identified are still with us, notably the need to look at "words" 

beyond the level of the graphic string, and to deal effectively with variant spellings, 

multiple manuscripts, and lemmatization. 

Consolidation: 1970s to mid-1980s 

If any single-word term can be used to describe this period, it would almost certainly be 

"consolidation." More people were using methodologies developed during the early 

period. More electronic texts were being created and more projects using the same 

applications were started. Knowledge of what is possible had gradually spread through 

normal scholarly channels of communication, and more and more people had come 

across computers in their everyday life and had begun to think about what computers 

might do for their research and teaching.

The diffusion of knowledge was helped not only by Computers and the Humanities but 

also by a regular series of conferences. The 1970 symposium in Cambridge was the start 

of a biennial series of conferences in the UK, which became a major focal point for 

computing in the humanities. Meetings in Edinburgh (1972), Cardiff (1974), Oxford 

(1976), Birmingham (1978), and Cambridge (1980) all produced high-quality papers. The 

Association for Literary and Linguistic Computing was founded at a meeting in King's 

College London in 1973. Initially it produced its own Bulletin three times per year. It also 

began to organize an annual meeting with some invited presentations and by 1986 had a 

journal, Literary and Linguistic Computing. By the mid-1970s, another series of 

conferences began in North America, called the International Conference on Computing 

in the Humanities (ICCH), and were held in odd-numbered years to alternate with the 

British meetings. The British conference and the ALLC annual meetings gradually began 

to coalesce. They continued to concentrate on literary and linguistic computing with 

some emphasis on "linguistic", where they offered a forum for the growing number of 

European researchers in what became known as corpus linguistics. ICCH attracted a 

broader range of papers, for example on the use of computers in teaching writing, and on 

music, art, and archaeology. The Association for Computers and the Humanities (ACH) 

grew out of this conference and was founded in 1978. 

The requirements of humanities computing also began to be recognized within academic 

computing centers. Still in the days of mainframe computing, it was necessary to register 

to use any computing facilities and that registration provided an opportunity for academic 

computing staff to find out what users wanted and to consider providing some standard 

software that could be used by many different people. The second version of the COCOA 

concordance program in Britain was designed to be run on different mainframe 

computers for exactly this purpose (Berry-Rogghe and Crawford 1973). It was distributed 

to different computing centers in the mid-1970s and many of these centers designated one 

person to act as support. Dissatisfaction with its user interface coupled with the 

termination of support by the Atlas Laboratory, where it was written, led the British 

funding bodies to sponsor the development of a new program at Oxford University. 

Called the Oxford Concordance Program (OCP), this software was ready for distribution 

in 1982 and attracted interest around the world with users in many different countries 

(Hockey and Marriott 1979a, 1979b, 1979c, 1980). Other packaged or generic software 

also appeared at this time and significantly reduced the cost of a project in terms of 

programming support. 

The need to avoid duplication of effort also led to consolidation in the area of text 

archiving and maintenance. With the advent of packaged software and the removal of the 

need for much programming, preparing the electronic text began to take up a large 

proportion of time in any project. The key driver behind the establishment of the Oxford 

Text Archive (OTA) in 1976 was the need simply to ensure that a text that a researcher 

had finished with was not lost. The OTA undertook to maintain electronic texts and, 

subject to the permission of the depositor and with appropriate copyright permissions, to 

make these texts available to anyone else who wanted to use them for academic purposes. 

It was the beginnings of a digital library, although nobody called it this initially, and its 

staff had to devise their own method of describing and documenting the material (Proud

1989). The amount of undocumented material highlighted the need for recognized 

procedures for describing electronic texts. 

The OTA's approach was to offer a service for maintenance of anything that was 

deposited. It managed to do this for some considerable time on very little budget, but was 

not able to promote the creation of specific texts. Groups of scholars in some discipline 

areas made more concerted attempts to create an archive of texts to be used as a source 

for research. Notable among these was the Thesaurus Linguae Graecae (TLG) begun at 

the University of California Irvine and directed for many years by Theodore Brunner. 

Brunner raised millions of dollars to support the creation of a "databank" of Ancient 

Greek texts, covering all authors from Homer to about ad 600, some 70 million words 

(Brunner 1993). A complementary collection of Classical Latin was later produced by the 

Packard Humanities Institute, and together with the TLG gave scholars in classical 

studies a research resource that was unrivaled in other disciplines for many years. Only 

Old English scholars had access to a similar comprehensive, but smaller corpus with the 

completion of the Old English Corpus for the Dictionary of Old English (Healey 1989). 

More centers for humanities computing were also established during this period. Some, 

for example the Norwegian Computing Center for the Humanities (now HIT) at Bergen, 

with substantial government support, incorporated a wide range of applications and 

projects. Others such as the Center for Computer Analysis of Texts (CCAT) at the 

University of Pennsylvania were more narrowly focused on the interests of the academics 

who had initially promoted them. Pockets of interest had become established around the 

world and scholars in those institutions on the whole enjoyed a good deal of support. 

This period also saw the introduction of courses on various aspects of humanities 

computing. Some courses were given by staff within academic computing centers and 

concentrated mostly on the mechanics of using specific software programs. Others looked 

more broadly at application areas. Those given by academics tended to concentrate on 

their own interests giving rise to student projects in the same application areas. A debate 

about whether or not students should learn computer programming was ongoing. Some 

felt that it replaced Latin as a "mental discipline" (Hockey 1986). Others thought that it 

was too difficult and took too much time away from the core work in the humanities. The 

string handling language SNOBOL was in vogue for some time as it was easier for 

humanities students than other computer languages, of which the major one was still 

Fortran. 

There were some developments in processing tools, mostly through the shift from tape to 

disk storage. Files no longer had to be searched sequentially. For a time there were 

various technologies for organizing material in databases, some of which were very 

effective for humanities material (Burnard 1987b), but gradually the relational model 

prevailed. In mainframe implementations this presented a better structure within which 

historians and others working with material drawn from sources (rather than the sources 

themselves) could work. However, relational technologies still presented some problems 

for the representation of information that needed to be fitted into tables. At least two 

hardware devices were invented in the 1970s for assisting searching. One was

implemented in David Packard's Ibycus computer, which was built to work with the TLG 

and some other classics material (Lancashire 1991: 204–5). The other was the Content 

Addressing File Store (CAFS), which worked on the British ICL computers (Burnard 

1987a). The idea of transferring processing into the hardware was very attractive to 

humanities researchers who had to deal with large amounts of material, but it did not 

catch on in a big way, possibly because it was overtaken by advances in the speed of 

conventional hardware. 

A glance through the various publications of this period shows a preponderance of papers 

based on vocabulary studies generated initially by concordance programs. The results 

were of interest either for some kinds of stylistic analyses or for linguistic applications. 

Increasingly complex mathematics were brought to bear on vocabulary counts, leaving 

some more humanities-oriented conference participants out in the cold. Apart from these, 

there was little really new or exciting in terms of methodology and there was perhaps less 

critical appraisal of methodologies than might be desirable. The important developments 

during this period lay more in support systems generated by the presence of more outlets 

for dissemination (conferences and journals) and the recognition of the need for standard 

software and for archiving and maintaining texts. Dissemination was concentrated in 

outlets for humanities computing and much less in mainstream humanities publications. It 

seems that we were still at a stage where academic respectability for computer-based 

work in the humanities was questionable and scholars preferred to publish in outlets 

where they were more likely to be accepted. 

New Developments: Mid-1980s to Early 1990s 

This period saw some significant developments in humanities computing. Some of these 

can be attributed to two new technologies, the personal computer and electronic mail. 

Others happened simply because of the increase of usage and the need to reduce 

duplication of effort. 

At first there were several different and competing brands of personal computers. Some 

were developed for games, some were standalone word processors and could not be used 

for anything else, and others were specifically aimed at the educational market rather than 

for general use. Gradually IBM PCs and models based on the IBM architecture began to 

dominate, with Apple Macintoshes also attracting plenty of use, especially for graphics. 

The personal computer is now a necessity of scholarly life, but in its early days it was 

considerably more expensive in relation to now and early purchasers were enthusiasts and 

those in the know about computing. The initial impact in humanities computing was that 

it was no longer necessary to register at the computer center in order to use a computer. 

Users of personal computers could do whatever they wanted and did not necessarily 

benefit from expertise that already existed. This encouraged duplication of effort, but it 

also fostered innovation where users were not conditioned by what was already available. 

By the end of the 1980s, there were three DOS-based text analysis programs: Word- 

Cruncher, TACT, and MicroOCP, all of which had very good functionality. Owners of

personal computers would work with these at home and, in the case of WordCruncher 

and TACT, obtain instantaneous results from searches. MicroOCP was developed from 

the mainframe program using a batch concordance technique rather than interactive 

searching. However, the main application of personal computers was that shared with all 

other disciplines, namely word processing. This attracted many more users who knew 

very little about other applications and tended to assume that the functions within word 

processing programs might be all that computers could do for them. 

The Apple Macintosh was attractive for humanities users for two reasons. Firstly, it had a 

graphical user interface long before Windows on PCs. This meant that it was much better 

at displaying non-standard characters. At last it was possible to see Old English 

characters, Greek, Cyrillic, and almost any other alphabet, on the screen and to 

manipulate text containing these characters easily. Secondly, the Macintosh also came 

with a program that made it possible to build some primitive hypertexts easily. 

HyperCard provided a model of file cards with ways of linking between them. It also 

incorporated a simple programming tool making it possible for the first time for 

humanities scholars to write computer programs easily. The benefits of hypertext for 

teaching were soon recognized and various examples soon appeared. A good example of 

these was the Beowulf Workstation created by Patrick Conner (Conner 1991). This 

presents a text to the user with links to a modern English version and linguistic and 

contextual annotations of various kinds. The first version of the Perseus Project was also 

delivered to the end user in HyperCard. 

Networking, at least for electronic mail, was previously confined to groups of computer 

scientists and research institutes. By the mid-1980s, facilities for sending and receiving 

electronic mail across international boundaries were provided by most academic 

computing services. At the 1985 ALLC conference in Nice, electronic mail addresses 

were exchanged avidly and a new era of immediate communication began. Soon e-mail 

was being sent to groups of users and the ListServ software for electronic discussion lists 

was established. Ansaxnet, the oldest electronic discussion list for the humanities, was 

founded by Patrick Conner in 1986 (Conner 1992). 

At the ICCH conference in Columbia, South Carolina, in spring 1987 a group of people 

mostly working in support roles in humanities computing got together and agreed that 

they needed to find a way of keeping in touch on a regular basis. Willard McCarty, who 

was then at the University of Toronto, agreed to look into how they might do this. On his 

return from the conference he discovered the existence of ListServ, and Humanist was 

born (McCarty 1992). The first message was sent out on May 7, 1987. McCarty launched 

himself into the role of editing what he prefers to call an "electronic seminar" and, except 

for a hiatus in the early 1990s when Humanist was edited from Brown University, has 

continued in this role ever since. 

Humanist has become something of a model for electronic discussion lists. McCarty has 

maintained excellent standards of editing and the level of discussion is generally high. 

For those of us in Europe the regular early morning diet of three to six Humanist digests 

is a welcome start to the day. Humanist has become central to the maintenance and

development of a community and it has made a significant contribution to the definition 

of humanities computing. Its archives going back to 1987 are a vast source of information 

on developments and concerns during this period and it was taken as an exemplar by the 

founders of the Linguist List, the key electronic forum for linguistics. 

This period also saw the publication in print form of the only large-scale attempt to 

produce a bibliography of projects, software, and publications. Two volumes of the 

Humanities Computing Yearbook (HCY) were published. The first, edited by Ian 

Lancashire and Willard McCarty appeared in 1988 with some 400 pages. The second 

volume, for 1989–90, has almost 700 pages with a much better index. For several years, 

until it began to get out of date, the HCY was an extremely valuable resource, fulfilling 

the role originally taken by the Computers and the Humanities Directory of Scholars 

Active, which had ceased to appear by the early 1970s. Preparing the HCY was a truly 

enormous undertaking and no further volumes appeared. By the early 1990s, the general 

consensus was that in future an online database would be a more effective resource. 

Although there have been various attempts to start something similar, nothing on a 

serious scale has emerged, and the picture of overall activity in terms of projects and 

publications is once again incomplete. 

In terms of intellectual development, one activity stands out over all others during this 

period. In November 1987 Nancy Ide, assisted by colleagues in ACH, organized an 

invitational meeting at Vassar College, Poughkeepsie, to examine the possibility of 

creating a standard encoding scheme for humanities electronic texts (Burnard 1988). 

There had been various previous attempts to address the problem of many different and 

conflicting encoding schemes, a situation that was described as "chaos" by one of the 

participants at the Vassar meeting. Now, the time was ripe to proceed. Scholars were 

increasingly tired of wasting time reformatting texts to suit particular software and had 

become more frustrated with the inadequacies of existing schemes. In 1986, a new 

encoding method had appeared on the scene. The Standard Generalized Markup 

Language (SGML), published by ISO, offered a mechanism for defining a markup 

scheme that could handle many different types of text, could deal with metadata as well 

as data, and could represent complex scholarly interpretation as well as the basic 

structural features of documents. 

Participants at the meeting agreed on a set of principles ("the Poughkeepsie Principles") 

as a basis for building a new encoding scheme and entrusted the management of the 

project to a Steering Committee with representatives from ACH, ALLC, and the 

Association for Computational Linguistics (Text Encoding Initiative 2001). 

Subsequently, this group raised over a million dollars in North America and oversaw the 

development of the Text Encoding Initiative (TEI) Guidelines for Electronic Text 

Encoding and Interchange. The work was initially organized into four areas, each served 

by a committee. Output from the committees was put together by two editors into a first 

draft version, which was distributed for public comment in 1990. A further cycle of work 

involved a number of work groups that looked at specific application areas in detail. The 

first full version of the TEI Guidelines was published in May 1994 and distributed in 

print form and electronically.

The size, scope, and influence of the TEI far exceeded what anyone at the Vassar meeting 

envisaged. It was the first systematic attempt to categorize and define all the features 

within humanities texts that might interest scholars. In all, some 400 encoding tags were 

specified in a structure that was easily extensible for new application areas. The 

specification of the tags within the Guidelines illustrates some of the issues involved, but 

many deeper intellectual challenges emerged as the work progressed. Work in the TEI led 

to an interest in markup theory and the representation of humanities knowledge as a topic 

in itself. The publication of the TEI Guidelines coincided with full-text digital library 

developments and it was natural for digital library projects, which had not previously 

come into contact with humanities computing, to base their work on the TEI rather than 

inventing a markup scheme from scratch. 

Much of the TEI work was done by e-mail using private and public discussion lists, 

together with a fileserver where drafts of documents were posted. From the outset anyone 

who served on a TEI group was required to use e-mail regularly and the project became 

an interesting example of this method of working. However, participants soon realized 

that it is not easy to reach closure in an e-mail discussion and it was fortunate that 

funding was available for a regular series of face-to-face technical meetings to ensure that 

decisions were made and that the markup proposals from the different working groups 

were rationalized effectively. 

Apart from major developments in personal computing, networking, and the TEI, the 

kind of humanities computing activities which were ongoing in the 1970s continued to 

develop, with more users and more projects. Gradually, certain application areas spun off 

from humanities computing and developed their own culture and dissemination routes. 

"Computers and writing" was one topic that disappeared fairly rapidly. More important 

for humanities computing was the loss of some aspects of linguistic computing, 

particularly corpus linguistics, to conferences and meetings of its own. Computational 

linguistics had always developed independently of humanities computing and, despite the 

efforts of Don Walker on the TEI Steering Committee, continued to be a separate 

discipline. Walker and Antonio Zampolli of the Institute for Computational Linguistics in 

Pisa worked hard to bring the two communities of humanities computing and 

computational linguistics together but with perhaps only limited success. Just at the time 

when humanities computing scholars were beginning seriously to need the kinds of tools 

developed in computational linguistics (morphological analysis, syntactic analysis, and 

lexical databases), there was an expansion of work in computational and corpus 

linguistics to meet the needs of the defense and speech analysis community. In spite of a 

landmark paper on the convergence between computational linguistics and literary and 

linguistic computing given by Zampolli and his colleague Nicoletta Calzolari at the first 

joint ACH/ALLC conference in Toronto in June 1989 (Calzolari and Zampolli 1991), 

there was little communication between these communities, and humanities computing 

did not benefit as it could have done from computational linguistics techniques. 

The Era of the Internet: Early 1990s to the Present

One development far outstripped the impact of any other during the 1990s. This was the 

arrival of the Internet, but more especially the World Wide Web. The first graphical 

browser, Mosaic, appeared on the scene in 1993. Now the use of the Internet is a vital 

part of any academic activity. A generation of students has grown up with it and naturally 

looks to it as the first source of any information. 

Initially, some long-term humanities computing practitioners had problems in grasping 

the likely impact of the Web in much the same way as Microsoft did. Those involved 

with the TEI felt very much that HyperText Markup Language (HTML) was a weak 

markup system that perpetuated all the problems with word processors and appearancebased 

markup. The Web was viewed with curiosity but this tended to be rather from the 

outside. It was a means of finding some kinds of information but not really as a serious 

tool for humanities research. This presented an opportunity for those institutions and 

organizations that were contemplating getting into humanities computing for the first 

time. They saw that the Web was a superb means of publication, not only for the results 

of their scholarly work, but also for promoting their activities among a much larger 

community of users. A new group of users had emerged. 

Anyone can be a publisher on the Web and within a rather short time the focus of a 

broader base of interest in humanities computing became the delivery of scholarly 

material over the Internet. The advantages of this are enormous from the producer's point 

of view. The format is no longer constrained by that of a printed book. Theoretically 

there is almost no limit on size, and hypertext links provide a useful way of dealing with 

annotations, etc. The publication can be built up incrementally as and when bits of it are 

ready for publication. It can be made available to its audience immediately and it can 

easily be amended and updated. 

In the early to mid-1990s, many new projects were announced, some of which actually 

succeeded in raising money and getting started. Particularly in the area of electronic 

scholarly editions, there were several meetings and publications devoted to discussion 

about what an electronic edition might look like (Finneran 1996; Bornstein and Tinkle 

1998). This was just at the time when editorial theorists were focusing on the text as a 

physical object, which they could represent by digital images. With the notable exception 

of work carried out by Peter Robinson (Robinson 1996, 1997, 1999) and possibly one or 

two others, few of these publications saw the light of day except as prototypes or small 

samples, and by the second half of the decade interest in this had waned somewhat. A 

good many imaginative ideas had been put forward, but once these reached the stage 

where theory had to be put into practice and projects were faced with the laborious work 

of entering and marking up text and developing software, attention began to turn 

elsewhere. 

Debates were held on what to call these collections of electronic resources. The term 

"archive" was favored by many, notably the Blake Archive and other projects based in 

the Institute for Advanced Technology in the Humanities at the University of Virginia. 

"Archive" meant a collection of material where the user would normally have to choose a 

navigation route. "Edition" implies a good deal of scholarly added value, reflecting the

views of one or more editors, which could be implemented by privileging specific 

navigation routes. SGML (Standard Generalized Markup Language), mostly in 

applications based on the TEI, was accepted as a way of providing the hooks on which 

navigation routes could be built, but significant challenges remained in designing and 

building an effective user interface. The emphasis was, however, very much on 

navigation rather than on the analysis tools and techniques that had formed the major 

application areas within humanities computing in the past. In the early days of the Web, 

the technology for delivery of SGML-encoded texts was clunky and in many ways 

presented a less satisfying user interface than what can be delivered with raw HTML. 

Nevertheless, because of the easy way of viewing them, the impact of many of these 

publishing projects was substantial. Many more people became familiar with the idea of 

technology in the humanities, but in a more limited sense of putting material onto the 

Web. 

Although at first most of these publishing projects had been started by groups of 

academics, it was not long before libraries began to consider putting the content of their 

collections on the Internet. Several institutions in the United States set up electronic text 

or digital library collections for humanities primary source material, most usually using 

the OpenText SGML search engine (Price-Wilkin 1994). While this provides good and 

fast facilities for searching for words (strings), it really provides little more than a 

reference tool to look up words. Other projects used the DynaText SGML electronic book 

system for the delivery of their material. This offered a more structured search but with 

an interface that is not particularly intuitive. 

A completely new idea for an electronic publication was developed by the Orlando 

Project, which is creating a History of British Women's Writing at the Universities of 

Alberta and Guelph. With substantial research funding, new material in the form of short 

biographies of authors, histories of their writing, and general world events was created as 

a set of SGML documents (Brown et al. 1997). It was then possible to consider extracting 

portions of these documents and reconstituting them into new material, for example to 

generate chronologies for specific periods or topics. This project introduced the idea of a 

completely new form of scholarly writing and one that is fundamentally different from 

anything that has been done in the past. It remains to be seen whether it will really be 

usable on a large scale. 

The Internet also made it possible to carry out collaborative projects in a way that was 

never possible before. The simple ability for people in different places to contribute to the 

same document collections was a great advance on earlier methods of working. In the 

Orlando Project, researchers at both institutions add to a document archive developed as a 

web-based document management system, which makes use of some of the SGML 

markup for administrative purposes. Ideas have also been floated about collaborative 

editing of manuscript sources where people in different locations could add layers of 

annotation, for example for the Peirce Project (Neuman et al. 1992) and the Codex 

Leningradensis (Leningrad Codex Markup Project 2000). The technical aspects of this 

are fairly clear. Perhaps less clear is the management of the project, who controls or vets 

the annotations, and how it might all be maintained for the future.

The TEI's adoption as a model in digital library projects raised some interesting issues 

about the whole philosophy of the TEI, which had been designed mostly by scholars who 

wanted to be as flexible as possible. Any TEI tag can be redefined and tags can be added 

where appropriate. A rather different philosophy prevails in library and information 

science where standards are defined and then followed closely – this to ensure that 

readers can find books easily. It was a pity that there was not more input from library and 

information science at the time that the TEI was being created, but the TEI project was 

started long before the term "digital library" came into use. A few people made good 

contributions, but in the library community there was not the widespread range of many 

years' experience of working with electronic texts as in the scholarly community. The 

TEI was, however, used as a model by the developers of the Encoded Archival 

Description (EAD), which has had a very wide impact as a standard for finding aids in 

archives and special collections. 

An additional dimension was added to humanities electronic resources in the early 1990s, 

when it became possible to provide multimedia information in the form of images, audio, 

and video. In the early days of digital imaging there was much discussion about file 

formats, pixel depth, and other technical aspects of the imaging process and much less 

about what people can actually do with these images other than view them. There are of 

course many advantages in having access to images of source material over the Web, but 

humanities computing practitioners, having grown used to the flexibility offered by 

searchable text, again tended to regard imaging projects as not really their thing, unless, 

like the Beowulf Project (Kiernan 1991), the images could be manipulated and enhanced 

in some way. Interesting research has been carried out on linking images to text, down to 

the level of the word (Zweig 1998). When most of this can be done automatically we will 

be in a position to reconceptualize some aspects of manuscript studies. The potential of 

other forms of multimedia is now well recognized, but the use of this is only really 

feasible with high-speed access and the future may well lie in a gradual convergence with 

television. 

The expansion of access to electronic resources fostered by the Web led to other areas of 

theoretical interest in humanities computing. Electronic resources became objects of 

study in themselves and were subjected to analysis by a new group of scholars, some of 

whom had little experience of the technical aspects of the resources. Hypertext in 

particular attracted a good many theorists. This helped to broaden the range of interest in, 

and discussion about, humanities computing but it also perhaps contributed to 

misapprehensions about what is actually involved in building and using such a resource. 

Problems with the two cultures emerged again, with one that was actually doing it and 

another that preferred talking about doing it. 

The introduction of academic programs is another indication of the acceptance of a 

subject area by the larger academic community. For humanities computing this began to 

happen by the later 1990s although it is perhaps interesting to note that very few of these 

include the words "Humanities Computing" in the program title. King's College London 

offers a BA Minor in Applied Computing with a number of humanities disciplines, and 

its new MA, based in the Centre for Humanities Computing, is also called MA in Applied

Computing. McMaster University in Canada offers a BA in Multimedia. The MA that the 

University of Virginia is soon to start is called Digital Humanities and is under the 

auspices of the Media Studies Program. The University of Alberta is, as far as I am 

aware, the first to start a program with Humanities Computing in its title, although the 

University of Glasgow has had an MPhil in History and Computing for many years. 

As the Internet fostered the more widespread use of computers for humanities 

applications, other organizations began to get involved. This led to some further attempts 

to define the field or at least to define a research agenda for it. The then Getty Art History 

Information Program published what is in my view a very interesting Research Agenda 

for Networked Cultural Heritage in 1996 (Bearman 1996). It contains eight papers 

tackling specific areas that cover topics which really bridge across digital libraries, and 

humanities research and teaching. Each of these areas could form a research program in 

its own right, but the initiative was not taken further. Meanwhile the ALLC and ACH 

continued to organize a conference every year with a predominance of papers on markup 

and other technical issues. An attempt to produce a roadmap and new directions for 

humanities computing for the 2002 conference in Germany produced a useful survey 

(Robey 2002), but little new, and would perhaps have benefited from more input from a 

broader community. But how to involve other communities was becoming more of a 

problem in an era when many more electronic resources for the humanities were being 

developed outside the humanities computing community. 

Conclusion 

If one humanities computing activity is to be highlighted above all others, in my view it 

must be the TEI. It represents the most significant intellectual advances that have been 

made in our area, and has influenced the markup community as a whole. The TEI 

attracted the attention of leading practitioners in the SGML community at the time when 

XML (Extensible Markup Language) was being developed and Michael Sperberg- 

McQueen, one of the TEI editors, was invited to be co-editor of the new XML markup 

standard. The work done on hyperlinking within the TEI formed the basis of the linking 

mechanisms within XML. In many ways the TEI was ahead of its time, as only with the 

rapid adoption of XML in the last two to three years has the need for descriptive markup 

been recognized by a wider community. Meanwhile, the community of markup theorists 

that has developed from the TEI continues to ask challenging questions on the 

representation of knowledge. 

There are still other areas to be researched in depth. Humanities computing can contribute 

substantially to the growing interest in putting the cultural heritage on the Internet, not 

only for academic users, but also for lifelong learners and the general public. Tools and 

techniques developed in humanities computing will facilitate the study of this material 

and, as the Perseus Project is showing (Rydberg-Cox 2000), the incorporation of 

computational linguistics techniques can add a new dimension. Our tools and techniques 

can also assist research in facilitating the digitization and encoding processes, where we 

need to find ways of reducing the costs of data creation without loss of scholarly value or 

of functionality. Through the Internet, humanities computing is reaching a much wider

audience, and students graduating from the new programs being offered will be in a 

position to work not only in academia, but also in electronic publishing, educational 

technologies, and multimedia development. Throughout its history, humanities 

computing has shown a healthy appetite for imagination and innovation while continuing 

to maintain high scholarly standards. Now that the Internet is such a dominant feature of 

everyday life, the opportunity exists for humanities computing to reach out much further 

than has hitherto been possible. 

References for Further Reading 

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Hamilton-Smith (eds.), The Computer and Literary Studies (pp. 309–16). Edinburgh: 

Edinburgh University Press. 

Bessinger, J. B. and S. M. Parrish (1965). Literary Data Processing Conference 

Proceedings. White Plains, NY: IBM. 

Bornstein, G. and T. Tinkle (1998). The Iconic Page in Manuscript, Print, and Digital 

Culture. Ann Arbor: University of Michigan Press. 

Brown, S., S. Fisher, P. Clements, K. Binhammer, T. Butler, K. Carter, I. Grundy, and S. 

Hockey (1997). SGML and the Orlando Project: Descriptive Markup for an Electronic 

History of Women's Writing. Computers and the Humanities 31: 271–84. 

Brunner, T. F. (1993). Classics and the Computer: The History of a Relationship. In J. 

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Computing 2: 7–12. 

Burnard, L. (1987b). Principles of Database Design. In S. Rahtz (ed.), Information 

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Burton, D. M. (1981a). Automated Concordances and Word Indexes: The Fifties. 

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and the Early Centers. Computers and the Humanities 15: 83–100.

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Programs, and the Products. Computers and the Humanities 15: 139–54. 

Burton, D. M. (1982). Automated Concordances and Word Indexes: Machine Decisions 

and Editorial Revisions. Computers and the Humanities 16: 195–218. 

Busa, R. (1974-). Index Thomisticus. Stuttgart: Frommann-Holzboog. 

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of Convergence between Computational Linguistics and Literary and Linguistic 

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Literary Pedagogy. Literary and Linguistic Computing 6: 50–8. 

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Publishing Dictionaries: Proceedings of the European Science Foundation Workshop, 

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2. 

Computing for Archaeologists 

Harrison Eiteljorg, II 

Archaeology 1 is the study of history as written not with ink on paper but with the debris 

of human activity found where it fell. Whereas historians read the written records of our 

ancestors, archaeologists read the material record, either to augment the historic one or to 

reconstruct prehistory when there is no written record. As with the historic record, the 

archaeological record is often prejudiced by accidental survival, biased sources, and 

skewed representations of certain materials. Of course, both historic and material records 

from the past may carry inadvertent meaning. When Shelley's Ozymandias cried out, 

"My name is Ozymandias, king of kings, / Look on my works, ye Mighty, and despair!" 

he, as have many figures from the past, sent an ironic and unintended message. 

The archaeologist's work is, first, to find the material remains of our ancestors, second, to 

unearth those remains in ways that maximize the information they can convey, and, 

finally, to interpret the evidence. Finding those remains may be done by excavation or 

surface survey, but both processes require the destruction of the very evidence that is the 

first fruit of the work. In some cases, the destruction is literal and complete, as when an 

archaeologist must dig through one level of a site to find another; often the destruction is 

not so complete but involves the removal of objects from their physical contexts. Since 

contexts provide crucial clues to both physical and temporal relationships, evidence is 

lost even by removing objects. The destructive nature of the work demands 

extraordinarily careful record keeping to avoid accidental information loss. Indeed, it can 

be argued that record keeping is the real occupation of the field archaeologist. 

The interpretative process requires careful examination of the records of excavation or 

survey; the contexts of the objects are as important to a full understanding as the objects 

themselves. For instance, a figurine found in the remains of a tomb later covered by a 

house must have been deposited before the construction of the house and before the 

deposit of anything in that house. On the other hand, a potsherd found in the bottom of a

trash pit must have been deposited at roughly the time the pit was dug, which may have 

been long after the deposit of flint tools located near the potsherd but not in the trash pit. 

The importance of context to the archaeologist highlights the importance of good records. 

Records of both the archaeological contexts and the artifacts, not to mention a great many 

other aspects of an excavation or survey, provide the keys to analyses. Those records are 

truly crucial, and the potential utility of the computer for that record keeping is obvious 

today. Less obvious is the fact that, if computers are used to record the basic information 

on which archaeologists build their understanding of the past, all practitioners must 

ultimately be able to use computers to retrieve that information. That is, computers and 

computer skills will be needed by all archaeologists if a substantial portion of the 

archaeological record is maintained in computer form. 

It is now obvious that computers are ideal for record keeping, but when archaeologists 

first started using computers, in the late 1950s, computers were arcane and foreign. Their 

record-keeping potential could not then be utilized by academics because of costs and 

access limits; affordable microcomputers lay well in the future. Data entry required 

punchcards or tape, and results were only available on paper. As a natural result, 

computers were used for tasks that required considerable processing power, not routine 

data storage. Early uses therefore tended to be for statistical processing, and, though 

statistics had been used in archaeology for decades, more statistical procedures and more 

mathematically complex statistical procedures could be performed with computers. 

During these early years of computer usage in archaeology, archaeologists had to learn 

computer languages in order to prepare data for processing and then to carry out the 

statistical processes. That made the use of computers less likely to penetrate deeply into 

the field. Nevertheless, some saw even in the late 1960s the enormous potential for 

computers to store large quantities of information for retrieval from what were then 

called databanks. 

By the early 1970s, there had already been some conferences on archaeological 

computing, and the microcomputer revolution began later in that decade, though the first 

microcomputers did not have the advantage of the IBM name. One of the most important 

things the early microcomputers did have was the database management system, dBase. 

That program and its many offspring have had an enormous impact on archaeology 

because of their record-keeping potential. Database management programs make it 

possible to manage large datasets without first learning to write long and complex 

computer programs (though the need to write routines for specific operations remains). 

The database software of the mid-1970s and after brought the promise of efficient record 

keeping, and the new capabilities were desperately needed as excavators were then 

expanding the quantity of material collected. For instance, studies of plant and animal 

remains in the archaeological record (to understand food sources and the surrounding 

ecosystem) required sifting through large quantities of earth to find seeds and bones that 

could only be interpreted with statistical analyses; such work cried out for sophisticated 

data-handling techniques. Similarly, more careful and conscientious attention to small

finds and fragmentary evidence could only become common with the advent of better 

recording techniques. It goes without saying that the recording of all these data would 

have been of little use had the programs not also made the retrieval of information – in an 

incredibly wide variety of forms – more efficient and flexible. 

Microcomputers – with relatively easy-to-use software – arrived just as those needs for 

more sophisticated data handling were being felt. As a result, database technology 

seemed to many a godsend. That is not to say that all archaeologists immediately began 

using computers and databases, or even that all have adopted them today, but the need for 

assistance with ever-more-voluminous records and the availability of sophisticated 

computer programs fed on one another. Archaeologists who were eager to deal with 

seeds, shells, bones, and small, fragmentary finds could do so, and the fact that they could 

record so much information with the aid of computers encouraged them to treat that level 

of record keeping as standard. That pushed others to attend to similar levels of detail and, 

of necessity, to turn to computers to help. The process is continuing, with new 

technologies being regularly added to the tool kit. 

The acceleration of computer use is ongoing, and the extent to which the quantity of 

recorded data may overwhelm the scholar's ability to synthesize is a matter of some 

debate. Until archaeologists can dispassionately evaluate the utility of different 

approaches to data collection, there is a natural tendency to record more, whether or not 

all the information is useful. 

The growth of computer use spawned the organization called Computer Applications and 

Quantitative Methods in Archaeology (CAA). It began with a small meeting at the 

University of Birmingham (England) in 1973 and has grown to an international 

organization with annual meetings in various cities in Europe. In December 1984 the 

Archaeological Computing Newsletter was launched to report on information about 

archaeological computing, and by the mid-1980s established professional archaeology 

organizations featured regular sessions about one or another aspect of computing at their 

annual meetings. 

The early and lasting interest in databases stemmed not only from the need to control 

huge quantities of excavation data but also from the hope that data storehouses could be 

used by scholars to retrieve and analyze information from related excavations, thus 

permitting broader syntheses. Indeed, many archaeologists still hope for such 

aggregations of data. While that giant data warehouse has been seen by many as the pot 

at the end of the rainbow, those most intimately familiar with the technology have, from 

the beginning, seen aggregated databases as a far more distant goal at best. Even 

archaeologists working in the same cultural and geographic areas do not – and cannot – 

excavate, survey, record their results, or use terms in precisely the same ways. As a 

result, combining data from multiple projects remains an illusive goal. Efforts to impose 

standardization have met with little success, and even such a carefully crafted and 

unthreatening potential aid as the Getty Art and Architecture Thesaurus has not been 

noticeably helpful in bringing the scholars in the field into terminological uniformity for 

a single language, much less across languages.

The difficulties with common terms and data structures have been exacerbated by the 

divergence between the needs of the archaeological community and those of museum 

professionals. Archaeologists and the museum curators who ultimately receive the 

excavated artifacts record their information about those artifacts in strikingly different 

arrangements, the one beginning with excavation context and the other with either a 

cultural classification or an object-based classification system. As a result, the databases 

of the two groups are organized differently, making large-scale cooperation problematic. 

Large-scale constructed datasets such as the collection of information about 

archaeological sites in Turkey (The Archaeological Settlements of Turkey website online 

at ) are less ambitious than the enormous, automatic aggregations 

once anticipated, but they are now becoming more common and often prove to be 

remarkably useful. Another good example of such constructed databases is the National 

Archeological Database of US public archaeological sites (online at 

) developed by the National Park Service and 

the Center for Advanced Spatial Technologies at the University of Arkansas (CAST, 

). Websites that serve as gateways to 

information, for instance, ARGE, the Archaeological Guide to Europe 

(), have also proved to be very valuable, and a recent project, 

called OASIS (), which makes possible timely 

access to information about archaeological projects throughout Britain, is an excellent 

example of real benefits for scholars by providing access to disparate data through a 

common central point but without imposing unrealistic new standards. 

Combining disparate datasets may not be a realistic near-term goal, but preserving 

datasets for future access is a necessity now. There are active digital repositories now 

available to archaeologists for the long-term preservation of their data files, and 

preservation of digital data is a major responsibility. The recovered artifacts and the data 

about them are the only surviving evidence of fieldwork. Neither is fully meaningful 

without the other. It may also be argued that economic development will result in fewer 

possibilities for excavation and field survey, making excavations in museum basements 

and old records the archaeology of the future. 

There is a serious problem with the expansion of data quantity and the consequently 

increasing use of databases. When archaeologists use computers to view data rather than 

spending time with the objects themselves, they risk losing the familiarity with the 

objects that can only come from sustained, intimate, physical contact. 

Databases were recognized as valuable tools quickly; so were graphical applications. 

Early graphics programs could only generate printed output – and slowly, very slowly. 

Nevertheless, maps and drawings have been so integral to the record keeping of the 

discipline that the development of graphics aids was an obvious necessity. The earliest 

graphics products were maps, but there were also programs developed before the end of 

the 1970s for drawing plans and sections. The early programs, often written by scholars, 

replicated many of the hand-drawing processes, and the drawings – the physical products

– were the desired results; the underlying computer data simply represented a means to an 

end. 

Programs were written by scholars to create maps, and even digital terrain models 

(draped grids illustrating the undulations of terrain) could be created from survey data 

before the advent of the personal computer. Maps were simply drawings, but powerful 

mapping software providing other sophisticated features was developed for other 

disciplines; that software, called geographic information system (GIS) programs, has 

been eagerly used by archaeologists, starting in the mid-1980s. For these programs the 

underlying data are far more important than any particular drawing. 

GIS programs combine maps and data about maps in ways that bring significant benefits 

to archaeology. The data about the maps are of two kinds, standard relational data tables 

(with information about artifacts, flora, fauna, etc.) linked to areas or points on maps, and 

information derived from map data, such as the steepness of the grade in a given area 

(from contour lines or point-source elevation data). The crucial benefit of GIS is the 

connection between bounded portions or individual points on a map and data about them 

– and the ability to analyze the data according to any of the available criteria. The 

resulting ability to analyze material remains in concert with the physical environment is 

extremely powerful. 

Vector-based maps rely upon stored data points and are only scaled when output to a 

screen or paper; as a result the precision of coordinates available from a vector-based 

map is limited only by the survey technology that produced the data points. On the other 

hand, raster-based maps are scaled drawings, and a given map may be at virtually any 

scale. Issues of scale and precision may thus become very complex, and using together 

maps with differing scales, precision, and levels of generalization (e.g., how many survey 

points define a river course or a coastline?) can yield very misleading results. These 

issues of scale, precision, and generalization with GIS make it necessary that users of GIS 

data be sophisticated and self-conscious in their use of maps and the data connected to 

them. 

The use of GIS has been aided in archaeology, as in other disciplines, by the fact that GIS 

data – maps and data files – can be created for one use/user and re-used by many others. 

Archaeologists may use GIS maps created by and for others, e.g., digital elevation maps 

made for the military, in addition to maps and data tables of their own. This is not an 

unmixed blessing, however, since available data – whether maps or data tables – may 

determine the questions asked or, more worrisome, the ones not asked. 

GIS software is the graphics road taken for mapping. The other graphics road, taken for 

plans and other record drawings, is computer-assisted design (CAD) software. The 

advent of CAD programs for personal computers brought significant and almost 

immediate change to record keeping for many archaeologists. Although CAD programs 

were created to assist with design processes, it was a short step to see their utility for 

modeling the existing world and existing structures; archaeologists began to use them in 

the mid-1980s.

Many archaeologists initially used CAD programs as drafting aids only, treating the 

computer-generated drawing as the final product. Plans and elevations can be generated 

at various scales and with differing emphases without time-consuming redrafting 2 . 

However, dimensional information can be retrieved from the CAD file at original 

measurement precision, unaffected by drawing scale, and the computer data can be 

segmented so that specific phases or other selections from the entire digital file can be 

included or excluded in any specific drawing. As a result, CAD data eventually came to 

be recognized as more full and complex than any individual drawing. 

The larger promise of CAD lay in its ability to record three-dimensionally and to generate 

drawings based on the full 3-D geometry of a site or structure. As database technology 

made it possible to record the added information that archaeologists were unearthing in 

the 1960s and 1970s, CAD made it possible for archaeologists to record 3-D information 

easily and with the precision of the original field measurements. Fortunately, advanced 

surveying equipment, particularly the total station and, to a lesser extent, desktop 

photogrammetry, made the gathering of 3-D data far easier. The 3-D capabilities of CAD 

and advanced surveying instruments have led to very complex 3-D models as 

archaeological records. The models record the full geometry, and all dimensions can be 

retrieved at surveyed precision. The 3-D models can also be segmented to permit any onscreen 

view or paper drawing to include or exclude parts of the whole according to the 

needs of the moment. 

Scholars conceive of CAD models of structures in two quite different ways. For some, 

the CAD model is the kind of record that a drawing once was – containing all the 

dimensional and geometric information known from surveying. For others the model is 

the starting point for reconstructing missing parts or phases, for making realistic images, 

or for envisioning larger ensembles, cityscapes, or landscapes. Those intending to 

reconstruct, illustrate, or envision the past may not need an exact record of existing 

conditions. As a result, two approaches to CAD modeling – one based on record-keeping 

practices and the other less concerned with precise dimensions – have been common. In 

general, archaeologists have been more likely to use CAD as a record-keeping 

technology, since their approach to data gathering emphasizes such recording. 

Archaeologists and architectural historians dealing with older and less complete 

structures have often used CAD for precise records as well. 

Archaeologists using CAD models principally for reconstructing, illustrating, or 

envisioning the past have worried less about precise dimensions and more about 

appearances. Buildings or larger ensembles imagined with the aid of the computer and 

with simpler, idealized dimensions are much easier and less expensive to make and can 

be presented in ways that are extremely compelling. However, using CAD as an aid to 

illustrate reconstructed realities nearly always involves subsidiary technologies, usually 

rendering programs or virtual reality programs that use CAD models as their starting 

points. 

Reconstructions based on CAD data – even idealized and simplified CAD data – have 

one very important benefit over hand-drawn reconstructions: they must be based on

fitting geometric shapes to one another. Therefore, CAD models are limited by the rules 

of geometry. A CAD program will only generate a reconstructed view based on the rules 

of geometry, not the hopes or dreams of a scholar. 

Computer reconstructions can be truly photorealistic, providing believably real images. 

That has drawbacks as well as obvious benefits. The photorealistic views generally omit 

the inevitable irregularities of real structures, dirt and grime, marks of age and 

deterioration, nearby vegetation, and so on. The surrounding structures must often be 

omitted as well – or be included as if they were fully known when they are not – or be 

shown only as featureless blocks. Standing alone or in a generalized context, an ancient 

structure looks unnatural; placed in a hypothetical context it provides a sense of reality 

that exceeds our knowledge. The root problem here is not with the computer but with the 

necessarily partial state of our knowledge. 

Virtual reality (VR) systems based on CAD models promise realistic visions of computer 

worlds through which users may navigate. However, they face similar problems of 

inadequate data. A new hybrid system provides reconstructions that are visible in glasses 

through which the actual remains can be seen at the same time. This promises a better 

marriage of the real and the reconstructed, but the technology has yet to be proved in 

actual use. 

VR and generalized models permit serious examination of larger settings. Both VR 

worlds and very generalized models can provide information about landscapes, urban 

scale, open spaces, communication arteries, and other aspects of both the natural and the 

constructed world. 

Archaeologists have used CAD, GIS, and databases primarily for data recording. In fact, 

that is the most important benefit the computer has brought to archaeology – the ability to 

manage more data more effectively. Of course, managing the data effectively implies 

accessing the data effectively as well. Databases, CAD models, and GIS programs all 

provide many ways to retrieve information, to analyze, to ask questions of the data, and 

to understand better what has been found and the relationships between and among those 

finds. That retrieval, of course, requires some computer skill. 

There are concerns unique to archaeology that complicate the use of all these 

technologies. For example, terminology issues greatly complicate the potential to share 

data with all these technologies, as already mentioned regarding databases. In addition, 

the ways archaeologists apply CAD and GIS technologies to data recording are quite 

different from the ways the software developers expected. Standards designed by the 

developers do not generally suffice for scholarly users, and the development of scholarly 

standards has been slow and ill-focused. The Archaeology Data Service in England has 

been especially helpful in this area, producing guides to good practice for scholars. 

Much early archaeological computing required scholars to write their own programs, but 

that is no longer necessary. Most now use commercial software, although special 

archaeological problems such as certain statistical procedures and seriation routines

continue to inspire programming by archaeologists. Another area in which programming 

has remained a necessity is that of simulation. Using computers to simulate development 

in relatively simple societies was considered a very promising technique as early as the 

1960s, but, as the popularity of the "new archaeology" waned, so did the enthusiasm for 

simulation. Those interested in simulation today are more likely to use GIS, CAD, or 

statistics and to compare various predefined scenarios to test possible explanations of 

development, than to use simulation algorithms or artificial intelligence algorithms, 

which have seen little use in archaeology. 

The use of commercial software has made it easier for scholars to work cooperatively on 

data, since many can access the same data files – whether data tables, CAD models, or 

GIS datasets – for data entry, examination, or analysis. The potential to share digital files 

over the Internet, of course, has made collaboration even easier and more common. In 

some cases, excavators have entered data into on-site database systems connected to the 

Internet so that the data can be accessed almost immediately after being entered. 

The value of the Internet for communications is easily missed in a discussion of 

technology; it seems too obvious and pervasive to need mentioning. In archaeology, 

however, colleagues are routinely working together on projects while living on different 

continents. Simple e-mail can be crucial in such cases. For communicating to a wider 

audience, of course, use of the Web has already changed the extent to which scholars can 

and will offer their information to the public. 

Computers have also aided archaeologists' use of photographs. Photographs of buildings, 

sites, features, and objects are central to the practice of archaeology, and digital imagery 

has not changed that. However, it has made possible the use of color for a much greater 

portion of the photographs – not in printed publications but over the Internet where the 

cost of a color photo is not noticeably different from the cost of a black-and-white photo. 

(In printed publications the problem remains. Small print runs make color prohibitively 

expensive.) The value of color is greater than one might imagine; scholars need to see 

colors accurately if they are fully to grasp the appearance of a structure or object. In 

particular, ceramic analysis demands careful attention to colors, but it is much too 

expensive to print color photos of the sherds that make up such a large portion of the 

recovered material from an archaeological project. 

Digital imagery has also brought better, less time-consuming, and less expensive 

enhancements of photographs. Such enhancements aid in interpreting satellite images or 

the stratigraphy represented in a trench wall by subtly changing colors. In at least one 

instance, photographs of faded and damaged fragments of frescoes were digitally 

enhanced to aid the restoration work, and satellite photographs of the Near East have 

been used to locate ancient roadways that seemed to have left no evidence on the 

landscape. 

Only recently coming into use has been photography/digitizing that includes a 3-D 

modeling component so that photographs and 3-D digitizers can be used to provide 3-D 

geometry of objects as well as color and tone. Objects can be modeled with these

techniques so that scholars can see – and rotate and measure – fully 3-D representations 

of objects on a computer. While still too expensive for general use, these processes 

provide unprecedented access to objects without risk of damage, although they also 

remove the scholars from the objects themselves. 

Sharing digital data over the Internet may be important for archaeologists, but scholarly 

electronic publication – over the Internet or on CDs – has not met the predictions of its 

early supporters. Some web-based monographs have appeared, as have some publications 

on CDs, and Internet Archaeology has been extraordinarily innovative in putting complex 

digital data on the Web as journal "articles." Sadly, very few of the electronic 

monographs seem to offer real permanence. CDs have a lifespan that can probably be 

measured in years, not decades, and too many websites are ephemeral. Thus, too few 

electronic publications provide the permanence required. Probably more important, 

electronic publication still does not normally carry the value of paper publication when 

advancement or promotion is considered. 

Although electronic monographs may not become common, web-delivered journals such 

as Internet Archaeology and supplements to printed articles, as pioneered by the 

American Journal of Archaeology, promise to bring more digital information to more 

archaeologists. That is not sufficient, however, because archaeology desperately needs 

the potential offered by electronic publication. The volume of material collected in the 

course of a project simply cannot be presented in a book or even a multi-volume series. 

Indeed, the use of databases, CAD models, and GIS programs creates a concomitant need 

to "publish" databases, CAD models, and GIS datasets – which can only be done 

digitally. Since electronic publication in the sense of a single, unified item that can be 

called the publication of a project now seems unlikely, and since there must be ways for 

archaeologists to obtain the electronic data along with the analyses, syntheses, and other 

expository texts that combine to make the record of a project, the future seems to lie in 

hybrid publications involving either printed or web-served text coupled with permanent 

access to the digital files that are the basic record of the project. Archival repositories for 

those files are required as part of this approach, and they are necessary anyway, as 

mentioned previously, to preserve the original data. There have been some successful 

repositories, most notably the Archaeology Data Service in the United Kingdom. At the 

same time, however, there is not a universally understood responsibility on the part of all 

archaeologists to prepare digital material for such repositories – not a trivial task – and 

then to make the actual deposit; nor have funding agencies generally recognized the 

importance of the long-term preservation of digital data 3 . Therefore, hybrid publications 

– text plus access to digital files – remain uncommon. 

Should the day come when hybrid publications are common, a major obstacle remains. 

Despite the importance of computers and computing to archaeology, too few scholars are 

being trained in the use of computers. Scholars have bemoaned the problem for more 

than 15 years (see Richards 1985; Eiteljorg 2001), but there remains a divide between 

those who understand the computer technologies required for the discipline and those 

who do not. The divide is not a matter of age but of education; young scholars are not 

required to learn computer skills in graduate programs. In addition, young scholars who

seem computer-savvy often lack precisely the skills most needed for archaeological 

computing – skills with database design, CAD programs, and GIS. Although it is now a 

given that any archaeology project will involve the use of computers, it is not a given that 

the project directors will know how to use them well or have the requisite skills to find 

helpers who do. Nor is it a given that the archaeologists of the future will be able to use 

the digital data created in the field today. Unfortunately, those who are adept with the 

technologies must often be self-taught, although these technologies are better learned 

from experts who understand the problems and pitfalls that are likely to be encountered. 

This problem of untrained or self-taught users of computer technology is not widely 

recognized or acknowledged in the field of archaeology at large, at least in part because 

archaeologists have not realized that all archaeologists need at least to be able to retrieve 

digital data from computer databases, CAD models, or GIS datasets. The absence of 

formal training represents a serious impediment both to effective application of computer 

technology and to the reuse of the digital data already gathered in computer form. 

Despite the many changes computers have brought to archaeology, the transformation 

from paper-based to digital recording is still incomplete. The discipline at large has not 

fully absorbed the need to preserve access to digital data for future scholars. It has not yet 

found an effective and relatively standard way to present digital data as part of a final 

publication. Its educational institutions have not accepted the need to prepare all 

archaeologists in the use of those computer technologies necessary in the field and the 

laboratory. In part, these problems reflect the nature of the discipline, a uniquely 

fragmented one consisting of practitioners who may have begun as historians, art 

historians, students of ancient languages, or anthropologists – but not as scientists 

dependent upon a tradition of prompt and full data sharing. The problems also reflect the 

unique independence of archaeologists, who must have a strong entrepreneurial spirit in 

order to fund and operate complex projects. To the extent that these problems will be 

solved, as they surely will, it is likely that centralized initiatives such as the Archaeology 

Data Service in England will be crucial to the process. Other countries are pursuing 

similar avenues, but the more decentralized United States may find a voice in the process 

only through individual institutions seeking practical standards as a matter of sheer 

necessity. 

See also chapter 15: Databases. 

Notes 

1 Archaeology as a discipline may be seen as monolithic, and archaeologists may be 

expected to know about the archaeology of any cultural or geographic area. In fact, 

however, archaeologists specialize very early in their educational careers, and areas of 

specialization can be remarkably narrow. Consequently, readers should be forewarned 

that the author may not be aware of developments far from his archaeological ken. 

2 Other programs can produce drawings at various scales and may seem to have the 

necessary virtues of CAD, but the use of 3-D Cartesian grid systems as the core for data 

storage makes CAD more than a drafting aid.

3 Despite the obvious need for archiving archaeological information, archival 

preservation of paper records has not been routine. Thus, the situation with digital data 

can be seen as a continuation of sadly normal archaeological practices. 


Allen, Kathleen M. S., W. Green Standton, and Ezra B. W. Zubrow, (eds.) (1990). 

Interpreting Space: CIS and Archaeology. London, New York, Philadelphia: Taylor and 

Francis. 

Badler, Norman and Virginia R. Badler (1977). SITE: A Color Computer Graphics 

System for the Display of Archaeological Sites and Artifacts. Philadelphia: University of 

Pennsylvania, Moore School of Electrical Engineering, Department of Computer and 

Information Science. 

Binford, Sally R. and Binford, Lewis R., (eds.) (1968). New Perspectives in Archeology. 

Chicago: Aldine. 

Duncan, J. M. and P. L. Main (1977). The Drawing of Archaeological Sections and Plans 

by Computer. Science and Archaeology 20: 17–26. 

Eiteljorg, H., II (2001). Computing in the Archaeological Curriculum. CSA Newsletter 

14, 2. Available at: http://csan.org/newsletter/fall01/nlf0104.html. 

Richards, J. D. (1985). Training Archaeologists to Use Computers. Archaeological 

Computing Newsletter 2: 2–5. 

Richards, J. D. and N. S. Ryan, (eds.) (1985). Data Processing in Archaeology (from the 

series Cambridge Manuals in Archaeology). Cambridge: Cambridge University Press. 

Sabloff, Jeremy A., (ed.) (1981). Simulations in Archaeology. Albuquerque: University of 

New Mexico Press. 

Upham, Steadman, (ed.) (1979). Computer Graphics in Archaeology: Statistical 

Cartographic Applications to Spatial Analysis in Archaeological Contexts 

(Anthropological Research Papers no. 15). Tempe: Arizona State University. 

The following contain numerous articles concerning archaeological computing and 

should be consulted broadly: 

Annual proceedings of the meetings on Computer Applications and Quantitative Methods 

in Archaeology beginning in 1973 (published for that one year only as Science in 

Archaeology, 9). 

Archaeological Computing Newsletter (tables of contents from number 45 (Spring 1996) 

onward may be found at . The

Archaeological Computing Newsletter will be published by Archeologia & Calcolatone 

from the end of 2004. 

CSA Newsletter (All issues beginning with volume 8 are available on the Web, 

; volumes 13 and following are only available on the 

Web.) 

Selected Websites 

– the website for the excavations at 

Çatalhöyük, Turkey, where modern technology has not only been extensively used but 

discussed and debated. 

– information about the site Teotihuacan, a site that 

has benefited from extensive use of computer technology and at which computers have 

been used for a very long time. 

– should be used in concert with the 

publication by James Packer (1997), The Forum of Trajan in Rome (Berkeley: University 

of California Press), for an example of an architectural historian using both traditional 

and computer methods. 

http://ads.ahds.ac.uk/> – the Archaeology Data Service in England (and 

for the ADS Guides to Good 

Practice and for a different version of 

the CAD Guide to Good Practice). 

– online journal, Internet Archaeology. 

- CSA Propylaea Project, still in early stages, to construct a stoneby-stone 

CAD model of the Propylaea. Discussions of methods, but only the CAD model 

of the predecessor is available. 

– excavations at Lahav, Israel, with 

discussion of data collection methods and computer use in the field. 

– Amiens cathedral project at 

Columbia University, with extensive imagery showing the illustrative power of CAD and 

rendering tools. 

– Savannah urban design/history/development project of the 

Savannah College of Art and Design, still in early stages. Only Windows users need 

apply, alas. 

– a study of hill forts in France with 

effective use of GIS technology.

– the Corinth Computer Project at the 

University of Pennsylvania. 

– the Digital Archive Network for Anthropology at 

North Dakota State University, a project involving the use of 3-D models of 

archaeological objects that may be examined on the Web. 

3. 

Art History 

Michael Greenhalgh 

Introduction 

The discipline itself in this context should be understood to include not only lecturers and 

students who impart and acquire knowledge, and curators who look after resources such 

as slides, but also the staff of museums and galleries, with the oversight and care of large 

quantities of works, usually many more than can be displayed. And we should remember 

that other historians need and use images from the past, 1 a fact reflected in the broad 

church of participants included in conferences. 2 In large collections especially, computers 

were early recognized as an essential tool in regimenting what was often incomplete 

paper documentation for the use of curators, conservators, registrars, and, eventually, the 

general public. 

Art historians use computers to order, sort, interrogate, and analyze data about artworks, 

preferably with images; increasingly they wish to use the Web as the carrier for 

multimedia research or teaching/learning projects. The fact that primary data for art 

historians are visual – images, still and perhaps moving – did not prevent an early interest 

in the ordering and interrogation of text data arranged into databases (following the 

development of automation in libraries) or studying how to deal with the quantitative data 

churned out by a counting machine (Floud 1979). 

This chapter charts such developing use, from the development of text databases through 

the introduction of digital imaging and then to the use of the Web in teaching and 

learning. It does not consider the use of computers in art, 3 nor yet text bibliographies of 

art historical material, such as the Bibliographic d'Histoire de I'Art, which are librarybased 

activities. It illustrates how disciplinary innovation in this area is inevitably 

dependent upon and triggered by technical innovation, and how the introduction and 

popularization of the Web with its consistent interface has brought new possibilities for 

computer use in learning. Each section introduces and explains the various problems, and 

then charts with relevant links how various people and organizations tried to solve them. 

Far from hymning today's computing world as a nirvana, the theme throughout is of 

difficulties with expertise, communication, and finance – that is, with people rather than 

technology. These are problems that intensify as the technologies and the potential

applications become more sophisticated (cf. the Virtual Museum of Computing: see 

http://vmoc.museophile.org/). 

Text Databases 

Early computers were unsuitable for humanities use: large, slow, expensive, and with 

software designed by and therefore for scientists, they were jealously guarded. Using one 

to write a book (with the attraction of supposed "automated indexing") was seen as nearblasphemy 

and, in any case, was difficult given the restricted software and the way such 

machines dealt with text. The construction of databases was no easier, because interface 

programs, which softened and simplified data entry and retrieval, were rare. And the 

discipline had two problems, which did not confront run-of-the-mill author-title-date 

databases: the part-whole problem (i.e., how to catalogue a multipart altarpiece, let alone 

a Gothic portal or a complete cathedral), and the element of imprecision, which is often 

part of historical data. 

What is more, the interchangeability and communication we take for granted (and 

demand) today did not exist: operating system did not necessarily talk unto operating 

system; input was usually via a text terminal if not via punchcards or paper tape 

(certainly not a VDU); interfaces hovered between the obscure and the feral; output was 

to line printer or daisy-wheel or, with some difficulty, to phototypesetter; and intercomputer 

communication was tricky because of the myriad of physical and logical 

formats in use. Nevertheless, some institutions (such as the Detroit Institute of Arts: cf. 

today's Visual Resources Art Image Database) used computers to develop catalogues of 

their collection, perforce using specially commissioned software, or adapting mainframe 

programs (such as IBM's STAIRS) to their needs. By the early 1980s, art historians 

began to develop database projects that grappled with the problem of describing images 

(see Corti 1984). As in other areas (such as theater history: cf. The London Stage, see 

Donahue, website) the road was often rocky and long, with memory and storage 

restrictions of early systems demanding laconic descriptors (Villard 1984); and funding 

for staff, machinery, and programming difficult to find. The Universit` degli Studi in 

Siena was early in the field, with a how-to-do-it manual (Bisogni and Corti 1980) 

followed by one on methodology describing projects (Bisogni 1980), two giving 

exemplary applications (di Bari et al. 1981) and yet another tackling the problem of 

dealing with images (Bisogni 1981). 

The first problem in establishing art historical databases was terminology. Even well after 

the penetration of the microcomputer, the gap between the attitudes of visual resources 

(VR) curators and academics was clear: few of the latter would take an interest in 

computerization until machine and network could demonstrably help with teaching or 

research; and all who had done battle with different slide rooms were quite used to a 

flexible (read: vague) use of terminology. In any case, most departments had "legacy" 

collections of slides, and few intended to relabel them all to fit some grand scheme. The 

VR curators were more precise, reasoning that chaos would result unless the same 

terminology was employed. Several conferences concentrated on this important matter 

(see Roberts 1990). Standards are fine, but is it the researchers who want them or the

librarians? When somebody at such a conference opined that For every simple problem, 

there is a complicated solution, perhaps she was thinking of the Art History Information 

Program 4 at the Getty, and its Art and Architecture Thesaurus: begun pre-computers, it 

offers an excellent set of standards, but these are expensive to implement and far from 

universally applicable 5 . The fears and expectations of museum professionals are well 

reflected in the journals of the period 6 . 

In any case, how to catalogue an artwork? One possibility was by meaning; and a fruitful 

project which began without any connection with a computer is ICONCLASS, the origins 

of which go back to 1954, and which provides an iconographical classification system, 

and now a CD-ROM, and web browser software. 

In these days before effective and cheap graphics, the discipline's associations and 

journals naturally concentrated on database matters. Thus, Computers and the History of 

Art publishes since 1991 a journal (CHArt, a development of a Newsletter begun in 

1985), holds annual conferences, usually themed, and now publishes them on the Web. 

The Visual Resources Association, initially curators meeting annually since 1968 at 

College Art Association conferences, was founded formally in 1982: it now has some 600 

members and a quarterly Bulletin, and correctly titles itself "The international 

organization of image media professionals" with members working in "educational and 

cultural heritage environments." The range of the membership indicates just how broadly 

"Art History" images are used and the problems and possibilities they entail are 

recognized: they include information specialists; digital image specialists; art, 

architecture, film, and video librarians; museum curators; slide, photograph, microfilm, 

and digital archivists; architectural firms; galleries; publishers; image system vendors; 

rights and reproductions officials; photographers; art historians; artists; and scientists. 

The Lure of "Intelligent" Software 

But surely computers are "thinking" machines, or so it was too often assumed. Much was 

made in the 1980s of computers as "intelligent", and able to learn and therefore respond 

just like a teacher. It should be difficult today to conceive of the computer as any more 

than a speedy idiot obeying a set of precise instructions; but many in the humanities do 

indeed continue to regard it as a black and magical (or black-magical) box – throw the 

data at it, and somehow the computer will make sense of them. 

Such a continuing substitute for idol-worship has had many adverse consequences, which 

continue to this day. The first was in the early 1980s, when the heralded "Fifth 

Generation", to be developed by the Japanese (the very mention of MITI struck 

apprehension into the Western heart), was to produce new levels of software. Second, this 

was the age, not coincidentally, when Artificial Intelligence programs – AI – would 

supposedly take over medical diagnosis, mineral exploration, and, of course, education. 

Intelligent systems were also proposed for art historians, because computer databases 

were seen as able to provide intelligent answers to researchers' questions, such as Cerri's 

expert system for archaeologists and art historians. Characteristically, Cerri was from the 

Computer Department at the Scuola Normale Superiore (SNS) at Pisa. The SNS provided

conferences, examples, and publications to drive along the application of computer 

processing to art history 7 . 

Graphics 

The breakthrough for art historians and other humanists came not with the introduction of 

microcomputers in the later 1970s (usually tricky to program and to use, with strange 

storage possibilities and nil communication, and intended for aficionados) but with the 

IBM PC in 1981 and then the Macintosh. I borrowed one from a computer laboratory at a 

large American University in late 1981: perhaps not recognizing the cuckoo in their 

superior mainframe nest, they hadn't played with it and didn't want to do so (just as many 

IBM engineers considered it a toy). As is well known, that machine with its open (i.e., 

easily and freely imitable) architecture led to an explosion of cheap machines with 

increasingly user-friendly and sometimes useful software, a cheap storage medium (the 

floppy disk) and, eventually, a hard disk and then CD-ROM. In 1984 arrived the Apple 

Macintosh, the first PC with a graphical user interface. The cheapness and apparent 

utility of micros set many people pondering how they might organize, access, and 

disseminate the mountains of text and graphics data used in art historical research and 

teaching (see Heusser 1987). 

Pictures on a computer were now theoretically possible for every art historian, even if 

only in greyscale. Given the puny power of such machines, however, they were not yet 

feasible – but that would change in the following decade. If the first programs that 

ensured the popularity of micros were spreadsheets, the second were database packages, 

for the first time enabling art historians and museum personnel to construct at minimal 

cost databases of artifacts. Until the late 1980s, these were text-only, and sometimes 

relational. Only with the more powerful hard-disk machines of the late 1980s did image 

databases come along, and then the users faced a perennial dilemma: should they 

purchase a commercial product, paying for the privilege of testing it as version follows 

version, and lock themselves into a proprietary setup? Or should they go for open rollyour-own 

software? Luckily, with the development of the freeware/shareware ethos, 

good open programs could be had for little money; while the development of avowedly 

non-proprietary operating systems such as Linux not only made people look at the PC 

with greater fondness, but also encouraged the development of freely distributable 

software in all application areas. 

So was it immediately "goodbye 35 mm slides, hello digital images"? If all successful 

computers have been digital machines from the start, the last twenty years have 

highlighted the problem of how to deal with analogue materials in an increasingly digital 

age. For the art historians, this means principally slides and photographs. The 1960s had 

seen the move from 6 × 6 lantern slides (usually greyscale) to 35 mm slides (usually 

color), and most university departments built up large collections which had to be bought, 

labeled, catalogued, perhaps mounted, and sometimes repaired. With strong light 

projected through them, slides fade: a prize exhibit in one collection was six slides of the 

selfsame Monet Haystack, all with different coloration, and none that of the actual 

painting. Every collection had much the same basic set of slides, curated by expensive

personnel: might money not be saved by collecting such images onto a more cheaply 

reproducible medium? 

The late 1980s also saw the introduction of powerful workstations with sufficient 

memory and screen resolution to manipulate graphics. These were still too expensive for 

most people, but pioneering projects such as Marilyn Aronberg Lavin's Piero Project and 

the English VASARI Project were funded, and demonstrated the possibilities of highpower 

computer graphics (including VRML models [Lavin 1992]: see below) to explore 

Piero della Francesca's monumental chapel in Arezzo. 

Examples 

1 Princeton Index of Christian Art, founded 1917. By 1990 it had some 800,000 file cards 

documenting over 25,000 different subjects, plus a cross-referenced photo file with 

nearly a quarter of a million images. Copies also in the Vatican, UCLA, Utrecht and 

Dumbarton Oaks. In 1990, with Mellon Foundation and Princeton funding, a 

computerized database was begun. 

2 The Marburger Index Inventory of Art in Germany is another long-lived venture 8 , 

which as Foto Marburg goes back to 1913, and collected photographs of art in Germany. 

Placed on microfiche in the 1980s, the Web version 9 currently offers access to some 

220,000 images. It offers a variety of iconographic catalogues ordered by ICONCLASS, 

as well as orderings according to function, form, material, techniques, and so on. 

3 The Witt Computer Index at the Courtauld Institute in London: controlled vocabulary, 

authority files, ICONCLASS for subject matter. 

4 The Census of Antique Works of Art Known to the Renaissance: beginning in 1946 in 

the photographic collection of the Warburg Institute, funding from the Getty Art History 

Information Program allowed a start to be made on its computerization; from 1981 

housed at both the Warburg and the Biblioteca Hertziana, Rome, a demonstra tion 

videodisk was pressed in 1988. Its home from 1995 has been the Kunstgeschichtlichen 

Seminar of the Humboldt University in Berlin. Cf. the conference Inaugurating a 

Scholarly Database: the Census of Antique Works of Art and Architecture Known to the 

Renaissance, Warburg Institute, March 19–21, 1992. 

Laserdisks 

One viable technology, which lost the race for watch-at-home films to the VCR, was the 

laserdisk or videodisk, which could hold about 40,000 still frames, was cheap to copy, 

and played to an ordinary TV monitor. What is more, it was a random-access device 

(simply punch in the desired frame number), and the images thereon did not fade. The 

images could be enregistered professionally, or a recorder (e.g., by Sony) bought and 

used. Although it could be operated standalone, the laserdisk player was easily linked to a 

computer, with the latter holding the text data controlling the display of images.

The 1980s therefore saw the production of several very ambitious laserdisk projects. 

Some of these were by Art History departments who laserdisked their holdings; little 

information is now available about these copyright-precarious ventures, and the results 

certainly did not get much circulated, so that economies of scale were unavailable. 

However, several museums and galleries also produced laserdisks of some of their 

holdings. High-quality projects included the Vatican Library videodisks, on sale from 

1993: the first three disks offered a total of 75,000 images, and it was estimated that the 

Latin MSS alone would occupy 400,000 images 10 . But the relatively high cost of the 

playing equipment, and their specialized market, meant that few such ventures were a 

financial (as opposed to a scholarly) success. Where laserdisks were useful was in 

supplementing the speed and storage deficiencies of computers; thus the first edition of 

Perseus had the software and text on a CD-ROM, with the images displayed on the linked 

laserdisk player. 

However, although laid down in digital format on the platter, the laserdisk is an analogue 

technology, and is really video, so that only one dimension of image is possible (NTSC 

and PAL being slightly different but neither offering more in modern digital parlance 

than a 300,000-pixel image). By comparing such images with what is possible today (up 

to about 5,000,000 pixels, or over 16 times that resolution), it is easy to see why the 

laserdisk has died. But it poses the dilemma: if laserdisks had a ten-year life, and even 

assuming today's support media will remain readable, will today's digital images soon 

seem puny, and need redigitizing? 

The decade 1984–94 therefore saw much activity as text databases began to be populated 

with images, at first by scanner or video. This was to be a long and difficult journey 

because of the huge number of records involved, the cost of digitizing, and worries about 

image resolution and longevity of the support (tapes, hard disks). 

3-D graphics 

Interest in graphics was not circumscribed by still images, because it seemed that the new 

power of computers and speedy networks could deliver much more interesting scenarios 

in the form of virtual reality. The construction in a computer of a graphical representation 

of the real world or an imaginary one was seen by some visionaries as offering much 

more attractive and educationally rich presentations. Why display St Peter's Square or the 

inside of the Louvre in flat images? Why not build them in the machine, and allow the 

user to zoom in, out, and around, accessing other resources (text, sound, video) with the 

click of a mouse? Was this not an effective way of allowing students to experience real 

environments it was impractical to visit? Package the presentations for the Web, using the 

Virtual Reality Modeling Language (VRML), and use them for teaching – although the 

flaw in the argument is that technologies developed for gaming do not necessarily meet 

exacting standards either of detail or of accuracy – of "realism." 

VRML models are difficult to prepare and expensive to construct; so that even if one 

convincing model were to be constructed, this would not help much in art history courses

dealing with tens of monuments. Nor are there any signs that more "intelligent" software 

will rescue what looks increasingly like a dead end (but not before many art historians, 

architects, and archaeologists had spent time on ambitious plans for a computer-generated 

version of the world). Of course, a continuing problem in anything to do with computers 

is hype versus reality; so that while we can all agree that three dimensions should be an 

improvement on two (cf. human sight), computer reconstructions of the real world (I 

exclude drawn reconstructions of archaeological sites – cf. titles such as Rediscovering 

Ancient Sites through New Technology – where such technologies probably do have a 

future) remain the triumph of hope over experience. Even if for the devotees of virtual 

reality we are "virtually there" (Lanier 2001), it is sensible to recognize the scale of the 

problem from data acquisition to processing and display 11 . Nevertheless, computer 

graphics, both raster and bitmapped, have a very large part to play in the study of art and 

architecture (cf. Stenvert 1991) 12 . We may even hope for the further development of 

autostereoscopic 3-D displays, which will allow the user to view the displayed scene 

without using stereo glasses 13 . 

CD-ROM and digital camera 

In the late 1980s and early 1990s, the generation of digital images for manipulation on 

standalone computers was done by the end user with a video camera, or by Kodak putting 

images onto their multi-resolution Photo-CD format. Two technologies that between 

1993 and 1998 moved from the high-price, low-volume specialist to the low-price, highvolume 

consumer category were the CD-ROM burner and the digital camera. CD-ROMs 

could be professionally pressed in the 1980s, but at high cost; and the first expensive and 

tricky "end user" burners appeared in 1994. For art history the burner and the camera go 

together (with their cousin the scanner), because the output of the latter is conveniently 

stored on the former; as images get larger, so more CD-ROM blanks are needed – hence 

the recent introduction of its child, the DVD burner, one version of which holds 4.2 GB, 

rather than the 700 MB of the conventional CD-ROM. 

By 1999, prices were reasonable for both items (about a tenfold drop in six years), and 

light and cheap portable cameras and burners helped scholars on research trips. Only now 

did the old argument of the art-historical computer-deniers that digital images were 

inferior to 35 mm slides come to grief. At last it became feasible and cheap to digitize 

sets of images for class use, although difficulties in the provision of such material to the 

students remained, as we shall see below. 

Examples 

1 To encourage involvement, the CIHA Conference in 2000 organized a competition for 

"the best digital productions in Art History." 

2 The Deutsche Forschungsgemeinschaft supports an initiative of the Art History 

Department of the Humboldt University intended to build up an international archive of 

Virtual Art (see Grau website).

The Internet, Web, and Communication 

In the mid-1980s, micros communicated little better than mainframes. For museums and 

galleries with data, how to interchange them? By tape, certainly; but would the tape from 

machine A fit machine B and, if so, could the data thereon be read with or without the 

program with which they were written? Were two sets of data compatible to the extent 

that they could be merged, and thereby fulfill a main reason for communication? In 

general, dog-in-the-manger attitudes have kept data discrete; and it is ironic that the 

development of the Web has meant that anyone's data can be read in a web browser, 

thereby negating any urgency in actually sharing them. Idealists, who expected that the 

Internet and then the Web would mean access to artifacts beyond gallery boundaries, 

after the manner of the librarians' union catalogue, 14 have been largely disappointed. 

If the PC and graphical user interface held the promise of the easy manipulation of 

images and concomitant text by art historians, their communication across the Internet 

(Naughton 1999) in universally readable formats was lacking. The enthusiasm and effort 

in humanities computing may be measured by the 701 pages of the 1989–90 Humanities 

Computing Yearbook: A Comprehensive Guide to Software and Other Resources 15 . 

Although digitizing images was in hand by 1990 (see Durran 1992–3, for examples), the 

Web and its protocols and software, a European invention from CERN (Naughton 1999: 

230–9), was the breakthrough (in 1993/4), offering a consistent way of viewing data in 

various formats, and hyperlinking between them. The data could be served from the local 

machine or, more normally, pulled down from a server Out There somewhere on the 

Web. Text and still images would write to the browser quickly; sound and video would 

continue to be problematical on slower networks. For the first time, communication 

looked easy, in that any connected machine that could run the browser could get the data 

– no need to bother about incompatible operating systems any more. This cheap, easy, 

and attractively multimedia protocol saw the designers of earlier teaching and learning 

packages (most of which began with the penetration of the micro) eager to upgrade their 

work and reach a larger audience. 

Education 

Suddenly, with the Web, computing in an education context also looked practicable at a 

reasonable price. Some departments offered course images on the Web using Mosaic as 

early as 1994. Although useful websites grew only slowly, and access around the world 

also (spreading from the G8 outwards), educators could now contemplate the use of these 

learning materials, with teacher and students being in different locations, and the data 

being accessed asynchronously, even if they stipulated the survival of the campus-based 

university. 

The minutiae and problems of communication and cooperation bypassed many 

administrators, in government as well as in education, who since its introduction had seen 

the microcomputer as a cost-effective way of teaching without teachers. Typical of this 

period (and our own!) was an increasing demand for education and a reluctance to fund

personnel: machines are cheaper, do not get sick or sue, and can be written down for tax 

purposes. So computers came into education, sometimes as a requirement based on faith 

rather than need, but more often as cost-cutters, not as education-enhancers or facilitators. 

The consequences are with us today – usually as much hardware as anyone can use, but 

an inadequate level of personnel, and no understanding of the need for programmers to 

write humanities-specific programs. The current hope is to have computers search visual 

databases by subject, and have the software recognize and retrieve – all images 

containing a tree and a river. Such content-based retrieval might be realized: it is logical 

to wish for software be able to "recognize" real-world objects, and it would be interesting 

to see such a process demonstrated on a wide range of art historical data 16 . 

Examples 

1 Journals promoted education via computer. 17 

2 In the museum world, the Museum Computer Network played an important role, with 

its archives online since March 1999 (eSpectra website). 

3 The Computers in Teaching Initiative (CTI) project was begun in 1984 by the British 

Computer Board for Universities and Research Councils to help staff use computers in 

support of teaching. In 1989, 24 specialist centers were established, including a CTI 

Center for History, Archaeology, and Art History. This offered workshops, and published 

a Newsletter welcoming "reports on the use of computers for teaching or research … 

announcements of new courses, reports on running courses, news about projects, 

software, datasets and conferences, and letters." The mission of the CTI 

(), according to its publicity, was "to maintain and enhance the 

quality of learning and increase the effectiveness of teaching through the applica tion of 

appropriate learning technologies." The aim was to "enable change in higher education, 

because with computers teachers and learners can determine the place, time and pace for 

learning." Their refereed journal, Active Learning, emphasizes "learning outcomes rather 

than enabling technologies." In 1991, 314 packages for historians were listed (Spaeth 

1991). 

Art History and Computing Today 

The discipline continues to be a minnow in the humanities pond, and a relatively 

expensive one (all those slides and photographs, and glossy books), but books which in 

years past targeted a very broad audience now focus on art historians. 18 There are many 

projects and ventures in the area (cf. CNI and Düsseldorf), and several ventures (to add to 

the pioneering Marburg Index – see above) which offer an overview of national holdings, 

such as the Joconde database of works in 75 French museums, which joins a series of 

other French initiatives (see, for example, the Ministère de la culture website). These are 

important undertakings: the architecture database called Mérimée, for instance, has over 

150,000 records. It also derives from a base prepared in the 1980s: as early as 1985, the 

Ministère de la culture committed in hardware alone FF18.2 million to archaeology, 

FF10.8 million for laboratories, and FF10.3 million for the digitization of images and

sound (Ministère de la culture 1985). Why are not the governments or cultural 

organizations of other first-world, computer-rich countries doing likewise? 

But as in many other disciplines, moves toward the use of computers and the Web in 

teaching and learning have been left to the initiative of individual academics; some of 

these have been told sententiously that academics should leave such technical matters to 

programmers; others have seen their enterprise met by lack of interest, help, or adequate 

financing from the administration. On the other hand, institutions in Germany and 

Switzerland promote computers in research and conferences (see Hamburg University 

website), and in courses on computing in the discipline (see Universitat Trier website). A 

collaborative venture – Scbule des Sebens - offers web-based seminars, using Web-CT. 

And although the connectivity of computers should also mean the enthusiastic 

cooperation of academics across institutions, and a willingness to share data, in no 

country has any such organizational initiative been taken nationally, and international 

initiatives tend to address narrow research issues rather than to aim to facilitate teaching. 

"No man is an island", wrote John Donne; but then he had no experience of our 

supposedly connected world, and of the fears throughout the system. If I develop a webbased 

course, who owns it, the institution or me? If it works, am I still needed? If ArtAl 

exists as such a course, how many young lecturers will be needed, except to keep it 

updated? 

So although the technologies (including Web software) are clever and advanced, it is the 

human element that restricts obvious developments in the discipline. These (some of 

which are valid across other disciplines) include: acknowledgment that computer literacy 

is essential for all staff and students; rethinking of traditional modes of course delivery 

(which doesn't mean their complete replacement by the Web); gradual replacement of 

analogue slide collections by digital ones retrieved from some international database-inthe-sky; 

insistence that web-based learning should improve the quality of education, 

rather than lower the unit of resource for the administrators; planning for classrooms 

which encompass existing web-based technologies, including videoconferencing. 

Conclusion: The Future 

Although computing for art historians is considerably simpler now than it was ten or 

twenty years ago, many problems remain, and they still revolve around communication, 

expertise, and finance. 

Ease of communication, and a relative freedom from hackers as from the 90 percent or 

more of idiocy which overloads the net, requires the development of private or restricted 

networks which will allow the easy transmission and viewing of visual material of 

spectacularly high resolution. In many ways it is a pity that the Internet and the Web, 

both developed in universities or university-like institutions on public money, did not tie 

all the technologies up with patents (against the ideology, I know), and then recoup funds 

by making commerce pay hugely for their use. This would certainly have softened the 

funding crisis in higher education.

A growing desire of art historians to teach using the Web requires either the upgrading of 

useful but general-purpose course-construction programs such as Blackboard or WebCT, 

or the development of suitable programs usable by newly computer-savvy staff. 

Yet financial problems in a small discipline are certain to remain. If there are more than 

1,999 museums online worldwide (MCN website), funding prevents many teaching 

institutions from presenting their materials on the Web. This will happen gradually, as 35 

mm slides degrade and as digital cameras take over from analogue ones. Then the day 

will soon come when VR curators must be as computer-literate as staff in university 

libraries (now usually part of something like a Division of Scholarly Information), as the 

Visual Resources Association (VRA) (see above) has recognized for years. In other 

words, such personnel must capture the high ground of indispensability just as librarians 

have so successfully presented themselves as the natural high priests for the curating of 

digital as well as of paper-based information. So how will such a small discipline keep its 

head above water if it goes thoroughly digital? We already have a system where numbers 

count instead of quality (5 students taking Poussin: bad; 200 students taking Desire and 

the Body. good). In the nineteenth century academics were sometimes paid according to 

the number of students they attracted (plus ça change …); so can we envisage those 

academics who develop expensive web-based courses recouping the outlay by some kind 

of pay-per-use micro-money, since computers are good at such calculations? Since 

tourism is a crucial element in the economy, and since universities are increasingly 

commercial organizations, should art historians relocate themselves (as many museums 

have done) as part of the heritage industry (see 

), and produce cultural tourism 

courses for paying customers as spin-offs from their student-directed university work? 

The wind of commerce hit museums harder than many universities. In some instances 

necessity occasioned the creation of good websites as the public face of the institution 

(interest logged via hits per week); in others it provoked a rethink of the mission of 

museums and a dumbing down to a lower common denominator, in displays, special 

exhibitions, and websites. Museums are in a state of flux because of computers and the 

Web: can/should technology be applied to these dusty mausolea of out-of-context 

artifacts to provide a learned, multimedia, immersive context – the nearest thing now 

possible to reintegrating the works with the context from which they were removed? Or is 

the museum of the future to be a theme park, lacking any truly educational mission, with 

Web displays as part of the fun? Believers in culture should hope for the former; but, as 

the inscription on Wren's tomb nearly has it, Si monumentum requiris, cinumclicke. 

Indeed, should we extrapolate from the existence of the Web to the decline of campusbased 

universities and physical museums and galleries? If galleries can only display a 

small proportion of their collection at any one time, should not funding go into webbased 

image databases? And if seminars and tutorials should remain because everyone 

recognizes the human and pedagogic value of face-to-face sessions, the fixed-term, fixedplace 

lecture might disappear in the face of the twin pressures of web-based profusely 

illustrated "lectures", and the increasing inability of students working to pay for their 

education to attend them. The challenge is to ensure that web-based "lectures" are of a

quality equivalent to or higher than traditional ones. We might hope that the digital 

image, available well-catalogued and in profusion in a discipline for the development of 

which the photograph was partly responsible, will enhance art history's popularity and 

underline the worldwide importance of culture in an age where networks reach around 

the world. 

Notes 

1 Thus the Institut de Recherche et d'Histoire des Textes published Le Médiéviste et 

I'Ordinateur, issues 26/7 of which (Autumn 1992-Spring 1993) were devoted to digitized 

images, "source essentielle pour l'historien et la plupart du temps difficile à consulter." 

2 Cf. Peter Denley and Deian Hopkin's History and Computing (1987): the result of a 

conference in 1986 to inaugurate an association for historians who used or wished to use 

computers in their work, whether that work was research or teaching; or Jean-Philippe 

Genet's L!0rdinateur et le Métier d'Historien (1990), from the conference at Talence, 

September 1989. 

3 See for a tour d'horizon. 

4 Earlier, . 

5 Online at . Other Getty projects have 

included the Union List of Artist Names; Guide for the Description of Architectural 

Drawings and Archives; Anthology of Museum Automation; Bibliography of the History 

of Art (BHA); The Provenance Index; The Avery Index to Architectural Periodicals; Witt 

Computer Index; Census of Antique Art and Architecture Known to the Renaissance. 

6 The International Journal of Museum Management and Curatorship 8,1 (March 1989), 

is largely devoted to computerization; the False Gods of the Editorial's title refers to the 

process of intellectual cheapening, not computers. 

7 The SNS Centre di Elaborazione Automatica di Dati e Documenti Storico-Artistici 

produced a Bollettino d'Informazioni. Vol. 2 for 1981, for example, offered papers on 

computerization of a lexicon of goldsmiths' work; a report on a seminar to discuss 

digitization at the Bargello and Museo Stibbert in Florence, a catalogue of reused antique 

sarcophagi, and a report on the computerization of a Corpus dell'Arte Senese. For the 

later history of this organization, cf. . 

8 See . 

9 See . 

10 Details in Baryla (1992–3: 23–4). NB: these were accompanied by a database.

11 See Debevec's notes for SIGGRAPH 99 Course no. 28, and cf. 

; a course at SIGGRAPH 2002 was devoted to 

Recreating the Past: ; 

also Polleyes et al., Reconstruction Techniques with Applications in Archeology, at 

. 

12 Cf. Stenvert (1991), with its excellent bibliography. 

13 For example, or the list at 

. 

14 Cf. virtual library catalogues of art history, such as that at 

with the interface 

available in English, French, German, and Italian. 

15 See Lancashire (1991: 9–17). These pages are devoted to art history. This gives an 

indication of the relatively slow start of this image-based discipline vis-a-vis text-based 

ones. 

16 Romer, who had previously published a paper entitled A Keyword is Worth 1,000 

Images, correctly concluded that "image and multimedia databases are heavily dependent 

on the quality of their stored descriptions" (1996: 55). 

17 Such as the Journal of Educational Multimedia and Hypermedia, or the CTI's Active 

Learning, no. 2, for July 1995, having the theme Using the Internet for Teaching. 

18 See also Andrews and Greenhalgh (1987) and Richard and Tiedemann's Internet für 

Kunsthistoriker: Eine praxisorientierte Einführung (1999). 


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4. 

Classics and the Computer: An End of the History 

Greg Crane 

Many non-classicists from academia and beyond still express surprise that classicists 

have been aggressively integrating computerized tools into their field for a generation. 

The study of Greco-Roman antiquity is, however, a data-intensive enterprise. Classicists 

have for thousands of years been developing lexica, encyclopedias, commentaries, 

critical editions, and other elements of scholarly infrastructure that are best suited to an 

electronic environment. Classicists have placed great emphasis on systematic knowledge 

management and engineering. The adoption of electronic methods thus reflects a very old 

impulse within the field of classics. The paper knowledge base on Greco-Roman 

antiquity is immense and well organized; classicists, for example, established standard, 

persistent citations schemes for most major authors, thus allowing us to convert 

nineteenth-century reference tools into useful electronic databases. Classicists are thus 

well prepared to exploit emerging digital systems. For many classicists, electronic media 

are interesting not (only) because they are new and exciting but because they allow us to 

pursue more effectively intellectual avenues than had been feasible with paper. While 

many of us compare the impact of print and of new electronic media, classicists can see 

the impact of both revolutions upon the 2,500-year history of their field. Electronic media 

thus allow us to pursue both our deepest and most firmly established scholarly values and 

challenge us to rethink every aspect of our field. 

The title of this chapter alludes to a 1993 article, "Classics and the Computer: the 

History", in which Theodore Brunner, the founder of the Thesaurus Linguae Graecae 

(TLG), described the development of computing in the classics up to the end of the 

1980s. Those wishing an account of the first generation of computer-based work in 

classics will find the story well documented in Brunner's work. This chapter takes a 

broader, more schematic, and more tendentious approach. To some extent, this approach 

is reactive: the rise of the personal computer and, more recently, of the World Wide Web 

has diffused computation throughout the daily life of scholars, and especially of students. 

A Foucauldian scholar might compare the shift from a few very specialized projects and 

extremely expensive hardware to the ubiquitous e-mail viewers, web browsers, word

processing systems, bibliographic database systems, etc., to the shift from the spectacle of 

the sovereign to the diffused microphysics of modern power. New computer-based 

projects continue to emerge; the December 2002 Ancient Studies – New Technology 

conference at Rutgers hosted presentations on roughly thirty computer-based projects 

about the ancient world. Perhaps more significant, even superficially traditional 

publications distributed even now in traditional format (whether paper-only publication 

or as electronic files that mimic paper presentation) depend upon an infrastructure that is 

almost entirely electronic. Digital systems have quietly become the norm. A follow-on to 

Brunner's history would lead far beyond the growing but much more tractable body of 

work that Brunner confronted more than a decade ago. 

The title of this chapter reflects an argument as well as a defensive strategy. There should 

not be a history of classics and the computer, for the needs of classicists are simply not so 

distinctive as to warrant a separate "classical informatics." Disciplinary specialists 

learning the strengths and weaknesses have, in the author's experience, a strong tendency 

to exaggerate the extent to which their problems are unique and to call for a specialized, 

domain-specific infrastructure and approach. Our colleagues in the biological sciences 

have been able to establish bioinformatics as a vigorous new field – but the biologists can 

bring to bear thousands of times more resources than can classicists. A tiny field such as 

classics, operating at the margins of the humanities, cannot afford a distinctive and 

autonomous history of its own. For classicists to make successful use of information 

technology, they must insinuate themselves within larger groups, making allies of other 

disciplines and sharing infrastructure. For classicists, the question is not whether they can 

create a classical informatics but whether such broad rubrics as computational 

humanities, computing in the humanities, cultural informatics, and so on, are sufficient or 

whether they should more aggressively strive to situate themselves within an informatics 

that covers the academy as a whole. Even academic technology, strictly defined, may be 

too limiting, since the most revolutionary information technology for twenty-first-century 

philologists may well emerge from programs such as TIDES (Translingual Information 

Detection Extraction Summarization), supported by the US military. 

But much as some of us may struggle to minimize the future history of classical 

computing as a separate movement, classicists have had to forge their own history. Even 

in the best of futures, where classicists customize general tools and share a rich 

infrastructure with larger disciplines, classicists will have to struggle mightily for their 

voices to be heard so that emerging systems and standards meet their needs. This chapter 

seeks to explain why classicists needed to create this separate history in the past and to 

ground current trends in a larger historical context. It is relatively easy to pass judgments 

on the past, but in using the past to argue for trends in the future this chapter justifies any 

unfairness in its own perfect hindsight by exposing its own predictions for the future. 

The following broad movements – which overlap with one another and all continue to the 

present – provide one way of looking at the history of classical computing. First, 

beginning with Father Roberto Busa's concordance of Aquinas's Latin writing in the late 

1940s, standard machines were used to produce particular projects (especially 

concordances such as Packard's Livy Concordance). Second, beginning at roughly 1970,

large, fairly centralized efforts took shape that sought to address major issues of classical 

computing infrastructure. These projects included David Packard's Ibycus system, the 

TLG, the Database of Classical Bibliography, the Bryn Mawr Classical Review, the Duke 

Databank of Documentary Papyri, and the Perseus Project. Third, beginning in the mid- 

1980s, the rise of the personal computer distributed computing throughout classics and, 

indeed, the humanities. Fourth, beginning in the mid-1990s, the rise of the Web spurred a 

vast outpouring of smaller projects in classics as in other fields. 

The following section takes one particular point in time twenty years ago (1982–3). The 

selection reflects the situation that existed when the author of this piece began his own 

work, but focusing on this point in time provides a brief case study with the particular 

illustrating more general conditions. The subsequent section provides a schematic view of 

classical computing. The conclusion briefly suggests how trends rooted in the past may 

carry us forward into the future. 

Cincinnati 1983: Retrospectives and Prospectives 

In the American Philological Association convention in 1983, held at Cincinnati, a 

crowded session considered the merits of two very different approaches to classical 

computing. I was part of a group describing a Unix-based approach that we had begun 

developing at the Harvard Classics Department in the summer of 1982. David Packard, 

who was then, and remains today, arguably the most significant figure in classical 

computing, was prepared to explain the rationale for his own Ibycus system, which he 

had spent years developing from the ground up to serve the needs of classicists. Then a 

graduate student and very conscious of my junior position, I was very nervous – if there 

was a tradition of young entrepreneurial leadership in technology, I had certainly not 

encountered it as a classicist. The underlying issues remain interesting topics of debate: 

how far do we follow generic standards, and at what point do the benefits of 

specialization justify departures from broader practice? 

Looking back after twenty years, we could argue that the positions we espoused had 

prevailed. The Ibycus minicomputer system is long gone and its successor, the Ibycus 

Scholarly Computer (SC) PC system, is no longer under development (although a few 

venerable Ibycus SCs continue to serve dedicated users to this day). The benefits which 

my colleagues and I placed on standards have, to some extent, proven themselves: the 

10,000 lines of source code, written in the C programming language under Unix, which 

provided an efficient searching environment for Greek and other languages, still compiles 

and can run on any Unix system (including Linux and OS X) – it would not be possible to 

buy today a computer that was less powerful than the fastest systems to which we had 

access twenty years ago. The Thesaurus Linguae Graecae itself now uses a Unix server 

to provide the core string-searching operations on which both Packard and my group 

were working twenty years ago. 

Such a triumphalist retrospective upon the past would, however, be unjust and inaccurate. 

First, systems are only a means to an end. David Packard opened his presentation in 

December 1983 with an observation that put our discussions into perspective and that has

shaped my own decision making ever since. He observed that software and systems were 

ephemeral but that primary sources such as well structured, cleanly entered source texts 

were objects of enduring value. In fact, the TLG had released a core set of Greek authors 

in machine-readable form and our work concentrated above all on providing textsearching 

facilities for this collection. Word processing and typesetting were major 

advantages of the new technology and the decrease in per page typesetting costs helped 

justify the considerable expense of any systems – Unix, Ibycus, or other – at the time. 

The TLG had begun work more than a decade before in 1972. The TLG began creating its 

digital library of classical Greek source texts by using the standard tools available from 

the UC Irvine computer center, but the problems of entering, formatting, and verifying 

Greek texts were very different from those of the number-crunching experiments and 

administrative databases for which those tools were developed. Packard's Ibycus system 

provided an environment far more suited to their needs than anything else available. 

Packard had gone so far as to modify the microcode of the Hewlett Packard 

minicomputer to enhance the speed of text searching. He created an entire environment, 

from the operating system up through a high-level programming language, aimed initially 

at serving the needs of classicists. Decades later, the boldness and achievement of 

creating this system seems only greater to this observer. It would have been immensely 

more difficult for the TLG - and many other smaller projects (such as the Duke Databank 

of Documentary Papyri and Robert Kraft's Septuagint Project) – to have been as 

successful. Without the Ibycus environment, few departments would have been able to 

justify the use of computers in the 1970s or early 1980s. Nor might the field have been 

able to provide the National Endowment to the Humanities funders with the same level of 

support for the TLG. 

The early 1980s represented a tipping point, for at that time new systems were emerging 

that would provide inexpensive and, even more important, relatively stable platforms for 

long-term development. The Unix operating system, C programming language, and 

similar resources provided tools that were independent of any one commercial vendor 

and that have continued to evolve ever since. MS DOS and the IBM PC appeared in 1981 

– before most of our current undergraduates were born. The Macintosh (which has now 

built its current operating system on a Berkeley Unix base) appeared in 1984 – at the 

same time as many students who entered American universities as freshmen in 2002. 

New languages such as Java and Perl have emerged, and web browsers have provoked a 

substantially new model of cross-platform interaction, but tools developed under Unix 

twenty years ago can still run. They may be abandoned, but only if better tools have 

emerged – not because the systems in which they were created are gone and they need to 

be, at the least, reconstituted. 

But even if the early 1980s represented the beginning of a new phase in the evolution of 

digital technology, the gap between our needs as classicists and the infrastructure at hand 

remained immense. The labor needed to adapt existing infrastructure to our needs was not 

so daunting as that which faced David Packard in building the Ibycus, but it was 

substantial.

Two classes of problems faced us, and they continue to face us even now. The first, to 

which I will return, consisted of tangible problems that we needed to solve. The second, 

however, arose from the gap in understanding between those of us who were new to the 

technology and our technological colleagues who were innocent of classics. Because we 

were unable to communicate our real needs, we made serious initial errors, investing 

heavily in tools that seemed suitable but proved, on closer examination, to be 

fundamentally unable to do what we needed. While many of the problems that we faced 

then have resolved themselves, the general problem remains: the biggest government 

funders of academic technology are the National Institutes of Health and the National 

Science Foundation whose aggregate funding ($20 billion and $5 billion respectively) 

exceeds that of the National Endowment for the Humanities ($135 million requested for 

2003) by a factor of 185. The academic technology specialists in higher education surely 

devote at least 1 percent of their time to the humanities, but the staggering disparity in 

support – governmental and private – for science, technology, and medicine means that 

the humanities are trapped at the margins of decision making. If our needs require 

substantial added investment beyond those of our colleagues outside the humanities (not 

to mention classics in particular), we will have great difficulties. We must be proactive 

and influence the shape of information technology as early as possible, tirelessly 

exploring common ground with larger disciplines and taking responsibility for pointing 

out where our challenges do, in fact, overlap with those of our colleagues from beyond 

the humanities. 

Our lack of sophistication, which became clear when Harvard began its own computing 

project in the summer of 1982, had one advantage. If we had been more knowledgeable, 

the department probably would not have moved forward but would have waited for 

technology to advance further. Instead, the department invested so many resources that 

we could not easily pull back. In the end, our work on the specialized problems of 

classical typesetting helped lower the publication costs of Harvard Studies in Classical 

Philology and the Loeb Classical Library, thus providing a justification for the initial 

investment. But if the results were ultimately satisfactory, the process was deeply flawed, 

and we were profoundly lucky to enjoy as much success as we did. Many other projects 

in classics and throughout the academy have faced similar problems and not been so 

fortunate. 

Several months of intensive work gave us a clearer idea of the challenges that we faced in 

1982.1 offer the following as a representative survey to document the state of the art at 

the time. 

Computer power and storage 

In 1965, Gordon E. Moore observed that the number of transistors that could be stored 

per unit area had been doubling since the transistor was invented and he argued that the 

trend would continue for the foreseeable future. The pace has slowed a bit – density has 

doubled every year and a half- but the exponential change has continued. Thus, twenty 

years ago we felt ourselves able to control staggering computational resources and looked 

upon the previous years with satisfaction. Now, of course, the subsequent twenty years

have made our initial systems appear primitive. The Psychology Department's Digital 

Equipment Corporation PDB 11/44 on which we did our work had far less computational 

power than the smallest desktop machine now available, but served dozens of users 

typing in manuscripts or conducting experiments. For us as classicists, disk storage was a 

crucial issue. Modern disks allowed us to imagine keeping vast libraries – dozens of 

megabytes – of text online for casual searching and analysis. The Psychology lab at the 

time had two 80-megabyte hard drives, each the size of a small washing machine. The 

Harvard Classics Department needed more storage to mount the TLG and purchased the 

largest machine then generally available. The Control Data Corporation disk held 660 

megabytes – four times the storage of both disks already installed. It cost $34,000 

(including our educational discount) and had a service contract of $4,000 per year. The 

disk arrived in a crate that we had to pry open. We needed a special-purpose disk 

controller ($2,000). Worst of all, we had to write a software driver to mount this disk, 

hacking the source code to the Berkeley Unix system. It took months before we could 

exploit more than a tiny fraction of the disk storage on this heavy, loud, expensive device. 

Our colleagues in the Ibycus world chose different hardware (at the time, the largest 

Ibycus systems had, if I recall correctly, 400-megabyte drives) but the basic parameters 

were the same for all of us. Disk storage was cumbersome and expensive. As I write this, 

the smallest disk that I can find contains 20 gigabytes (30 times as much as our CDC 

behemoth) and costs $200 (150 times less). The price/performance ratio has thus 

increased by a factor of c.45,000. This does not even consider the fact that this 20gigabyte 

drive plugs directly into a computer without modification and that it fits in a 

notebook. 

Machines have grown so fast and inexpensive that it is perhaps difficult for most of us to 

imagine the extent to which hardware constrained the way we designed systems and thus 

the questions that we could pursue. The extraordinarily high cost of disk storage meant 

that Packard chose not to create indices for the TLG. All searches read through the texts 

from start to finish. Packard modified the microcode of the HP minicomputer to increase 

the search speed, thus providing another reason to build an entirely new operating 

system. Limitations on storage meant that digital images of any kind were impractical. 

The first TLG texts that we received at Harvard were in a compressed format that reduced 

storage by c.2Q percent. We had to write a program to decompress the files – the 

program was short but we had to write one ourselves as none existed, and this simply 

added to the overhead of working with the TLG documents. 

Greek display 

The graphical displays which we now take for granted were not in standard circulation. 

Displays were monochrome terminals that could cope with letters but not drawings, much 

less color. Even displaying textual data posed major technical barriers. The TLG had 

already developed an excellent ASCII encoding scheme for classical Greek and we had 

no scientific reason to use anything other than BETA Code, but our colleagues insisted 

that they needed to see fully accented classical Greek. We thus had to devote substantial 

energy to the font problem – some of us who worked on Greek fonts in the period still 

view "font" as the one most disturbing four-letter word in English.

To display Greek, we needed to use special terminals that could display customized 

character sets. We designed Greek fonts on graph paper, converted the dot patterns into 

hexadecimal codes, programmed the data on to chips and then physically inserted these 

chips into the displays. The monitors that we used cost $1,700 each and provided users 

with shared access to the overtaxed minicomputers in the psychology department. 

Networks 

The Internet was still tiny, connecting a few key research institutions over a relatively 

slow network. When we first began work in 1982, we had access to no machine-tomachine 

networking other than log-ins via dial-up modems. To provide our colleagues 

access to the machine in the William James building, we purchased massive spools with 

thousands of feet of twisted pair cable and then ran this cable through a network of steam 

tunnels and conduits to Widener Library and other buildings. We did have e-mail within 

our single machine but we only gained access to inter-machine e-mail when we joined a 

network called UUCP. At the time, the machine would effectively grind to a halt every 

time it processed mail. Nor was mail a very effective tool, since we knew very few 

people outside of our own group who had e-mail accounts. Simple file transfers were 

beyond us and the current Internet would have seemed outlandish: experience near the 

bleeding of technology tends to generate a schizophrenic attitude, alternating between the 

visionary and the cynical. 

Multilingual text editing 

David Packard developed an editor for the Ibycus that could manage Greek and English. 

When we first considered working with Unix our best consultant suggested that it would 

take twenty minutes to modify the source code for the standard Unix text editor (Vi) to 

handle Greek. In fact, the Unix text editor assumed an ASCII character set and would 

have required a complete rewrite to manage any other character sets. We spent a good 

deal of time trying to develop a multilingual text editor. We had made good progress 

when the Macintosh arrived. The Macintosh knew nothing about languages at the time, 

but it understood fonts, and multiple fonts were enough to serve the basic needs of 

classicists. We abandoned our editor and resolved never to address a general problem that 

the marketplace would solve for us. 

Text retrieval 

Pioneers such as Gerald Salton had laid the foundation for the science of information 

retrieval in the 1950s and 1960s. Techniques already existed to provide efficient 

searching of textual databases. The tools at our disposal were also powerful and flexible. 

Unix provided a superb scripting language and development environment within which to 

reuse existing programs. Unix provided several text-searching programs (the infamously 

non-mnemonic grep, egrep and fgrep). We had the source code for everything and could 

thus modify any existing Unix program. Searching the TLG and other scholarly textual 

databases should have been easy.

In fact, it proved quite difficult to build services that our colleagues would actually use on 

the generic tools of Unix. Three issues confronted us. First, we needed a reasonable 

interface. Few classicists even today have proven willing to learn how to use a Unix 

environment directly. Since we were working more than a decade before web browsers 

radically reduced the labor needed to create simple interfaces, this task required both 

programming and design work. 

Second, we needed to generate standard citations for the search results. The Unix search 

utilities returned lines that had a particular pattern. The TLG files had a complex scheme 

for encoding changes in book, chapter, section, or line numbers. The search program had 

to examine each line in a file and update the various registers, as well as deal with the 

exceptions (e.g., line 178a, line 38 appearing before line 34, etc.) that have found their 

way into our texts. Adding such routines to the fine-tuned text-scanning modules of the 

Unix search routines proved non-trivial and would have required virtual rewrites. 

Third, speed was an issue. The systems to which we had access were too slow. We 

learned quickly why David Packard had modified the microcode of this HP computer to 

increase linear search speeds. Ultimately, we were able to match the linear search speeds 

on the Ibycus on DEC VAX computers by rewriting the core search loop in VAX 

assembly language so that we could utilize a special pattern-matching language. Of 

course, the VAX computers were more expensive (and more powerful) than the HP 

computers. Also, while classics departments owned Ibycus computers, we shared all of 

our machines with many other users – and others did not appreciate our clogging the 

system with searches that slowed down the disks and the CPU alike. 

Fourth, searching classical Greek raises two problems. First, classical Greek has a 

complex system of accentuation, with the accent shifting around the word as inflections 

vary. Thus, searches need to be able to ignore accents and scan for underlying stems: e.g., 

searching for forms of the verb π µπω ("to send"), we need to match "' πεµπoν" and "π 

µπεις" We can write regular expressions to accommodate this or simply search for "πεµπ" 

or "π µπ" but such permutations can become complex and require knowledge of vowel 

length and other features of the language. More significantly, Greek is a highly inflected 

language. The search tools developed under Unix implicitly assumed English – with its 

minimal system of inflections – as its model. The tools at our disposal simply were not 

designed for a language in which a single verb can have a thousand different forms. 

Ultimately, we developed a multilingual full text retrieval system from scratch. The 

system used a set of inverted indices, added 50 percent to the storage needs of the TLG (a 

significant factor then), but provided almost instantaneous lookups. The system 

comprised more than 15,000 lines of code when completed and provided a reasonable 

TLG solution for a decade, finally yielding to personal computer-based programs to 

search the subsequent TLG CDs. 

From 1983 to 2003: Past Trends and Prospects

The details listed in the section above provide an insight into one particular point in time. 

The problems described above warrant documentation in part precisely because it is hard 

to remember now what barriers they posed. Looking back over the past twenty years, the 

following broad themes stand out. 

Increasingly powerful hardware 

Moore's law continues to hold. Even if technology were to freeze at 2003 levels, we 

would still need a generation to digest its implications. The cost of storing textual 

databases such as the TLG is almost zero. We can store hundreds of thousands of images, 

vast geographic datasets and anything that was published in print. 

Visualizations 

These include not only virtual reality displays and geographic information systems but 

also automatically generated timelines and other data visualization techniques. The result 

will be possibilities for more holistic analysis of the Greco-Roman world, with 

philologists making much more effective use of art and archaeological materials than 

before. 

Language technologies 

I single out the broad class of "language technologies", a rubric that includes machine 

translation, cross-lingual information retrieval (e.g., type in "guest friend" and locate 

passages in Greek, Latin, Arabic, Sanskrit, etc.), summarization, clustering, syntactic 

analysis (and the possibility of generating large syntactic databases for Greco-Roman 

source texts), etc. The US defense establishment is investing heavily in rapidly 

deployable tools for "low-density languages" (e.g., languages for which few if any 

computational resources exist) – intelligence analysts have found themselves compelled 

to develop capabilities in languages such as Albanian and Pashtun. The underlying 

resources for these techniques are bilingual text corpora, morphological analyzers, online 

lexica, grammars and other knowledge sources. Classicists already have these resources 

online, and other languages promise to follow suit. The next twenty years promise to 

introduce a golden age of philology, in which classicists not only explore new questions 

about Greek and Latin but also explore corpora in many languages which they will never 

have the opportunity to master. 

Annotation managers 

Classicists have a long tradition of standalone commentaries and small notes on 

individual words and passages. All major literary classical Greek source texts are 

available from the TLG and many from Perseus. Authors can already publish annotations 

directly linked to the passages that readers see (rather than buried in separate 

publications). The hypertex-tual nature of web reading is stimulating new tools and new 

opportunities with classicists, who can bring online a rich tradition of annotations.

Rise of library repositories 

The World Wide Web spurred a generation of pseudo-publication: documents more 

broadly available than any print publication in history could at any given time reach 

millions of machines. The same documents often ran, however, under individual 

accounts, with many URLs being changed or pointing to documents that were no longer 

online or, arguably worse, that had been substantively changed since the original link had 

been added. A variety of library repositories are now coming into use 1 . Their features 

differ but all are dedicated to providing long-term storage of core documents and to 

separating authors from preservation. In the world of publication, alienation is a virtue, 

because in alienating publications, the author can entrust them to libraries that are 

designed to provide stable access beyond the lifespan of any one individual. Unless we 

transfer stewardship – and control – of our work at some point, then our work will not 

outlive us. 

Convergence of needs 

The examples listed above reflect a common theme: as our computing infrastructure 

grows in power, the generality of the tools developed increases and the degree to which 

classicists (and other humanists) need to customize general tools becomes more defined. 

Where Packard had to create a whole operating system, we were, twenty years ago, able 

to build on Unix. Where we needed to work on our own multilingual text editor, Unicode 

provides multilingual support now at the system level. The set of problems particular to 

classicists is shrinking. We are better able now than ever before to share infrastructure 

with our colleagues not only in the humanities but in the rest of the academy as well. The 

rising NSF-sponsored National Science Digital Library (NSDL) will, if it is successful, 

probably establish a foundation for the integration of academic resources across the 

curriculum. Nevertheless, classicists need to reach out to their colleagues and to begin 

influencing projects such as the NSDL if these science-based efforts are to serve our 

needs in the future. Otherwise, we may find that simple steps that could radically improve 

our ability to work in the future will have been overlooked at crucial points in the coming 

years. Our history now lies with the larger story of computing and academia in the 

twenty-first century. 

Notes 

1 Among the best known are D-Space (at ) and FEDORA (at 

). 


Ancient Studies – New Technology, conference held at Rutgers University, December 

2002. At http://tabula.rutgers.edu/conferences/ancient_studies2002/. Accessed April 5, 

2004.

Brunner, T. F. (1993). Classics and the Computer: The History. In J. Solomon (ed.), 

Accessing Antiquity: The Computerization of Classical Databases (pp. 10–33). Tucson: 

University of Arizona Press. 

The Bryn Mawr Classical Review. Accessed April 5, 2004. At 

http://ccat.sas.upenn.edu/bmcr/. 

Busa, R. (1949). La terminologia tomistica dell'interiorita; saggi di metodo per 

un'interpretazione della metafisica della presenza (p. 279). Milano: Fratelli Bocca. 

The Database of Classical Bibliography. Accessed April 5, 2004. At 

http://www.library.usyd.edu.au/databases/deb.html. 

The Duke Databank of Documentary Papyri. Accessed April 5, 2004. At 

http://scriptorium.lib.duke.edu/papyrus/texts/DDBDP.html. 

Packard, D. W. (1968). A Concordance to Livy. Cambridge, MA: Harvard University 

Press. 

The Perseus Project. Accessed April 5, 2004. At http://www.perseus.tufts.edu. 

Thesaurus Linguae Graecae Project. Accessed April 5, 2004. At http://www.tlg.uci.edu/. 

TIDES (Translingual Information Detection Extraction Summarization). Accessed April 

5, 2004. At http://tides.nist.gov/. 

5. 

Computing and the Historical Imagination 

William G.Thomas, II 

In the late 1960s and early 1970s historians seemed to think that their profession, the craft 

and art of history itself, was on the brink of change. Everywhere one looked the signs 

were unmistakable. A kind of culture war broke out in the profession and a flurry of tense 

conference panels, public arguments, and roundtables took place with subtitles, such as 

"The Muse and Her Doctors" and "The New and the Old History." This culture war pitted 

the "new" history, largely influenced by social science theory and methodology, against 

the more traditional practices of narrative historians. The "new" historians used 

computers to make calculations and connections never before undertaken, and their 

results were, at times, breathtaking. Giddy with success, perhaps simply enthusiastic to 

the point of overconfidence, these historians saw little purpose in anyone offering 

resistance to their findings or their techniques. When challenged at a conference, more 

than one historian responded with nothing more than a mathematical equation as the 

answer. Computers and the techniques they made possible have over the years altered

how many historians have understood their craft. To some they have opened the historical 

imagination to new questions and forms of presentation, while to others they have instead 

shuttered the historical imagination, at best limiting and channeling historical thinking 

and at worst confining it to procedural, binary steps. This chapter traces where the 

historical profession has come in the years since these professional debates and tries to 

assess how computing technologies have affected the discipline and how they will shape 

its future scholarship. 

Not all historians in the 1960s and 1970s considered the computer the future of the 

profession. One railed against worshiping "at the shrine of that bitch goddess 

QUANTIFICATION." Another, Arthur Schlesinger, Jr., one of America's foremost 

historians, wrote in reply to the rising confidence in cliometrics and quantitative history, 

"Almost all important questions are important precisely because they are not susceptible 

to quantitative answers" (Swierenga 1970: 33). Other critics of quantitative methods took 

aim not at the methods these historians used but instead questioned whether the "armies" 

of graduate students needed to develop large-scale projects and the technicians to 

maintain them were worth the cost. One prominent British social historian, Lawrence 

Stone, disparaged the contributions of the new history and the costs it entailed: "It is just 

those projects that have been the most lavishly funded, the most ambitious in the 

assembly of vast quantities of data by armies of paid researchers, the most scientifically 

processed by the very latest in computer technology, the most mathematically 

sophisticated in presentation, which have so far turned out to be the most disappointing." 

These sentiments are still widely held in the discipline and at the heart of them lay 

fundamental disagreements over the practice and method of history. 

In the United States the greatest fight in the contest over computational methods in 

history took place in 1974 with the publication of Robert Fogel and Stanley Engerman's 

Time on the Cross: The Economics of American Negro Slavery. The book was a 

magisterial work of quantitative methodology and historical analysis, and it came in two 

volumes, one of which was dedicated entirely to quantitative methods and data. Fogel 

and Engerman admitted in their prologue that the book "will be a disturbing one to read." 

They also admitted that cliometrics, or quantitative history, had limitations and that 

"there is no such thing as errorless data" (Fogel and Engerman 1974: 8, 10). Fogel and 

Engerman concentrated their study on some very hotly contested issues: the economic 

profitability of slavery, the economic success of the South in the years before the Civil 

War, and the relative productivity of slave and free agriculture. 

Time on the Cross received extensive criticism for two main reasons. First, the book 

rested so much of its key findings and interpretations on purely quantitative analysis and 

addressed purely economic questions, largely ignoring the textual and qualitative analysis 

of other historians as well as the social and political context of slavery. Second, the book 

addressed one of the most explosive and challenging subjects in American history – 

slavery. It seemed everyone was interested in the subject. Fogel and Engerman appeared 

on national television and in Time magazine, and were reviewed in nearly every 

newspaper and magazine. The publication of the methods and evidence volume alone 

was enough to intimidate many scholars.

C. Vann Woodward, a distinguished historian of the American South, in an early review 

of the book was understanding of the authors but clearly concerned about where their 

computers had led (or misled) them. He noted that "the rattle of electronic equipment is 

heard off stage, and the reader is coerced by references to Vast research effort involving 

thousands of man and computer hours' and inconceivable mountains of statistical data." 

Woodward let it be known what the stakes were: "The object of the attack is the entire 

'traditional' interpretation of the slave economy." Woodward could hardly believe the line 

of reasoning that led Fogel and Engerman to assert that on average only "'2 percent of the 

value of income produced by slaves was expropriated by their masters,' and that this falls 

well within modern rates of expropriation." It was one of many disturbingly cold and 

skeptically received findings that the cliometricians put forward. Still, Woodward 

concluded that "it would be a great pity" if the controversy enflamed by Fogel and 

Engerman's conclusions were to "discredit their approach and obscure the genuine merits 

of their contribution" (Woodward 1974). 

Other reviewers were unwilling to concede any ground to the cliometricians. Withering 

criticism began shortly after the book was published, and it was led by Herbert Gutman, 

whose Slavery and the Numbers Game (1975) put forward the most devastating attack. 

As if to mock the cliometricians' elaborate reliance on numerical analysis and technical 

jargon, Gutman consistently referred to the book as "T/C" and to the authors as "F + E." 

Gutman's blood boiled when Fogel and Engerman icily concluded about the frequency of 

slave sales that "most slave sales were either of whole families or of individuals who 

were at an age when it would have been normal for them to have left the family." Gutman 

focused not on the statistical accuracy or the techniques of the cliometricians, but on the 

implications of their assumptions, the quality of their evidence, and how they used 

evidence. "Computers are helpful", Gutman pointed out, but not necessary for 

understanding the assumptions behind the statistical evidence (Gutman 1975: 3). Much of 

his critique faulted Fogel and Engerman's model of analysis for making almost no room 

for enslaved persons' agency, and instead making consistent and deeply embedded 

assumptions that everything in the enslaved person's life was directly subject to control 

and action by the slave holder. Gutman suggested that Fogel and Engerman's 

methodological, statistical, and interpretative errors all consistently aligned to produce a 

deeply flawed book, one that depicted the plantation South and slavery in a benign, even 

successful, light. In the end, the Fogel and Engerman finding that the typical enslaved 

person received less than one whipping per year (0.7, to be exact) helped drive home 

Gutman's critique. The number was presented as relatively minimal, as if such a thing 

could be counted and its effect established through quantification. Gutman pointed out 

that the really important measure was to account for the whip as an instrument of "social 

and economic discipline." The whipping data for Time on the Cross came from one 

plantation, and when Gutman re-examined the data he found that "a slave – 'on average' – 

was whipped every 4.56 days" (1975: 19). 

After Time on the Cross, advocates of numerical analysis and computing technology 

found themselves on the defensive. But this was not always the case. At the end of World 

War II in 1945 Vannevar Bush, the Director of the Office of Scientific Research and 

Development and one of the United States' leading scientists, tried to envision how the

advances in science during the war might be most usefully directed. In an Atlantic 

Monthly essay, titled "As We May Think", Bush turned to examples from historical 

inquiry to make his key point – technology might be turned to the possibilities for 

handling the growing mass of scientific and humanistic data. The problem Bush 

described seems only more pressing now: "The investigator is staggered by the findings 

and conclusions of thousands of other workers – conclusions which he cannot find the 

time to grasp, much less remember, as they appear. Yet specialization becomes 

increasingly necessary for progress, and the effort to bridge between disciplines is 

correspondingly superficial" (Bush 1945). 

Bush's vision was for a machine he called "the memex", a strikingly prescient description 

of a networked desktop computer. The machine would enable a scholar to map what 

Bush called a "trail" through the massive and growing scholarly record of evidence, data, 

interpretation, and narrative. He wanted machines to make the same kinds of connections, 

indeed to emulate the human mind with its fantastic power of association and linkage. 

Bush's principal examples for the memex's applications were spun out of history – a 

research investigating the origins of the Turkish longbow – and he considered historians 

strong candidates to become "a new profession of trail blazers, those who find delight in 

the task of establishing useful trails through the enormous mass of the common record." 

As historians went about their noble work, Bush thought, they would leave nothing 

hidden from view, instead producing scholarship that was intricately connected in ways 

that could be accessed, replicated, and extended: "The inheritance from the master 

becomes, not only his additions to the world's record, but for his disciples the entire 

scaffolding by which they were erected." 

Some American historians were already working in the ways Bush described. They were 

using Hollerith (IBM) punchcards and undertaking large-scale analysis of a wide array of 

records. Frank Owsley and Harriet Owsley in their landmark 1949 study of the Old South 

used statistical methods to make linkages across disparate federal census records and to 

create new quantitative datasets to analyze the class structure of the region. Frank Owsley 

and a large number of his graduate students worked on thousands of manuscript census 

returns for several counties from Alabama, Mississippi, Georgia, and Tennessee, and 

linked record-by-record the individuals and households in the population, slave owners, 

and agricultural schedules. Owsley's work challenged the idea that the planter elite 

dominated the South and instead suggested that a plain folk democracy characterized the 

region. The questions Owsley asked demanded methods of computational linking and 

quantitative analysis. Other historians in the 1940s and 1950s also worked on large 

statistical projects, including Merl Curtis analysis of the American frontier and William 

O. Aydelotte's study of the British Parliament in the 1840s. 

In retrospect, we can see three distinct phases in the ways historians have used computing 

technologies. Owsley, Curti, Aydelotte, and a few others in the 1940s were part of the 

first phase of quantitative history. These historians used mathematical techniques and 

built large datasets. A second phase began in the early 1960s and was associated with an 

emerging field of social science history. The "new" social, economic, and political history 

concentrated on mobility, political affiliation, urbanization, patterns of assimilation, and

legislative behavior. It included historians using a range of statistical techniques, such as 

Lee Benson and Allan Bogue of the so-called "Iowa school" of quantification, as well as 

Olivier Zunz, Michael F. Holt, Steven Thernstrom, J. Morgan Kousser, and many others. 

Many "new" social, political, and economic historians drew on the massive data collected 

in the Inter-University Consortium for Political and Social Research (ICPSR) which was 

founded at the University of Michigan in 1962 with support from the American Historical 

Association and the American Political Science Association to collect and make available 

historical data on county elections, census data, congressional roll call votes, and other 

miscellaneous political files. The computer made possible, or at least more practical and 

compelling, the study of history "from the bottom up." Political historians examined the 

influences at play in voting, not just the rhetoric of a few leaders; social historians found 

patterns to describe the world of average people; and economic historians developed 

models to account for multiple variables of causation. 

The technology was alluring, but historians worried about the costs of the research and 

the time and training it took. Robert P. Swierenga, a historian with considerable 

experience in quantitative methods, confessed in a 1974 article that "to compute the 

Yules Q coefficient statistic for congressional roll call analysis" might require five hours 

of mainframe computer time to run the analysis, not to mention the time and cost of 

coding the 100,000 data cards needed for the computation. Swierenga was skeptical that 

"desk consoles" would solve the problem because historians' data were simply too large 

for the machines of the foreseeable future (Swierenga 1974: 1068). 

The PC revolution and the rise of the Internet, along with the Moore's law exponential 

increase in speed and disk capacity of computing technology, led to the third phase. In 

this current phase, the networking capacity of the Internet offers the greatest 

opportunities and the most challenges for historians. At the same time as the Internet has 

created a vast network of systems and data, personal computers and software have 

advanced so far that nearly every historian uses information technologies in their daily 

work. 

Some significant methodological issues have emerged with the rise of PCs and "off the 

shelf" software. Manfred Thaller, an experienced quantitative historian, argued in 1987 

that database programs suited for businesses simply did not work for historians, who 

continually face "fuzzy" or incomplete data. The most obvious limitation that historians 

encounter with commercial database programs is the date function, since most programs 

cannot interpret or query nineteenth-century or earlier dates and a nest of problems in 

date functionality accompany any attempt to use historical dating in these programs. The 

nature of historical sources and the complexity of historical relationships led Thaller and 

others to challenge the easy comfort historians have with these programs. Thaller has 

been a voice crying in the wilderness, as historians in the 1980s and 1990s snapped up 

commercial database and spreadsheet programs with little hesitation and bent them to 

their needs. Meanwhile, Thaller and other historians at Gottingen developed a software 

system, KLEIO, for the needs of historical researchers and scholars. KLEIO's data 

processing system differs from standard packages because of its data structure. The 

software allows for three forms of related information: documents, groups, and elements.

KLEIO handles a wide range of "fuzzy" data, historical dating systems, and types of 

documents (see Denley and Hopkin 1987: 149; Harvey and Press 1996: 190–3). 

Throughout the 1980s historians, especially in Europe, refined the use of databases for 

historical computing. In the process they questioned how these tools affected their 

interpretations and whether the relational database models simply could not account for 

non-tabular data such as long texts, sound, images, and maps. Manfred Thaller's system 

tried to separate what he called the "knowledge environment" from the system 

environment or software, so that ideally the meaning of the records in a database 

remained independent of its source information. Database design for historical computing 

led Thaller and other quantitative historians to make distinctions between designs that 

build around the method of analysis or model from those which build around the source. 

In the former, historians extract information out of sources from the data's original 

context into a set of well-defined tables arranged to allow for predetermined queries. In 

the latter, historians concentrate on capturing the original text or data and its entire source 

context, only later making decisions about analysis and organization of the data. Thaller's 

purist position on the use of databases in historical computing has gained attention in the 

field and sharpened the debate, but it has not slowed down the widespread use of 

commercial software for historical analysis. Most historians have taken a far more 

pragmatic approach. 

Despite the fundamental changes in information technologies and in historians' use of 

them, the recovery from the "battle royale" over Time on the Cross has been slow for 

those American historians working with computers. Manfred Thaller and the debates over 

database methodology and theory remained well out of the mainstream of American 

history in the 1980s. Twenty years after Time on the Cross, when Daniel Greenstein 

published A Historian's Guide to Computing (1994), he felt compelled to convince his 

readers that computers should not be "tarred with the same brush as the social science 

historians" (1994: 1). His treatment of regression analysis amounted to less than a page, 

while e-mail, bibliographic software, and note-capturing programs took up more than a 

chapter. Similarly, Evan Mawdsley and Thomas Munck, in their Computing for 

Historians: An Introductory Guide (1993), cautioned that they were "not champions of 

'cliometrics', 'quantification', the 'new' history, 'scientific history', or even what is called 

'social science history'" The idea that some historians pushed the profession to use 

computers and quantification techniques struck these authors as "the tail wagging the 

dog" (Mawdsley and Munck 1993: xiii). J. Morgan Kousser, in his review of social 

science history in the 1980s, admitted that most historians were moving abruptly away 

from quantitative methods. Time and distance tempered some critics, and by 1998 Joyce 

Appleby in her presidential address to the American Historical Association remarked that 

the quantifying social historians had "immediate, substantive, conceptual, and ideological 

effects" on the profession. Appleby considered them responsible for an important shift in 

the practice of history from unexplained to "explicit" assumptions about research 

methodology and from descriptive to analytical narrative (Appleby 1998: 4). 

British historians, however, have established a long and comparatively polite respect for 

historical computing. The Association for History and Computing (AHC) was founded at

Westfield College, University of London, in 1986, and has sponsored several large 

conferences and published proceedings. At the 1988 meeting, Charles Harvey speculated 

on the reasons for the strong interest in AHC among British historians. Chief among 

them, according to Harvey, is the "strength of support for empirical, scientific, 

developmental history in Britain" (Mawdsley et al. 1990: 206). Harvey described the 

British historical profession as focused on process not outcomes, rooted in scientific 

inquiry and only marginally concerned with theoretical debates. British historical 

scholarship was characterized by a focus on the importance of primary sources, the 

application of scientific notions in historical analysis, an emphasis on the specialized 

skills of the profession, the wide gulf between historians and social scientists, and the 

emphasis on applied skills and research methods in teaching students. Few of these 

characteristics describe the American historical profession and this may explain the 

thriving interest in "history and computing" in England. 

Despite the success of and interest in AHC, some British historians have viewed 

computing technology variously as the handmaiden of postmodernism, as a witless 

accomplice in the collapse of narrative, and as the silent killer of history's obligation to 

truth and objectivity. One recent British historian argued: "The declining importance of 

the so-called grand narratives of national and class histories, and the fragmentation and 

loss of cultural authority of scholarly history in the face of increasingly diffuse popular 

and political uses of 'history,' cannot be separated from the impact of the new 

technologies" (Morris 1995: 503). Another suggested that when a historian "embarks on a 

statistical analysis he crosses a kind of personal Rubicon" (Swierenga 1974: 1062). 

Across that divide, in this view, the historian finds restrictions imposed by the software 

that defy the historian's allegiance to basic craft and adherence to evidence. 

If Time on the Cross soured many American historians on computational methods, the 

Philadelphia Social History Project helped sustain them for a time. The project began in 

the 1970s under the leadership of Theodore Hershberg and took over a decade to 

complete. At first, skeptical critics considered it a painful example of the tail wagging the 

dog, long on money, time, and detail and short on results. Hershberg and his colleagues at 

the University of Pennsylvania used the computer to make linkages between a vast array 

of historical data and set out to establish guidelines for large-scale historical relational 

databases. The project took in nearly $2 million in grants and, in the process, offered 

historians a model for large-scale research and interdisciplinary activity. Numerous 

dissertations and theses drew on its data collection, and great expectations for the project 

were widely claimed. Still, the project ended with what many considered a thud – the 

publication in 1981 of a book of essays by sixteen historians working with the 

Philadelphia Social History Project that offered no larger synthesis for urban history. 

Critics faulted the project and its authors for over-quantification, for paying too much 

attention to cultivating an interdisciplinary "research culture", and for allowing federal 

dollars to push a research agenda about "public policy" matters removed from history. 

While historians continued to ponder the pros and cons of quantitative methods and while 

the profession increasingly turned to cultural studies, or took the "linguistic turn", as 

some have called the move toward the textual and French theory, computer scientists

were hammering out a common language for shared files over the Internet. The World 

Wide Web's opening in 1993 with the creation of HTML and browser technologies 

offered historians a new medium in which to present their work. One of the first 

historians to understand the implications of the Web for scholarship, teaching, and 

historical study generally was Edward L. Ayers at the University of Virginia. He was 

already embarking on a computer-aided analysis of two communities in the American 

Civil War when he first saw the Mosaic (TM) browser and the Web in operation at the 

Institute for Advanced Technology in the Humanities at the University of Virginia. Ayers 

immediately discarded his initial plans to distribute his research project on tape to 

libraries and instead shifted the direction entirely to the World Wide Web. As a result, 

Ayers's Valley of the Shadow Project was one of the earliest sites on the World Wide 

Web and perhaps the first work of historical scholarship on it. 

From its inception the Valley of the Shadow Project was more than met the eye. The 

general public understood it as a set of Civil War letters, records, and other accounts. 

Students and teachers praised it for opening up the past to them and allowing everyone 

"to be their own historian." Scholars stood initially aloof, wondering what could possibly 

be so exciting about an electronic archive. Gradually historians began to pay attention to 

the Web and to the Valley of the Shadow Project in particular. Some historians saw the 

Project as perhaps a potentially troublesome upstart that threatened to change the 

narrative guideposts laid down in other media. James M. McPherson, one of the leading 

historians of the Civil War period whose work had done so much to influence Ken 

Burns's The Civil War, considered the Project potentially too narrow and argued in a 

major conference panel on the Valley Project that the communities Ayers had chosen 

were not representative of the North and South. McPherson, and other critics as well, 

were beginning to recognize that the digital medium allowed Ayers to create a thoroughly 

captivating, technically savvy, and wholly unexpected comparative approach to the Civil 

War, one so complex and interconnected that such a thing seemed impossible in more 

linear media such as film and books. 

While Ayers was getting started on the Valley of the Shadow Project, another group of 

historians was already producing an electronic textbook for the American history survey 

course. The American Social History Project, based at City University of New York, 

included Roy Rosenzweig, Steve Brier, and Joshua Brown. Their Who Built America?, a 

CD-ROM of film, text, audio, images, and maps, aggressively and successfully placed 

social history, especially labor history, at the center of the national story. The CD-ROM 

won a major prize from the American Historical Association in 1994 and one reviewer 

called it a "massive tour-de-force, setting the standard for historians who aim to make 

their work accessible to broad audiences via multimedia" (Darien 1998). Other reviewers, 

for N-Net, speculated that Who Built America? was part of a larger trend toward CD- 

ROMs and multimedia history, an "experiment" that would undoubtedly inspire others 

and lead to new forms of scholarship and teaching (Frost and Saillant 1994). These 

reviewers, though, also listed the daunting system requirements to run the CD-ROM: 

"Macintosh computer running system 6.0.7 or higher; 4 megabytes of installed RAM in 

System 6 or 5 megabytes in System 7, with a minimum of 3–5 megabytes to allocate to

HyperCard; hard disk with 7 megabytes of free space, or 8 if QuickTime and HyperCard 

must be installed; 13-inch color monitor; QuickTime-ready CD-ROM drive." 

It turns out that Edward Ayers's early decision to produce the Valley of the Shadow 

Project for the World Wide Web was one of the keys to that Project's long-term success. 

While the team at the University of Virginia working with Ayers has produced CD- 

ROMs and Ayers himself is publishing a narrative book out of the electronic archive, it is 

the website that has reached millions of users and to which all of the other scholarly 

objects point. The CD-ROMs of the Valley of the Shadow and Who Built America? 

contain remarkable materials, but their self-contained systems and off-the-network 

approach hobbled them, and by the late 1990s the CD-ROM seemed a relic in the fastmoving 

technology marketplace. The World Wide Web offered connectivity and 

hypertext on a scale that the public demanded and that scholars were beginning to see as 

immensely advantageous. "Historians might begin to take advantage of the new media", 

Ayers wrote, "by trying to imagine forms of narrative on paper that convey the 

complexity we see in the digital archives." In his call for a "hypertext history", Ayers 

admitted that while the technology offers grand possibilities, even with the crude tools 

presently in use, there are significant barriers for historians. Ayers called hypertext 

history potentially a "culmination of a long-held desire to present a more 

multidimensional history and a threat to standard practice" (Ayers 1999). 

All of the connectivity and digitization has opened up history and historical sources in 

unprecedented ways, yet the technology has not come without tensions, costs, and 

unexpected sets of alliances and demands for historians, educators, administrators, and 

the public. The opportunities of digital technology for history notwithstanding, Roy 

Rosenzweig, one of the leading scholars of the Who Built America? CD-ROM, and 

Michael O'Malley questioned whether professional historians can "compete with 

commercial operations" (Rosenzweig and O'Malley 1997: 152). Permission to pay for 

copyright, the costs of design and graphical layout, maintaining programming 

technologies and software all conspire to favor commercial publishing companies rather 

than professional historians. More recently, Rosenzweig has cautioned historians about 

the prospects of writing history in a world of information overload. "Historians, in fact, 

may be facing a fundamental paradigm shift from a culture of scarcity to a culture of 

abundance", Rosenzweig observed, while at the same time archivists and librarians warn 

that vast electronic records are being lost every day in government, business, and 

academic institutions, not to mention in homes, churches, schools, and nonprofits. 

Problems of "authenticity", Rosenzweig pointed out, plague digital preservation, and 

libraries and archives face skyrocketing costs and difficult choices. But the historians 

face equally demanding problems. "The historical narratives that future historians write 

may not actually look much different from those that are crafted today", according to 

Rosenzweig, "but the methodologies they use may need to change radically" 

(Rosenzweig 2003). The vast size of born digital electronic data collections and the 

interrelationships among these data present historians with a fundamental methodological 

issue, according to Rosenzweig. They will need tools and methods, perhaps borrowed 

from the tradition of social science history, to make sense of these records.

If historians face an unprecedented scale of information, they also encounter in the digital 

medium an unexpectedly versatile mode of presenting their work. Many historians are 

beginning to ask what can we expect historical scholarship to look like in the networked 

electronic medium of the Internet and what forms of historical narrative might be 

enhanced or enabled. Robert Darnton, past president of the American Historical 

Association, National Book Award finalist, and innovator in historical forms of 

scholarship, sketched out his ideas for the future of electronic publishing in a 1999 New 

York Review of Books essay, titled "The New Age of the Book." He was concerned in 

large part with the future of the book, especially the academic monograph, and the 

university presses that produce and publish them. Darnton considered history to be "a 

discipline where the crisis in scholarly publishing is particularly acute" (Darnton 1999). 

Books won prizes and sold fewer than 200 copies; academic presses struggled to stay out 

of the red, and authors, especially young scholars before tenure, frantically tried to 

publish their work. Darnton used Middle East Studies as his example of "an endangered 

field of scholarship" about which the public cared and thought little. Only a few years 

after Darnton's review article, the importance of Middle East Studies could hardly be 

argued, as the events of September 11, 2001, brought the field to the immediate attention 

of the American public. Darnton observed that the unpredictability of the market and the 

pressures on presses, tenure committees, and scholars seemed to conspire against the 

future of the academic book. 

Darnton asked whether electronic publishing "could provide a solution." He outlined 

significant advantages for the field of history where connections among evidence were so 

eye-opening during research in the archive and often so difficult to reproduce in 

narrative. Darnton wished for a new form for historical scholarship: "If only I could show 

how themes crisscross outside my narrative and extend far beyond the boundaries of my 

book … instead of using an argument to close a case, they could open up new ways of 

making sense of the evidence, new possibilities of making available the raw material 

embedded in the story, a new consciousness of the complexities involved in construing 

the past" (Darnton 1999). Darnton cautioned against "bloating" the book and piling on 

appendages to narrative. Instead, he called for a trim pyramid of layers: summary at the 

top and documentation, evidence, commentary, and other materials below. 

It is not yet clear how digital technology will affect the practice of history or whether 

historians will heed Darnton's call to consider the advantages of electronic publication. In 

a show of leadership Darnton offered historians an example of what new electronic 

scholarship might look like, publishing a dynamic essay in the American Historical 

Review (Darnton 2000). In recent years several historians are working toward common 

ends. Philip Ethington has written an electronic essay and presentation for the Web on the 

urban history of Los Angeles. Ethington "explores the hypothesis that the key concept in 

the search for historical certainty should be 'mapping' in a literal, not a metaphoric, sense" 

(Ethington 2000). His work includes a wide range of media and sources to create, or 

rather recreate, the "panorama" of the city. Ethington suggests that the website can be 

read like a newspaper, inviting readers to wander through it, skipping from section to 

section, and focusing on what strikes their interest. Motivated "simultaneously by two 

ongoing debates: one among historians about 'objective knowledge,' and another among

urbanists about the depthless postmodern condition", Ethington's electronic scholarship 

grasps the "archetype of 'hyperspace'" to address these concerns. 

Finally, Ayers and this author are working on an American Historical Review electronic 

article, titled "The Differences Slavery Made: A Close Analysis of Two American 

Communities." This piece of electronic scholarship operates on several levels to connect 

form and analysis. First, it allows one to reconstruct the process by which our argument 

was arrived at, to "follow the logic" of our thinking, in effect to reconstruct the kind of 

"trails" that Vannevar Bush expected the technology to allow historians. This electronic 

scholarship also uses spatial analysis and spatial presentation to locate its subjects and its 

readers within the context of the historical evidence and interpretation. And it presents 

itself in a form that allows for unforeseen connections with future scholarship. 

Historians will increasingly use and rely on "born digital" objects for evidence, analysis, 

and reference, as libraries and other government agencies increasingly digitize, catalogue, 

and make accessible historical materials (see Rosenzweig 2003). Some of these materials 

are hypertextual maps, others are annotated letters, edited video, oral histories, or 

relational databases. These digital objects vary widely according to their origin, format, 

and purpose. A born digital object is one created expressly for and in the digital medium, 

and therefore is more than a digital replication of an analogue object. For these objects, 

such as a reply to an email message, there is no complete analogue surrogate and as a 

consequence historians will need to understand not only what these objects explain but 

also how they were created. The electronic text, for example, marked up in Text 

Encoding Initiative (TEI) language becomes transformed in the process of both 

digitization and markup. Its unique markup scheme, as well as the software and hardware 

at both the server and client ends, affect how the text behaves and how its readers 

encounter it. Literary scholars, such as Espen Aarseth for example, have widely discussed 

the nature of narrative in cyberspace, and Aarseth calls cybertexts "ergotic" to distinguish 

them as non-linear, dynamic, explorative, configurative narratives (Aarseth 1997: 62). 

For historians the first stage in such textual developments for narrative have already been 

expressed in a wide range of digital archives. While these archives might appear for 

many users as undifferentiated collections of evidence, they represent something much 

more interpreted. Digital archives are often themselves an interpretative model open for 

reading and inquiry, and the objects within them, whether marked-up texts or hypermedia 

maps, derive from a complex series of authored stages. What Jerome McGann called 

"radiant textuality", the dynamic multi-layered expressions that digital technologies 

enable, applies to huge edited digital texts as well as to discrete objects within larger 

electronic archives (see McGann 2001: for example 151–2, 206–7). 

The step from these archives to second-order historical interpretation necessarily involves 

the incorporation and explication of born digital objects, and "The Differences Slavery 

Made" offers an example of scholarship that emerges from and in relationship to born 

digital scholarly objects. The work fuses interpretative argument with the digital 

resources of the Valley of the Shadow Project, and it is designed for a future of 

networked scholarship in which interpretation, narrative, evidence, commentary, and 

other scholarly activities will interconnect. The resulting piece is intended to fuse form

and argument in the digital medium. The authors propose a prismatic model as an 

alternative to Darnton's pyramid structure, one that allows readers to explore angles of 

interpretation on the same evidentiary and historiographical background. The prismatic 

functionality of the article offers to open the process of historical interpretation to the 

reader, providing sequential and interrelated nodes of analysis, evidence, and their 

relationship to previous scholarship. As important, the article tries to use its form as an 

explication of its argument and subject. Slavery, in other words, must be understood as 

having no single determinative value, no one experience or effect; instead, its refractive 

powers touched every aspect of society. The article's form – its modules of refracted 

analysis, evidence, and historiography – is meant to instruct and carry forward the 

argument. 

Given the sweeping changes within the field of history and computing, we might ask 

what digital history scholarship might tackle in the future. A range of opportunities 

present themselves. The most anticipation and attention currently surround what is 

loosely called "historical GIS." Historical GIS (geographic information systems) refers to 

a methodology and an emergent interdisciplinary field in which computer-aided spatial 

analysis is applied to archaeology, history, law, demography, geography, environmental 

science, and other areas (see Knowles 2002). Historians are building large-scale systems 

for rendering historical data in geographic form for places across the world from Great 

Britain to Tibet, and they are finding new answers to old questions from Salem, 

Massachusetts, to Tokyo, Japan. Legal scholars have begun to examine "legal 

geographies" and to theorize about the implications of spatial understandings and 

approaches to legal questions (see Blomley 1994). These scholars seem only a step away 

from adopting historical GIS approaches to their studies of segregation, slavery, race 

relations, labor relations, and worker safety. Environmental historians and scientists, too, 

have developed new approaches to human and ecological change, examining subjects 

ranging from salt marsh economies and cultures in North America to the character and 

culture of Native American Indian societies in the river basins of the Chesapeake. Taken 

together these efforts represent remarkable possibilities for an integrated and networked 

spatial and temporal collection. 

But historians might dream up even more highly interpretive and imaginative digital 

creations. Extending historical GIS, they might attempt to recreate "lost landscapes" in 

ways that fully allow readers to move and navigate through them. These fourdimensional 

models might restore buildings, roads, and dwellings to historic landscapes 

as well as the legal, economic, social, and religious geographies within them. Networks 

of information, finance, trade, and culture might also find expression in these models. 

Readers might do more than query these datasets; they might interact within them too, 

taking on roles and following paths they could not predict but cannot ignore. Readers of 

these interpretations will have some of the same questions that the critics of earlier 

computer-aided history had. The goal for historians working in the new digital medium 

needs to be to make the computer technology transparent and to allow the reader to focus 

his or her whole attention on the "world" that the historian has opened up for 

investigation, interpretation, inquiry, and analysis. Creating these worlds, developing the

sequences of evidence and interpretation and balancing the demands and opportunities of 

the technology will take imagination and perseverance. 


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Rosenzweig, Roy and Michael O'Malley (1997). Brave New World or Blind Alley? 

American History on the World Wide Web. Journal of American History 84, 3 (June): 

132–55. 

Rosenzweig, Roy and Michael O'Malley (2001). The Road to Xanadu: Public and 

Private Pathways on the History Web. Journal of American History 88, 2 (September): 

548–79. 

Shorter, Edward (1971). The Historian and the Computer: A Practical Guide. Englewood 

Cliffs, NJ: Prentice Hall. 

Swierenga, Robert P., (ed.) (1970). Quantification in American History. New York: 

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Woodward, C. Vann (1974). The Jolly Institution. New York Review of Books, May 2. 

6. 

Lexicography 

Russon Wooldridge 

Lexicography is here considered in the literal and concrete sense of the word: the writing 

of the lexicon, the ordered description of the lexicon of a language in the form of a 

reference work usually called a dictionary. 

The following is intended as a typological and partial account of the use of computers in 

lexicography, dealing with the essential applications and the main examples, for English 

and French, of computer-assisted lexicographical products. The dictionaries considered 

are those intended for the general public; scant mention will be made of those created for 

specialists. 

Nature of the Dictionary Text 

A dictionary has fundamentally the same structure as a telephone directory, a hospital's or 

general practitioner's medical files, or a library catalogue. Each unit of these collections is 

a record containing a number of fields, potentially the same for each record (some fields 

are blank) and placed in the same order, the essential characteristic of this relational 

database being its recursiveness. 

• telephone directory entry: name, address, telephone number 

• medical record: name, personal coordinates, medical history, progress notes, 

consultations, lab reports, etc. 

• library catalogue record: title, author, place and date of publication, subject, material, 

ISBN number, holdings, etc. 

• dictionary entry: headword, pronunciation, part of speech, definition, examples, 

etymology, etc. 

Example of two dictionary entries (source: Dictionnaire universel francophone): 

dictionnaire n. m. Ouvrage qui recense et décrit, dans un certain ordre, un ensemble 

particulier d'éléments du lexique (sens 4). Dictionnaire médical, étymologique. – 

Dictionnaire de la langue ou dictionnaire de langue, qui décrit le sens, les valeurs, les

emplois, etc. des mots d'une langue. Le dictionnaire de I'Académiefrançaise. – 

Dictionnaire bilingue, qui donne les équivalents des mots et expressions d'une langue 

dans une autre langue. Un dictionnaire français – vietnamien. – Dictionnaire 

encyclopédique, qui, outre les descriptions de mots, fournit des développements 

encyclopédiques consacrés aux objets désignés par les mots. Syn. Fam. dico. 

informatique n. f. et adj. Technique du traitement automatique de l'information au 

moyen des calculateurs et des ordinateurs. Informatique de gestion. adj. Relatif à cette 

technique. Traitement par des moyens informatiques. 

Encycl. L'informatique est apparue avec le développement des calculateurs électroniques 

à grande capacité, les ordinateurs (le mot informatique date de 1962). La rapidité d'accès 

et de traitement de l'information, l'automatisme du fonctionnement des ordinateurs et la 

systématique des résolutions ont ouvert un très vaste champ d'application à 

l'informatique: recherche scientifique (ex.: contrôle de la trajectoire d'un satellite); 

Industrie (conception assistée par ordinateur, contrôle et commande des machines, des 

processus); gestion des entreprises (opérations administratives, simulation, recherche 

opérationnelle); enseignement programmé; documentation, banques d'informations; 

informatique individuelle. La liaison de plusieurs ordinateurs accroit la puissance de leur 

traitement, la télématique assurant la transmission (V. télématique, ordinateur, réseau). 

The recursiveness of the informational fields of the above two dictionary entries is 

indicated by typography, position, and abbreviation: (1) headword in bolded large letters; 

(2) part of speech conventionally abbreviated; (3) definition; (4) examples of usage in 

italics. Fields 1, 3, and 4 are given in full because of the idiosyncratic nature of lexical 

units; field 2 is given in abbreviated form since its values belong to a small finite class. 

Typography, position, abbreviation, and ellipsis (none of the four fields is explicitly 

named) are the features of dictionary recursiveness and economy (the dictionary is also a 

commercial product). Occasional fields tend to be named: "Syn." for synonyms; 

"Encycl." for encyclopedic information (normal, systematic, unlabeled information is 

linguistic); "V." for cross-reference to related terms. 

Even the simplest dictionary entries, such as the ones quoted above, tend to be 

structurally complex. Besides the main binary equations – (a) dictionnaire = masculine 

noun; (b) dictionnaire [means] "Ouvrage qui recense et décrit, dans un certain ordre, un 

ensemble particulier d'éléments du lexique"; (c) [the word] dictionnaire [typically occurs 

in expressions such as] Dictionnaire médical (domain of experience), [dictionnaire] 

étymologique (domain of language) – there are also ternary ones: dictionnaire > 

[exemplified in] Dictionnaire bilingue > [which means] "[dictionnaire] qui donne les 

équivalents des mots et expressions d'une langue dans une autre langue." (The implicit 

copulas and other terms are here made explicit and enclosed in square brackets.) 

Idiosyncrasy is characteristic of lexis and also of the dictionary, which in the vast 

majority of its realizations is composed by (fallible) human beings. Just as the treatment 

of lexical units will vary enormously according to part of speech, frequency of usage, 

monosemy or polysemy, register, and other variables, so will dictionary-writing tend to 

vary according to time (beginning, middle, or end of the alphabet, or of the writing of the

dictionary, even day of the week) and writer (entry-writer A and entry-writer B are 

individual human beings and not clones or machines). 

Setting aside the question of idiosyncrasy and variability of lexical units and dictionarywriting 

(the latter nevertheless an important obstacle in computerizing the Trésor de la 

langue française – see below), the well-ordered dictionary requires three types of 

sophisticated competence on the part of the user: (1) linguistic competence obviously; (2) 

dictionary competence, a particular type of textual competence, enabling one, for 

example, to find a word beginning with m by opening the dictionary more or less in the 

middle, to know that adj. means adjective/adjectif (and through linguistic competence to 

know what an adjective is), etc.; (3) pragmatic competence to make sense of references to 

the outside world: Le dictionnaire de l'Académie française, "calculateurs électroniques", 

etc. 

The requirement of different types of user competence combined with the frequent use of 

ellipsis can result in cases of ambiguity that tax the analytical faculties of the dictionary 

reader and render powerless the analytical parser of the computer. The following 

examples are taken from the entry for GAGNER in Lexis (Wooldridge et al. 1992): 

(a) Gagner quelque chose (moyen de subsistance, récompense), l'acquérir par son travail 

(b) Gagner son biftek (pop.) [= gagner tout juste sa vie] 

(c) Gagner le Pérou (= des sommes énormes) 

(d) Gagner le maquis (syn. PRENDRE) 

(e) C'est toujours ça de gagné (fam.) [= c'est toujours ça de pris] 

(f) Il ne gagne pas lourd (= peu) 

(g) Je suis sorti par ce froid, j'ai gagné un bon rhume (syn. plus usuels: ATTRAPER, 

PRENDRE, fam. CHIPER) 

Each of the seven items contains an equation of synonymy, the equation concerning 

either the whole or part of the first term: the object of the verb in (a) and (c), the adverbial 

qualifier in (f), the verb in (d) and (g), the whole expression in (b) and (e). Linguistic 

competence is necessary to equate quelque chose and l' (a), ne … pas lourd with peu (f), 

the conjugated form ai gagné with the infinitives attraper, prendre, and chiper (g). The 

dictionary user also has to deal with the variability of the synonymy delimiters and 

indicators (parentheses, brackets, equals sign, syn. label, upper case). 

In brief, the dictionary, in theory a systematic relational database, with ordered records 

and recurrent fields, may in human practice be as variable as the lexicon it sets out to 

describe. Successful applications of the computer to man-made dictionaries are, then, 

usually modest in their ambitions. Computer-driven dictionaries (machine dictionaries)

tend to be procrustean in their treatment of language, or limit themselves to relatively 

simple areas of lexis such as terminology. 

Pre-WWW (World Wide Web) 

Modern lexicography did not wait for the invention of the computer, nor even that of the 

seventeenth-century calculating machines of Leibniz and Pascal, to apply computer 

methods to dictionaries. In 1539, the father of modern lexicography, Robert Estienne, 

King's Printer, bookseller, humanist, and lexicographer, published his Dictionaire 

francois-latin (1539), a "mirror-copy" of his Dictionarium latinogallicum of the previous 

year. Each French word and expression contained in the glosses and equivalents of the 

Latin-French dictionary had its own headword or sub-headword in the French-Latin; each 

Latin word or expression contained in the headwords and examples of the Latin-French 

occurred as an equivalent to the corresponding French in the French-Latin. Example of 

the words aboleo and abolir.: 

1 Dispersion: 

1538: "ABOLEO […] Abolir, Mettre a neant." 

> 1539: "Abolir, Abolere" (s.v. Abolir), "Mettre a neant […] Abolere" (s.v. Mettre). 

2 Collection: 

1538: "ABOLEO […] Abolir", "ABROGO […] Abolir", "Antique […] Abolir" 

"Conuellere […] abolir." "Exterminare […] abolir." "Inducere […] Abolir", "Interuertere 

[…] Abolir", "OBLITERO […] abolir.", "Resignare, Abolir." 

> 1539: "Abolir, Abolere, Abrogare, Antiquare, Conuellere, Exterminare, Inducere, 

Interuertere, Obliterare, Resignare." 

Moving forward four centuries and several decades, we find the first applications of the 

computer to lexicography in the 1960s and 1970s. In the 1960s, the Centre pour un 

Trésor de la langue française in Nancy started keyboarding representative works of 

literature and technical treatises to provide source material for its print dictionary, the 

Dictionnaire de la langue du XIX e et du XX e siècle, commonly known as the Trésor de la 

langue française, or TLF. In the late 1970s, there appeared in England two machinereadable 

dictionaries, the Oxford Advanced Learners' Dictionary and the Longman 

Dictionary of Contemporary English; the latter used the computer not only to print off 

the paper dictionary, but also to help in its writing (Meijs 1992: 143–5). 

The first early dictionary to be computerized was Jean Nicot's Thresor de la langue 

françoyse of 1606. The text was keyboarded in Nancy and Toronto between 1979 and 

1984, indexed at the University of Toronto with the mainframe concordance program 

COGS, published in microfiche concordance form in 1985, indexed as a standalone 

interactive database with WordCruncher in 1988, and put on the World Wide Web in

1994 (see sections on the WWW era and on technological change, below). It is not 

without interest to note that in the early 1980s funding agencies expected concordance 

projects to undertake lemmatization of text forms. An argument had to be made to 

demonstrate the absurdity of attempting to lemmatize an already partly lemmatized text: 

dictionary headwords are lemmas. The Nicot project initially had the ambition of 

labeling information fields (Wooldridge 1982), until it quickly became obvious that such 

fields, though present and analyzable by the human brain, are impossible to delimit 

systematically in a complex early dictionary such as Nicot's Thresor, where position, 

typography, and abbreviation are variable, and functional polyvalence is common. The 

challenge is not negligible in modern dictionaries, where explicit field-labeling is the 

norm. Other early dictionaries have since been digitally retro-converted, notably Samuel 

Johnson's Dictionary of the English Language, published on CD-ROM in 1996. 

The 1980s saw the emergence of large-scale computer-assisted lexicographical 

enterprises. The COBUILD (Collins and Birmingham University International Language 

Database) Project started in 1980, with the intention of creating a corpus of contemporary 

English for the writing of an entirely new dictionary and grammar. The young discipline 

of corpus linguistics and the COBUILD Project fed off each other in this innovative 

lexicographical environment (Sinclair 1987; Renouf 1994). The New Oxford English 

Dictionary Project was formed to produce the second edition of the OED with the aid of 

computer technology. The project was international in scope: it was conceived and 

directed in England, the role of the computer was defined and implemented in Canada, 

the text was keyboarded in the United States of America. The second edition appeared in 

print in 1989 and on CD-ROM in 1992. 

Where early electronic tagging of dictionaries was restricted to typographical codes for 

printing the finished product, it soon became necessary to add information tags so that 

not only could the text be correctly displayed on screen or paper, but it could also be 

searched and referenced by fields. The dictionary was just one of the types of text whose 

structure was analyzed by the Text Encoding Initiative (TEI) (Ide et al. 1992). 

The last decade of the twentieth century witnessed the proliferation of electronic 

dictionaries distributed on CD-ROM. For example, the 1993 edition of the Random 

House Unabridged Dictionary came both in print and on CD-ROM, the two being sold 

together for the price of one. As one might expect of a freebie, the functionality of the 

Random House CD-ROM is rudimentary. On the other hand, the CD-ROM version of the 

Petit Robert, published in 1996, offers many advantages over the print edition: besides 

basic look-up of words and their entries, the user can search for anagrams (the search 

term dome produces dome and mode), homophones (saint produces sain, saint, sein, 

seing), etymologies by language (families: African, Amerindian, Arabic, etc., or specific 

idioms: Bantu, Hottentot, Somali, etc.), quotations by author, work, or character, plus 

full-text searches in either the complete dictionary text (full entries), or the particular 

fields of examples of usage or synonyms and antonyms. 

It is often the case that a number of entries are made more complete through the access to 

the full text granted by an electronic version of a dictionary. For example, to take the case

of the Petit Robert, sabotage, applied to work, organizations, or machinery, in the entry 

for the word, is used in an important – and common – figurative sense in a quotation 

concerning speaker. "Sabotage de la prononciation de notre belle langue par les speakers 

de la radio." Dictionaries tend to be more conservative in their treatment of a word in its 

own entry than elsewhere. 

A brief mention should be made of computer-assisted lexicographical tools for the 

everyday user, the main ones being word-processor spellcheckers and thesauruses. 

A good broad account of the period 1960-early 1990s, that of computer-assisted and 

computer-driven lexicography resulting in print and CD-ROM dictionaries, is given by 

Meijs (1992); an in-depth one by Knowles (1990). 

Lexicography in the WWW Era 

Like many other human practices, lexicography – and particularly lexicography – has 

been transformed by the World Wide Web. The Web works by virtue of words; to quote 

from the title of a well-known book by James Murray's granddaughter (Murray 1977), the 

Web, like the dictionary, is a "web of words." One reads the words in a book, one looks 

up headwords in a dictionary, one surfs the Web by keywords. The millions of documents 

published on the Web constitute, through the structuring of search engine keywords, a 

vast dictionary, an encyclopedic dictionary of concepts and words. Conventional 

dictionaries, whether paper or electronic, pale by comparison, though many of the latter 

are caught in the online Web of words. 

An early demonstration of the Web as super- or meta-dictionary can be found in 

Wooldridge et al. (1999). A Web search of the Canadian French word enfirouaper 

(search term: enfirou*) collected occurrences of the verb and derivatives both used and 

commented on; the documents were of all types: political and personal, newspaper report 

and manifesto, poetry and prose, dialogue and dictionary. The occurrences of the word in 

use showed that dictionary and glossary treatment is narrow and out-of-date (cf. sabotage 

above). Applying the principles of corpus creation and analysis learned from the 

COBUILD Project, the WebCorp Project at the University of Liverpool uses standard 

Web search engines such as Google and AltaVista to collect results from the Web and 

format them in easily analyzable KWIC concordances (Kehoe and Renouf 2002). For 

example, expressions such as one Ave short of a rosary, two leeks short of a harvest 

supper, or two sheets short of a bog roll, encountered in the novels of Reginald Hill, are 

individual realizations of the commonly used pattern "one/two/three/a/an/several X short 

of a Y" (X being constituent parts of the whole Y), which can be expressed in Google or 

AltaVista by variants of the search term "one * short of a". Since WebCorp is, at least at 

the time of writing, freely available on the Web, corpus linguistics has become a 

lexicographical tool for the general public. 

Meta-sites are a good source of information about online dictionaries. For French, two 

good ones are Robert Peckham's Leximagne – l'Empereur des pages dico and Carole 

Netter's ClicNet: Dictionnaires. The latter gives links for the following categories (I

translate): Multilingual dictionaries; French-language dictionaries and encyclopedias; 

Grammar, morphology, orthography, and linguistics; Historical dictionaries; Dictionaries 

of Architecture, Visual arts, Slang, Law, Economics and Finance, Gastronomy and 

Dietetics, History, Humor, Games, Multicultural dictionaries, Literature, Media, Music, 

Nature and Environment, Science, Political science, Services, Social sciences and 

Humanities, Sports, Techniques, Tourism, Various vocabularies; Internet glossaries; 

Discussion lists; Lexical columns; Other servers. 

Most online dictionaries are fairly modest in scope and are published as straight text, just 

like a print dictionary. A few, however, can be queried interactively as relational 

databases, and may offer other features. It is interesting then to compare those of the two 

main online dictionaries of English and French, the OED and the TLF. 

• In the late 1990s, a first online version of the second edition of the OED became 

available through the OED Project at the University of Waterloo; a more generally 

available edition, the OED Online, was launched on the main OED website in 2000. Both 

versions are accessible by subscription only and allow the following types of search: 

"lookup" (as in the print version), "entire entry" = full-text search, "etymology", "label" 

(= field label). Electronic network technology has also made a significant contribution to 

the OED's reading program: in place of the parcels of paper slips from around the globe 

delivered by the Post Office to the Oxford Scriptorium of James Murray's day, readers 

can now send in words, references, and details via the Web. The OED website gives a 

detailed history of the dictionary, thus adding to a scholarly value rare on online 

dictionary sites. 

• The complete version of the TLFI (Trésor de la langue française informatisé), 

published on the Web in 2002, is both free and, somewhat ambitiously, lets the user limit 

queries to one or several of 29 fields, including "entrée" (the whole entry), "exemple" 

(with subcategories of various types of example), "auteur d'exemple" (examples by 

author), "date d'exemple" (by date), "code grammatical", "définition", "domaine 

technique", "synonyme/antonyme." The 16-volume print TLF suffered from a high 

degree of writing variation (cf. the first section, above), making field tagging an 

extremely difficult task and forcing the Institut National de la Langue Française (INaLF) 

team to adopt in part a probabilistic approach in creating the electronic version (Henry 

1996). 

A characteristic of the Web is the hyperlink, which facilitates, among other things, the 

association between text and footnote (intratextual link), that between the bibliographical 

reference and the library (intertextual link), or that between a word A encountered within 

the dictionary entry for word B and the entry for word A (e.g. "anaptyxis: epenthesis of a 

vowel" > epenthesis). The Dictionnaire universel francophone en ligne (DUF), an 

important free language resource for speakers and learners of all varieties of French and 

the online equivalent of the one-volume general language print dictionary to be found in 

most homes, has hyperlinks for every word contained within its entries, allowing the user 

to refer to the entry of any given word with a single click (e.g. "sabotage n. m. 1. TECH

Action de saboter (un pieu, une traverse, etc.)" > nom, masculin, technique, 

technologie, technologique, action, de, saboter, un, pieu, traverse, et caetera). 

Apart from dictionaries of general contemporary language, there are a large number of 

marked or specialized ones. In the field of early dictionaries, there are several sixteenth- 

to early twentieth-century ones freely accessible in database form in the section 

Dictionnaires d'autrefois on the site of the ARTFL (American and French Research on 

the Treasury of the French Language) Project at the University of Chicago: Estienne, 

Nicot, Bayle, Académie française (also on an INaLF server in Nancy). Interactive 

databases of several of these and others are on a server of the University of Toronto. 

Terminology, once the reserve of paying specialists, is now freely available on the Web. 

For example, a Glossaire typographique et linguistique or a Terminology of Pediatric 

Mastocytosis;, a pediatric mastocytosis term such as anaphylaxis occurs on tens of 

thousands of Web pages (69,700 hits with Google on September 28, 2002). 

Web lexicography offers a number of tools, a significant one being automatic translation 

(e.g., Babelfish) intended to translate the gist of a Web document into a language 

understood by the user. A good account of the merits and drawbacks of automatic 

translation on the Web is given by Austermühl (2001). 

Along with the professional team dictionaries, such as the OED, the TLF, or the DUF, 

and the specialized lexicons accessible on the Web, there are also to be found dictionaries 

and glossaries compiled by amateurs and individuals. If one wants, for example, to 

explore the Dublin slang encountered in Roddy Doyle's Barrytown Trilogy, the most 

ready source of online information is the O'Byrne Files. 

A final word should be reserved for recreational lexicography. The word games of the 

parlor, radio, television, books, and the press proliferate on the Web. The OED site 

proposes "Word of the Day"; COBUILD has "Idiom of the Day", "The Definitions 

Game", and "Cobuild Competition." Many sites offer "Hangman" or "Le Jeu du pendu." 

There are various types of "Crossword" or "Mots croisés", "Anagrams" and 

"Anagrammes;" Online "Scrabble" has interactive play sites and tool box (dictionary) 

sites. 

A Case Study in Technological Change 

This last section takes a single dictionary-computerization project and looks at the 

various technological stages it has gone through over the years. The project in question is 

that concerning Jean Nicot's Thresor de la langue françoyse. 

(a) Mechanography. When the present writer set out to analyze Nicot's Thresor in 

Besançon, the technology of the time used to manipulate textual data involved a BULL 

mechanographical computer using IBM cards and only capable of handling small, simple 

corpora such as the plays of Corneille or the poems of Baudelaire. The idea, which 

occurred in the 1960s, of putting the Thresor into digital, full-text searchable form had to 

wait for technological advances.

(b) Keyboarding, tape-perforation, and storing on magnetic tape. In 1979, the manual 

capture of half of the Thresor was begun at the Institut national de la langue française in 

Nancy, followed in 1980 by the commencement of capture of the other half at the 

University of Toronto. In Nancy, keyboarded data were captured onto paper tape and 

then transferred to magnetic tape; Toronto entry was sent directly from a keyboard via a 

telephone modem to an IBM mainframe computer. Data sent from Nancy to Toronto by 

mail on magnetic tape were made compatible with the Toronto input through a variety of 

routines written in various languages including Wylbur. 

(c) Concordancing on microfiches. In 1984, the complete unified text was run through the 

COGS mainframe concordance program written at the University of Toronto. Practically 

the entire resources of the central computing service of the University of Toronto were 

reserved for one night to index and concord the approximately 900,000 words of the text 

of the Thresor. Some of the concordance output was done with Spitbol routines. The 

thirty magnetic tapes were output commercially to microfiches. 

(d) WordCruncher on a standalone. In 1988, the data were transferred via modem and 

five-and-a-quarter inch floppy diskettes from the mainframe to a midi-computer and 

thence to an IBM AT personal computer with a 20 MB hard disk. This time it only took 

the resources of one small machine to index the full text of the Thresor and create an 

interactive concordance database. 

(e) The World Wide Web. The Thresor was first put online in 1994 as an interactive 

database at the ARTFL Project of the University of Chicago, the ASCII data files being 

converted to run under the program Philologic. In 2000, ARTFL's Dictionnaires 

d'autrefois were installed on a server at the INaLF in Nancy using the Stella interactive 

database program. In the same year, the Thresor was put up as an interactive database on 

a Windows server at the University of Toronto running under TACTweb, the data files 

first of all being indexed by TACT on an IBM-compatible. The reference fields – entry 

headword, page, and typeface – derive from the tags entered manually during the first 

stage of data capture in Nancy and Toronto. 

Conclusion 

The most radical effect that the computer has had on lexicography – from dictionaries on 

hard disk or CD-ROM, through to dictionaries on the World Wide Web and to the Webas-mega-dictionary 

– has been to supplement the limited number of paths for information 

retrieval determined in advance by author and publisher with the infinite number of paths 

chosen by the dictionary-user. It is now normal for the user to feel in charge of 

information retrieval, whether it be through access to the full text of a dictionary or the 

entire reachable resources of the Web. Headwords have been supplanted by keywords. 

References for Further Reading

Austermühl, Frank (2001). Electronic Tools for Translators. Manchester: St Jerome 

Publishing. 

ClicNet: Dictionnaires et lexiques [Dictionaries and lexicons] (2002). Carole Netter, 

Swarthmore College. At http://clicnet.swarthmore.edu/dictionnaires.html. 

Collins COBUILD. At http://titania.cobuild.collins.co.uk/. 

Dictionnaire universel francophone en ligne (since 1997). [Online universal francophone 

dictionary]. Hachette and AUPELF-UREF. At http://www.francophonie.hachettelivre.fr/. 

Dictionnaires d'autrefois [Early dictionaries]. ARTFL, University of Chicago. Accessed 

April 5, 2004. At http://www.lib.uchicago.edu/efts/ARTFL/projects/dicos/. 

Estienne, Robert (1538). Dictionarium latinogallicum [Latin-French dictionary]. Paris: 

R. Estienne. 

Estienne, Robert (1539). Dictionaire francoislatin [French-Latin dictionary]. Paris: R. 

Estienne. 

Glossaire typographique et linguistique (since 1996–7) [Typographical and linguistic 

dictionary]. Alis Technologies Inc. Accessed April 5, 2004. At 

http://babel.alis.com:8080/glossaire/index.fr.html. 

Henry, Françoise (1996). Pour une informatisation du TLF [For a computerization of the 

TLF]. In D. Piotrowski (ed.), Lexicographie et informatique: autour de l'informatisation 

du Trésor de la langue française (pp. 79–139). Paris: Didier Érudition. 

Ide, Nancy, Jean Véronis, Susan Warwick-Armstrong, and Nicoletta Calzolari (1992). 

Principles for Encoding Machine Readable Dictionaries. In H. Tommola, K. Varantola, 

T Salmi-Tolonen, and J. Schopp (eds.), Euralex 1992 Proceedings (pp. 239–46). 

Tampere: University of Tampere. 

Johnson, Samuel (1996). A Dictionary of the English Language on CD-ROM, ed. Anne 

McDermott. Cambridge and New York: Cambridge University Press. 

Kehoe, Andrew and Antoinette Renouf (2002). WebCorp: Applying the Web to 

Linguistics and Linguistics to the Web. In WWW2002: Eleventh International World 

Wide Web Conference. Accessed April 5, 2004. At 

http://www2002.org/CDROM/poster/67/. 

Knowles, Francis E. (1990). The Computer in Lexicography. In F. J. Hausmann, O. 

Reichmann, H. E. Wiegand, and L. Zgusta (eds.), Wörterbucher: Ein Internationales 

Handbuch zur Lexicographie, vol. 1 (pp. 1645–72). Berlin and New York: Walter de 

Gruyter.

Leximagne - l'empereur des pages dico [The emperor of dictionary pages]. TennesseeBob 

Peckham, University of Tennessee-Martin. Accessed April 5, 2004. At 

http://www.utm.edu/departments/french/dico.shtml. 

Meijs, Willem (1992). Computers and Dictionaries. In Christopher S. Butler (ed.), 

Computers and Written Texts (pp. 141–65). Oxford and Cambridge, MA: Blackwell. 

Murray, K. M. Elisabeth (1977). Caught in the Web of Words. New Haven, CT: Yale 


The O'Byrne Files (since 2000). Accessed April 5, 2004. At 

http://homepage.tinet.ie/~nobyrne/slang.html. 

Oxford English Dictionary. Accessed April 5, 2004. At http://www.oed.com/. 

Renouf, Antoinette (1994). Corpora and Historical Dictionaries. In I. Lancashire and R. 

Wooldridge (eds.), Early Dictionary Databases (pp. 219–35). Toronto: Centre for 

Computing in the Humanities. 

Sinclair, John M., (ed.) (1987). Looking Up. London and Glasgow: Collins. 

Terminology of Pediatric Mastocytosis. MastoKids.org. Accessed April 5, 2004. At 

http://www.mastokids.org/index.php?x=terminology.php. 

Le Trésor de la langue française informatisé [Computerized treasury of the French 

language] (2002). Accessed April 5, 2004. At http://atilf.atilf.fr/tlf.htm. 

WebCorp. Accessed April 5, 2004. At http://www.webcorp.org.uk/. 

Wooldridge, Russon (1982). Projet de traitement informatique des dictionnaires de 

Robert Estienne et de Jean Nicot. [Project for the computerization of the dictionaries of 

Robert Estienne and Jean Nicot]. In Manfred Höfler (ed.), La Lexicographie française du 

XVI e au XVIII e siècle (pp. 21–32). Wolfenbüttel: Herzog August Bibliothek. 

Wooldridge, Russon (1985). Concordance du Thresor de la langue françoyse de Jean 

Nicot (1606) [Concordance of Nicot's Thresor de la langue françoyse (1606)]. Toronto: 

Éditions Paratexte. Wooldridge, Russon (2000). Interactive database of Dictionnaires de 

la Renaissance. Accessed April 5, 2004. At 

http://www.chass.utoronto.ca/~wulfric/dico_tactweb/tiden.htm. 

Wooldridge, Russon, Astra Ikse-Vitols, and Terry Nadasdi (1992). Le Projet CopuLex. 

[The CopuLex project]. In R. Wooldridge (ed.), Historical Dictionary Databases (pp. 

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2004. At http://www.chass.utoronto.ca/~wulfric/lexperimenta/enfirouaper/. 

7. 

Linguistics Meets Exact Sciences 

Jan Hajič 

Linguistics is a science that studies human language, both spoken and written. It studies 

the structure of language and its usage, the function of its elements, and its relation to 

other bordering sciences (psycholinguistics, sociolinguistics, etc.), of which the most 

important related field today is computer science. Diachronic linguistics studies language 

from the perspective of its development over time, whereas synchronic linguistics studies 

the function and structure of the current live language. General linguistics studies human 

language as a system, but particular languages (such as English, Mandarin, Tamil, or any 

other) are studied in detail as well. Due to its complexity, the study of language is often 

divided into several areas. Phonetics and phonology are related to speech (or more 

precisely, to the spoken language), whereas orthography deals with the standard written 

form of a particular language, including capitalization and hyphenation where 

appropriate. Morphology studies the composition of words by morphemes (prefixes, 

roots, suffixes, endings, segmentation in general, etc.) and its relation to syntax, which 

introduces structure into the description of language at the level of phrases, clauses, and 

sentences. Sentences are typically considered to be very important basic language units. 

Semantics and pragmatics study the relation of the lower levels to meaning and contents, 

respectively. 

A description of a (correct) behavior of a particular language is typically called a 

grammar. A grammar usually generalizes: it describes the language structurally and in 

terms of broad categories, avoiding the listing of all possible words in all possible clauses 

(it is believed that languages are infinite, making such a listing impossible). An English 

grammar, for example, states that a sentence consisting of a single clause typically 

contains a subject, expressed by a noun phrase, and a verb phrase, expressed by a finite 

verb form as a minimum. A grammar refers to a lexicon (set of lexical units) containing 

word-specific information such as parts of speech (noun, verb, adjective, particle, etc.) or 

syntactic subcategorization (e.g., that the verb "to attach" has a subject and an indirect 

object with the preposition "to"). 

From the historic perspective, the scientific, economic, and political developments in the 

world before and during World War II were preparing the field of linguistics for what 

happened shortly thereafter. Natural language moved into the focus of people working in 

several scientific fields previously quite distant from linguistics (and other humanities as 

well): computer science, signal processing, and information theory, supported by 

mathematics and statistics. Today, we can say that one of the turning points for linguistics

was Claude Shannon's work (1948). Shannon was an expert in communication theory, 

and his work belongs to what is known today as information theory, a probabilistic and 

statistical description of information contents. However, he was interested not only in the 

mathematical aspects of technical communication (such as signal transfer over telegraph 

wires), but he and Warren Weaver also tried to generalize this approach to human 

language communication. Although forgotten by many, this work was the first attempt to 

describe the use of natural language by strictly formal (mathematical and statistical, or 

stochastic) methods. The recent revival of stochastic methods in linguistics only 

underscores the historical importance of their work. 

Developments in theoretical linguistics that led to the use of strictly formal methods for 

language description were strongly influenced by Ferdinand de Saussure's work (1916). 

His work turned the focus of linguists to so-called synchronic linguistics (as opposed to 

diachronic linguistics). Based on this shift in paradigm, the language system as a whole 

began to be studied. Later, Noam Chomsky (even though his view differs from de 

Saussure's in many respects) came up with the first systematic formalization of the 

description of the sentences of natural language (1957). It should be noted, however, that 

Chomsky himself has always emphasized that his motivation for introducing formal 

grammar has never been connected with computerization. Moreover, he has renounced 

probabilistic and statistical approaches. For various reasons, most notably the lack of 

computer power needed for probabilistic and other computationally intensive approaches, 

his work stayed dominant in the field of computational linguistics for more than thirty 

years. 

In line with the formal means he proposed, Chomsky also adopted the view (introduced 

by the descriptivist school of American linguists) that sentence structure in natural 

language can be represented essentially by recursive bracketing, which puts together 

smaller, immediately adjacent phrases (or so-called constituents) to form bigger and 

bigger units, eventually leading to a treelike sentence structure. An alternative theory 

known in essence from the nineteenth century (Becker 1837) but developed in its modern 

form by Tesniere (1959) and other European linguists states that the relation between two 

sentence constituents is not that of closeness but that of dependence. This theory offers 

some more flexibility in expressing relations among constituents that are not immediately 

adjacent, and it is also more adequate for a functional view of language structure. 

Other formal theories emerged during the 1960s, 1970s, and early 1980s. The generalized 

phrase structure grammars (Gazdar) are still close to the context-free grammar formalism 

as proposed by Chomsky. The formalism later developed into so-called head-driven 

phrase structure grammars, a formalism that has characteristics of both the immediate 

constituent structure and dependency structure. Similarly, the independently developed 

lexicalized functional grammar (Kaplan and Bresnan 1983) explicitly separates the 

surface form, or constituent structure (so-called c-structure) from functional structure (fstructure, 

which is close to the predicate-argument structure) and uses lexical information 

heavily.

Chomsky himself proposed a number of modifications of his original theory during the 

same period, most notably the government and binding theory (1981), later referred to as 

the principles and parameters theory, and recently the minimalist theory (1993). While 

substantially different from the previous theories and enthusiastically followed by some, 

they seem to contribute little to actual computational linguistics goals, such as building 

wide-coverage parsers of naturally occurring sentences. Chomsky's work is thus more 

widely used and respected in the field of computer science itself, namely in the area of 

formal languages, such as syntax of programming or markup languages. 

During the 1980s, stochastic methods (based largely on Shannon and Weaver's work, cf. 

above) re-emerged on a higher level, primarily thanks to the greatly increased power of 

computers and their widespread availability even for university-based research teams. 

First, the use of stochastic methods has led to significant advances in the area of speech 

recognition (Bahl et al. 1983), then it was applied to machine translation (Brown et al. 

1990), and in the late 1990s, almost every field of computational linguistics was using 

stochastic methods for automatic text and speech processing. More complex, more 

sophisticated ways of using probability and statistics are now available, and formal nonprobabilistic 

means of language descriptions are being merged with the classic 

information-theoretic methods, even though we are still waiting for a real breakthrough in 

the way the different methods are combined. 

Computational Linguistics 

Computational linguistics is a field of science that deals with computational processing of 

a natural language. On its theoretical side, it draws from modern (formal) linguistics, 

mathematics (probability, statistics, information theory, algebra, formal language theory, 

etc.), logic, psychology and cognitive science, and theoretical computer science. On the 

applied side, it uses mostly the results achieved in modern computer science, user studies, 

artificial intelligence and knowledge representation, lexicography (see chapter 6), and 

language corpora (see chapter 21). Conversely, the results of computational linguistics 

contribute to the development of the same fields, most notably lexicography, electronic 

publishing (see chapter 35), and access to any kind of textual or spoken material in digital 

libraries (see chapter 36). 

Computational linguistics can be divided (with many overlappings, of course) into 

several subfields, although there are often projects that deal with several of them at once. 

Theoretical computational linguistics deals with formal theories of language description 

at various levels, such as phonology, morphology, syntax (surface shape and underlying 

structure), semantics, discourse structure, and lexicology. Accordingly, these subfields 

are called computational phonology, computational semantics, etc. Definition of formal 

systems of language description also belongs here, as well as certain research directions 

combining linguistics with cognitive science and artificial intelligence. 

Stochastic methods provide the basis for application of probabilistic and stochastic 

methods and machine learning to the specifics of natural language processing. They use

heavy mathematics from probability theory, statistics, optimization and numerical 

mathematics, algebra and even calculus – both discrete and continuous mathematical 

disciplines are used. 

Applied computational linguistics (not to be confused with commercial applications) tries 

to solve well-defined problems in the area of natural language processing. On the analysis 

side, it deals with phonetics and phonology (sounds and phonological structure of words), 

morphological analysis (discovering the structure of words and their function), tagging 

(disambiguation of part-of-speech and/or morphological function in sentential context), 

and parsing (discovering the structure of sentences; parsing can be purely structureoriented 

or deep, trying to discover the linguistic meaning of the sentence in question). 

Word sense disambiguation tries to solve polysemy in sentential context, and it is closely 

related to lexicon creation and use by other applications in computational linguistics. In 

text generation, correct sentence form is created from some formal description of 

meaning. Language modeling (probabilistic formulation of language correctness) is used 

primarily in systems based on stochastic methods. Machine translation usually combines 

most of the above to provide translation from one natural language to another, or even 

from many to many (see Zarechnak 1979). In information retrieval, the goal is to retrieve 

complete written or spoken documents from very large collections, possibly across 

languages, whereas in information extraction, summarization (finding specific 

information in large text collections) plays a key role. Question answering is even more 

focused: the system must find an answer to a targeted question not just in one, but 

possibly in several documents in large as well as small collections of documents. Topic 

detection and tracking classifies documents into areas of interest, and it follows a story 

topic in a document collection over time. Keyword spotting (or, flagging a document that 

might be of interest based on preselected keywords) has obvious uses. Finally, dialogue 

systems (for man-machine communication) and multi-modal systems (combining 

language, gestures, images, etc.) complete the spectrum. 

Development tools for linguistic research and applications are needed for quick 

prototyping and implementation of systems, especially in the area of applied 

computational linguistics as defined in the previous paragraph. Such support consists of 

lexicons for natural language processing, tools for morphological processing, tagging, 

parsing and other specific processing steps, creation and maintenance of language 

corpora (see chapter 21) to be used in systems based on stochastic methods, and 

annotation schemas and tools. 

Despite sharing many of the problems with written language processing, speech (spoken 

language) processing is usually considered a separate area of research, apart even from 

the field of computational linguistics. Speech processing uses almost exclusively 

stochastic methods. It is often referred to as automatic speech recognition (ASR), but the 

field is wider: it can be subdivided into several subfields, some of them dealing more 

with technology, some with applications. Acoustic modeling relates the digitized speech 

signal and phonemes as they occur in words, whereas language modeling for speech 

recognition shares many common features with language modeling of written language, 

but it is used essentially to predict what the speaker will utter next based on what she said

in the immediate past. Speaker identification is another obvious application, but it is 

typically only loosely related to the study of language, since it relies more on acoustic 

features. Small-vocabulary speech recognition systems are the basis for the most 

successful commercial applications today. For instance, systems for recognizing digits, 

typically in difficult acoustic conditions such as when speaking over a telephone line, are 

widely used. Speaker-independent speech recognition systems with large vocabularies 

(sometimes called dictation) are the focus of the main research direction in automatic 

speech recognition. Speaker adaptation (where the speech recognizers are adapted to a 

particular person's voice) is an important subproblem that is believed to move today's 

systems to a new level of precision. Searching spoken material (information retrieval, 

topic detection, keyword spotting, etc.) is similar to its text counterpart, but the problems 

are more difficult due to the lack of perfect automatic transcription systems. A less 

difficult, but commercially very viable field is text-to-speech synthesis (TTS). 

It is clear however that any such subdivision can never be exact; often, a project or 

research direction draws upon several of the above fields, and sometimes it uses methods 

and algorithms even from unrelated fields, such as physics or biology, since more often 

than not their problems share common characteristics. 

One very important aspect of research in computational linguistics, as opposed to many 

other areas in humanities, is its ability to be evaluated. Usually, a gold-standard result is 

prepared in advance, and system results are compared (using a predefined metric) to such 

a gold-standard (called also a test) dataset. The evaluation metric is usually defined in 

terms of the number of errors that the system makes; when this is not possible, some 

other measure (such as test data probability) is used. The complement of error rate is 

accuracy. If the system produces multiple results, recall (the rate at which the system hits 

the correct solution by at least one of its outputs) and precision (the complement of false 

system results) have to be used, usually combined into a single figure (called F-measure). 

Objective, automatic system evaluation entered computational linguistics with the revival 

of statistical methods and is considered one of the most important changes in the field 

since its inception – it is believed that such evaluation was the driving force in the fast 

pace of advances in the recent past. 

The Most Recent Results and Advances 

Given the long history of research in computational linguistics, one might wonder what 

the state-of-the-art systems can do for us today, at the beginning of the twenty-first 

century. This section summarizes the recent results in some of the subfields of 

computational linguistics. 

The most successful results have been achieved in the speech area. Speech synthesis and 

recognition systems are now used even commercially (see also the next section). Speech 

recognizers are evaluated by using so-called word error rate, a relatively simple measure 

that essentially counts how many words the system missed or confused. The complement 

of word error rate is the accuracy, as usual. The latest speech recognizers can handle 

vocabularies of 100,000 or more words (some research systems already contain million-

word vocabularies). Depending on recording conditions, they have up to a 95 percent 

accuracy rate (with a closely mounted microphone, in a quiet room, speaker-adapted). 

Broadcast news is recognized at about 75–90 percent accuracy. Telephone speech (with 

specific topic only, smaller vocabulary) gives 30–90 percent accuracy, depending on 

vocabulary size and domain definition; the smaller the domain, and therefore the more 

restricted the grammar, the better the results (very tiny grammars and vocabularies are 

usually used in successful commercial applications). The worst situation today is in the 

case of multi-speaker spontaneous speech under, for example, standard video recording 

conditions: only 25–45 percent accuracy can be achieved. 

It depends very much on the application whether these accuracy rates are sufficient or 

not; for example, for a dictation system even 95 percent accuracy might not be enough, 

whereas for spoken material information retrieval an accuracy of about 30 to 40 percent 

is usually sufficient. 

In part-of-speech tagging (morphological disambiguation), the accuracy for English 

(Brants 2000) has reached 97–98 percent (measuring the tag error rate). However, current 

accuracy is substantially lower (85–90 percent) for other languages that are 

morphologically more complicated or for which not enough training data for stochastic 

methods is available. Part-of-speech tagging is often used for experiments when new 

analytical methods are considered because of its simplicity and test data availability. 

In parsing, the number of crossing brackets (i.e., wrongly grouped phrases) is used for 

measuring the accuracy of parsers producing a constituent sentence structure, and a 

dependency error rate is used for dependency parsers. Current state-of-the-art English 

parsers (Collins 1999; Charniak 2001) achieve 92–94 percent combined precision and 

recall in the crossing-brackets measure (the number would be similar if dependency 

accuracy is measured). Due to the lack of training data (treebanks) for other languages, 

there are only a few such parsers; the best-performing published result of a foreignlanguage 

parser (on Czech, see Collins et al. 1999) achieves 80 percent dependency 

accuracy. 

Machine translation (or any system that produces free, plain text) is much more difficult 

(and expensive) to evaluate. Human evaluation is subjective, and it is not even clear what 

exactly should be evaluated, unless a specific task and environment of the machine 

translation system being evaluated is known. Machine translation evaluation exercises 

administered by DARPA (Defense Advanced Research Projects Agency) in the 1990s led 

to about 60 percent subjective translation quality of both the statistical machine 

translation (MT) systems as well as the best commercial systems. Great demand for an 

automatic evaluation of machine translation systems by both researchers and users has 

led to the development of several automated metrics (most notably, those of Papineni, see 

Papineni et al. 2001) that try to simulate human judgment by computing a numeric match 

between the system's output and several reference (i.e., human) translations. These 

numbers cannot be interpreted directly (current systems do not go over 0.30–0.35 even 

for short sentences), but only in relation to a similarly computed distance between human

translations (for example, that number is about 0.60 with 4 reference translations; the 

higher the number, the better). 

Successful Commercial Applications 

The success of commercial applications is determined not so much by the absolute 

accuracy of the technology itself, but rather by the relative suitability to the task at hand. 

Speech recognition is currently the most successful among commercial applications of 

language processing, using almost exclusively stochastic methods. But even if there are 

several dictation systems on the market with accuracy over 95 percent, we are for the 

most part still not dictating e-mails to our computers, because there are many other 

factors that make dictation unsuitable: integration with corrections is poor, noisy 

environments decrease the accuracy, sometimes dramatically. On the other hand, 

scanning broadcast news for spotting certain topics is routinely used by intelligence 

agencies. Call routing in the customer service departments of major telephone companies 

(mostly in the USA) is another example of a successful application of speech recognition 

and synthesis: even if the telephone speech recognition is far from perfect, it can direct 

most of the customer's calls correctly, saving hundreds of frontline telephone operators. 

Directory inquiries and call switching in some companies are now also handled 

automatically by speech recognition; and we now have cell phones with ten-number 

voice dialing – a trivial, but fashionable application of speech recognition technology – 

not to mention mass-market toys with similar capabilities. 

Successful applications of non-speech natural language processing technology are much 

more rare, if we do not count the ubiquitous spelling and grammar checkers in word 

processing software. The most obvious example is machine translation. General machine 

translation systems, an application that has been being developed at many places for 

almost fifty years now, is still lousy in most instances, even if it sort of works for rough 

information gathering (on the Web for example); most translation bureaus are using 

translation memories that include bilingual and multilingual dictionaries and previously 

translated phrases or sentences as a much more effective tool. Only one system – 

SYSTRAN – stands out as a general machine translation system, now being used also by 

the European Commission for translating among many European languages (Wheeler 

1984). Targeted, sharply constrained domain-oriented systems are much more successful: 

an excellent example is the Canadian METEO system (Grimaila and Chandioux 1992), in 

use since May 24, 1977, for translating weather forecasts between English and French. 

Research systems using stochastic methods do surpass current general systems, but a 

successful commercial system using them has yet to be made. 

Searching on the Web is an example of a possible application of natural language 

processing, since most of the Web consists of natural language texts. Yet most of the 

search engines (with the notable exception of Askjeeves and its products 

, which allows queries to be posted in plain English) still use simple 

string matching, and even commercial search applications do not usually go beyond 

simple stemming. This may be sufficient for English, but with the growing proportion of

foreign-language web pages the necessity of more language-aware search techniques will 

soon become apparent. 

Future Perspectives 

Due to the existence of corpora, to the ascent of stochastic methods and evaluation 

techniques, computational linguistics has become mostly an experimental science – 

something that can hardly be said about many other branches of humanities sciences. 

Research applications are now invariably tested against real-world data, virtually 

guaranteeing quick progress in all subfields of computational linguistics. However, 

natural language is neither a physical nor a mathematical system with deterministic 

(albeit unknown) behavior; it remains a social phenomenon that is very difficult to handle 

automatically and explicitly (and therefore, by computers), regardless of the methods 

used. Probability and statistics did not solve the problem in the 1950s (weak computer 

power), formal computational linguistics did not solve the problem in the 1960s and 

1970s (language seems to be too complex to be described by mere introspection), and 

stochastic methods of the 1980s and 1990s apparently did not solve the problem either 

(due to the lack of data needed for current data-hungry methods). It is not unreasonable to 

expect that we are now, at the beginning of the twenty-first century, on the verge of 

another shift of research paradigm in computational linguistics. Whether it will be more 

linguistics (a kind of return to the 1960s and 1970s, while certainly not leaving the new 

experimental character of the field), or more data (i.e., the advances in computation – 

statistics plus huge amounts of textual data which are now becoming available), or neural 

networks (a long-term promise and failure at the same time), a combination of all of 

those, or something completely different, is an open question. 


There are excellent textbooks now for those interested in learning the latest developments 

in computational linguistics and natural language processing. For speech processing, 

Jelinek (2000) is the book of choice; for those interested in (text-oriented) computational 

linguistics, Charniak (1996), Manning and Schutze (1999), and Jurafsky and Martin 

(2000) are among the best. 

Bahl, L. R., F. Jelinek, and R. L. Mercer (1983). A Maximum Likelihood Approach to 

Continuous Speech Recognition. IEEE Transactions on Pattern Analysis and Machine 

Intelligence 5, 2: 179–220. 

Becker, K. F. (1837). Ausfürliche Deutsche Grammatik als Kommentar der 

Shulgrammatik. Zweite Abtheilung [Detailed Grammar of German as Notes to the School 

Grammar]. Frankfurt am Main: G. F. Kettembeil. 

Brants, T. (2000). TnT - A Statistical Part-of-Speech Tagger. In S. Nirenburg, 

Proceedings of the 6th ANLP (pp. 224–31). Seattle, WA: ACL.

Brown, P. F., J. Cocke, S. A. Della Pietra, V. J. Della Pietra, F. Jelinek, J. D. Lafferty, R. 

L. Mercer, and P. S. Roossin (1990). A Statistical Approach to Machine Translation. 

Computational Linguistics 16, 2: 79–220. 

Charniak, E. (1996). Statistical Language Learning. Cambridge, MA: MIT Press. 

Charniak, E. (2001). Immediate-head Parsing for Language Models. In N. Reithinger and 

G. Satta, Proceedings of the 39th Annual Meeting of the Association for Computational 

Linguistics (pp. 116–23). Toulouse: ACL. 

Chomsky, A. N. (1957). Syntactic Structures. The Hague: Mouton. 

Chomsky, A. N. (1981). Lectures on Government and Binding (The Pisa Lectures). 

Dordrecht and Cinnaminson, NJ: Foris. 

Chomsky, A. N. (1993). A Minimalist Program for Linguistic Theory. In K. Hale and S. 

J. Keyser (eds.), The View from Building 20: Essays in Linguistics in Honor of Sylvain 

(pp. 1–52). Cambridge, MA: Bromberger. 

Collins, M. (1999). Head-driven Statistical Models for Natural Language Parsing. PhD 

dissertation, University of Pennsylvania. 

Collins, M., J. Hajič, L. Ramshaw, and C. Tillmann (1999). A Statistical Parser for 

Czech. In R. Dale and K. Church, Proceedings of ACL 99 (pp. 505–12). College Park, 

MD: ACL. 

Gazdar, G. et al. (1985). Generalized Phrase Structure Grammar. Oxford: Blackwell. 

Grimaila, A. and J. Chandioux (1992). Made to Measure Solutions. In J. Newton (ed.), 

Computers in Translation, A Practical Appraisal (pp. 33–45). New York: Routledge. 

Jelinek, F. (2000). Statistical Methods for Speech Recognition. Cambridge, MA: MIT 

Press. 

Jurafsky, D. and J. H. Martin (2000). Speech and Language Processing. New York: 

Prentice Hall. 

Kaplan, R. M., and J. Bresnan (1983). Lexical-functional Grammar: A Formal System for 

Grammatical Representation. In J. Bresnan, The Mental Representation of Grammatical 

Relations (pp. 173–381). Cambridge, MA: MIT Press. 

Manning, C. D. and H. Schütze (1999). Foundations of Statistical Natural Language 

Processing. Cambridge, MA: MIT Press.

Papineni, K., S. Roukos, T. Ward, and Wei-Jing Zhu (2001). Bleu: A Method for 

Automatic Evaluation of Machine Translation. Published as IBM Report RC22176. 

Yorktown Heights, NY: IBM T. J. Watson Research Center. 

Pollard, C. and I. Sag (1992). Head-driven Phrase Structure Grammar. Chicago: 

University of Chicago Press. 

Saussure de, F. (1949). Court de linguistique générale, 4th edn. Paris: Libraire Payot. 

Shannon, C. (1948). The Mathematical Theory of Communication. Bell Systems 

Technical Journal 27: 398–403. 

Tesnière, L. (1959). Elements de Syntaxe Structural. Paris: Editions Klincksieck. 

Wheeler, P. J. (1984). Changes and Improvements to the European Comission's 

SYSTRAN MT System, 1976–1984. Terminologie Bulletin 45: 25–37. European 

Commission, Luxembourg. 

Zarechnak, M. (1979). The History of Machine Translation. In B. Henisz-Dostert, R. 

Ross Macdonald, and M. Zarechnak (eds.), Machine Translation. Trends in Linguistics: 

Studies and Monographs, vol. 11 (pp. 20–8). The Hague: Mouton. 

8. 

Literary Studies 

Thomas Rommel 

The systematic study and analysis of literature dates back to the beginnings of literary 

"text production"; even the earliest forms of oral literature were practiced in a context of 

descriptive and prescriptive aesthetics. With the rise of written literature emerged a canon 

of rules that could be applied to text in order to evaluate its adherence to poetic norms 

and values, and very soon quantitative and qualitative methods of text analysis were 

applied in textual exegesis. But the analysis of literature is traditionally seen as a 

subjective procedure. Objectivity, based on empirical evidence, does not seem to figure 

prominently in studies that elucidate meaning from literary texts. In most studies, 

however, some kind of exemplary textual sampling does take place, and scholars 

occasionally arrive at value judgments that are based on the observation of frequent 

occurrences or the absence of certain textual features. The exact number of occurrences 

and/or their distribution in long texts is difficult to establish, because literary texts, in 

particular novels, make a thorough analysis of every single word or sentence almost 

impossible. Empirical evidence that is truly representative for the whole text is extremely 

difficult to come by, and mainstream literary scholarship has come to accept this 

limitation as a given fact:

A simultaneous possession by the reader of all the words and images of Middlemarch), À 

la recherche du temps perdu, or Ulysses may be posited as an ideal, but such an ideal 

manifestly cannot be realized. It is impossible to hold so many details in the mind at 

once. 

(Miller 1968: 23) 

The first computer-assisted studies of literature of the 1960s and 1970s used the potential 

of electronic media for precisely these purposes – the identification of strings and 

patterns in electronic texts. Word lists and concordances, initially printed as books but 

later made available in electronic format, too, helped scholars come to terms with all 

occurrences of observable textual features. The "many details", the complete sets of 

textual data of some few works of literature, suddenly became available to every scholar. 

It was no longer acceptable, as John Burrows pointed out, to ignore the potential of 

electronic media and to continue with textual criticism based on small sets of examples 

only, as was common usage in traditional literary criticism: "It is a truth not generally 

acknowledged that, in most discussions of works of English fiction, we proceed as if a 

third, two-fifths, a half of our material were not really there" (Burrows 1987: 1). 

Literary computing was seen to remedy this shortcoming, and it has provided substantial 

insights into some questions of style and literary theory. Most studies on patterns and 

themes that were published in the last twenty years question concepts of text and method, 

and by investigating literature with the help of a powerful tool these studies situate 

themselves in a context of meta-discourse: the question of "method" remains at the heart 

of most electronic analysis of literature. 

Seen as a mere tool without any inherent analytical power of its own, the computer in 

literary studies enhances the critic's powers of memory electronically, thereby providing 

a complete database of findings that meet all predefined patterns or search criteria. As 

error-prone manual sampling becomes obsolete, textual analysis as well as the ensuing 

interpretation of a text as a whole can be based on a complete survey of all passages that 

promise results, no matter how long the text is. Comparative approaches spanning large 

literary corpora have become possible, and the proliferation of primary texts in electronic 

form has contributed significantly to the corpus of available digital texts. In order to be 

successful, literary computing needs to use techniques and procedures commonly 

associated with the natural sciences and fuse them with humanities research, thereby 

bringing into contact the Two Cultures: "What we need is a principal use of technology 

and criticism to form a new kind of literary study absolutely comfortable with scientific 

methods yet completely suffused with the values of the humanities" (Potter 1989: xxix). 

The history of literary computing, however, shows that only a limited number of textual 

phenomena can be analyzed profitably in the context of quantitative and qualitative 

computer-based analyses of style. These phenomena have to be linked to some surface 

features that can be identified by electronic means, usually by some form of pattern 

matching. Computers are exceptionally well suited for this kind of analysis, and only 

human intuition and insight, in combination with the raw computing power of machines

programmed to act as highly specialized electronic tools, can make some texts or textual 

problems accessible to scholars. As Susan Hockey writes: 

In the most useful studies, researchers have used the computer to find features of interest 

and then examined these instances individually, discarding those that are not relevant, 

and perhaps refining the search terms in order to find more instances. They have also 

situated their project within the broader sphere of criticism on their author or text, and 

reflected critically on the methodology used to interpret the results. 

(Hockey 2000: 84) 

The methodological implications of such approaches to literary texts accommodate 

computer-based and computer-assisted studies within the theoretical framework of 

literary-linguistic stylistics. In this context, texts are seen as aesthetic constructs that 

achieve a certain effect (on the reader) by stylistic features on the surface structure of the 

literary text. These features are sometimes minute details that the reader does not 

normally recognize individually but that nevertheless influence the overall impression of 

the text. The presence or absence of such features can only be traced efficiently by 

electronic means, and while the reader may be left with a feeling of having been 

manipulated by the text without really knowing how, the computer can work out 

distribution patterns that may help understand how a particular effect is achieved. 

"[U]nexpectedly high or low frequencies or occurrences of a feature or some atypical 

tendency of co-occurrence are, in their very unexpectedness or atypicality, noteworthy", 

Michael Toolan maintains, but then continues that "[elaborate statistical computations are 

unlikely to be illuminating in these matters of subtle textual effect" (1990: 71). This view, 

frequently expounded by scholars who see literary computing more critically, points at 

one of the central shortcomings of the discipline: in order to be acknowledged by 

mainstream criticism, computer-based literary studies need to clarify that the computer is 

a tool used for a specific result in the initial phases of literary analysis. No final result, let 

alone an "interpretation" of a text, can be obtained by computing power alone; human 

interpretation is indispensable to arrive at meaningful results. And in particular the aim of 

the investigation needs to be clarified; every "computation into criticism", to use 

Burrows's term, has to provide results that transcend the narrow confines of stylostatistical 

exercises. 

As for studies of punctuation, sentence length, word length, vocabulary distribution 

curves, etc., the numbers have been crunched for about twenty years now. It is clearly 

established that the distribution of such features is not random, or normal in the statistical 

sense. The extent of such variance from the models has been measured with great 

precision. But since no one ever claimed that a literary text was a random phenomenon, 

or a statistically normal distribution, it is difficult to see the point of the exercise. 

(Fortier 1991: 193) 

Statistics, in conjunction with quantifiable data and a (supposedly) positivistic attitude 

toward textual phenomena, have contributed to the image of computer-based literary

analysis as a "difficult" or "marginal" pursuit. And in the context of a shift away from 

close reading toward a more theory-oriented approach to literary texts, new models of 

textuality seemed to suggest that literary computing was occupied with fixed meanings 

that could be elucidated by counting words and phrases. This image was further enhanced 

by references to this procedure in literature itself, as David Lodge shows in his novel 

Small World: 

"What's the use? Let's show him, Josh." And he passed the canister to the other guy, who 

takes out a spool of tape and fits it on to one of the machines. "Come over here", says 

Dempsey, and sits me down in front of a kind of typewriter with a TV screen attached. 

"With that tape", he said, "we can request the computer to supply us with any information 

we like about your ideolect." "Come again?" I said. "Your own special, distinctive, 

unique way of using the English language. What's your favourite word?" "My favourite 

word. I don't have one." "Oh yes you do!" he said. "The word you use most frequently." 

The simplistic view of computer-based studies as "counting words" has been a major 

factor for later studies that were seen in this light. Contrary to received opinion, studies of 

literature that use electronic means are mostly concerned with questions of theory and 

method. Especially the notion of what constitutes a "text" and how, therefore, a given 

theory of text influences the procedures of analysis and interpretation, form the basis of 

every literary analysis. 

A literary text, interpreted as an aesthetic construct that achieves a certain effect through 

the distribution of words and images, works on various levels. Without highly elaborate 

thematic – and therefore by definition interpretative – markup, only surface features of 

texts can be analyzed. These surface features are read as significant in that they influence 

the reader's understanding and interpretation of the text. This has a number of theoretical 

implications: if a literary text carries meaning that can be detected by a method of close 

reading, then computer-assisted studies have to be seen as a practical extension of the 

theories of text that assume that "a" meaning, trapped in certain words and images and 

only waiting to be elicited by the informed reader, exists in literature. By focusing 

primarily on empirical textual data, computer studies of literature tend to treat text in a 

way that some literary critics see as a reapplication of dated theoretical models: 

One might argue that the computer is simply amplifying the critic's powers of perception 

and recall in concert with conventional perspectives. This is true, and some applications 

of the concept can be viewed as a lateral extension of Formalism, New Criticism, 

Structuralism, and so forth. 

(Smith 1989: 14) 

Most studies, both quantitative and qualitative, published in the context of literary 

humanities computing after powerful desktop computers became available, tend to 

prioritize empirical data, either in the form of automatically extracted stylistic features, or 

as encoded thematic units that are then quantified, mapped, and interpreted.

Most suitable for this kind of literary analysis are studies of repeated structures in texts. 

These are usually characters, syllables, words, or phrases that reappear throughout a text 

or a collection of texts. These repetitions are frequently recognized by readers as 

structural devices that help segment a text, or link passages in texts. Chapters, characters, 

locations, thematic units, etc., may thus be connected, parallels can be established, and a 

systematic study of textual properties, such as echoes, contributes substantially to the 

understanding of the intricate setup of (literary) texts. This type of analysis is closely 

linked to theoretical models of intertextuality used in non-computer-based literary 

studies, and here the impact of electronic procedures is felt most acutely. Repetitions and 

echoes can be traced throughout a text in a consistent fashion; it takes, however, a sound 

theoretical model that allows one, first to identify, and then to isolate common formal 

properties of these textual units. The criterion of reliability and verifiability of results and 

findings is all-important in studies of repeated structures, and maps of distribution and 

significant presences and absences of textual features are used as the basis for a more 

detailed analysis. In this area computer-assisted approaches substantially contributed to 

the understanding of literary texts, and electronic studies of literary texts provided 

empirical evidence for the analysis of a broad range of intertextual features. 

The methodological problems connected with this kind of approach feature prominently 

in nearly all electronic studies of literary texts: how does traditional criticism deal with 

formal properties of text, and where do electronic studies deviate from and/or enhance 

established techniques. Most computer-assisted studies of literature published in the last 

twenty years examine their own theoretical position and the impact of formal(ized) 

procedures on literary studies very critically. Nearly all come to the conclusion that 

rigorous procedures of textual analysis are greatly enhanced by electronic means, and that 

the basis for scholarly work with literary texts in areas that can be formalized is best 

provided by studies that compile textual evidence on an empirical basis. 

The concept of rigorous testing, ideally unbiased by personal preferences or interpretation 

by the critic, relies on the assumption that textual properties can be identified and isolated 

by automatic means. If automatic procedures cannot be applied, stringent procedures for 

the preparation of texts have to be designed. It has been, and still is, one of the particular 

strengths of most electronic studies of literature that the criteria used in the process of 

analysis are situated in a theoretical model of textuality that is based on a critical 

examination of the role of the critic and the specific properties of the text. 

These textual properties often need to be set off against the rest of the text, and here 

markup as the most obvious form of "external intervention" plays a leading role. The 

importance of markup for literary studies of electronic texts cannot be overestimated, 

because the ambiguity of meaning in literature requires at least some interpretative 

process by the critic even prior to the analysis proper. Words as discrete strings of 

characters, sentences, lines, and paragraphs serve as "natural" but by no means value-free 

textual segments. Any other instance of disambiguation in the form of thematic markup is 

a direct result of a critic's reading of a text, which by definition influences the course of 

the analysis. As many computer-based studies have shown, laying open one's criteria for 

encoding certain textual features is of prime importance to any procedure that aspires to

produce quantifiable results. The empirical nature of the data extracted from the 

electronic text and then submitted to further analysis allows for a far more detailed 

interpretation that is indeed based on procedures of close reading, and this "new critical 

analyse de texte, as well as the more recent concepts of inter-textuality or Riffaterrian 

micro-contexts can lead to defensible interpretations only with the addition of the rigour 

and precision provided by computer analysis" (Fortier 1991: 194). 

As the majority of literary critics still seem reluctant to embrace electronic media as a 

means of scholarly analysis, literary computing has, right from the very beginning, never 

really made an impact on mainstream scholarship. Electronic scholarly editions, on the 

contrary, are readily accepted in the academic community and they are rightly seen as 

indispensable tools for both teaching and research. But even the proliferation of 

electronic texts, some available with highly elaborate markup, did not lead to an 

increasing number of computer-based studies. 

This can no longer be attributed to the lack of user-friendly, sophisticated software 

specifically designed for the analysis of literature. If early versions of TACT, Word- 

Cruncher, OCP, or TuStep required considerable computing expertise, modern versions 

of these software tools allow for easy-to-use routines that literary scholars without 

previous exposure to humanities computing can master. In addition, dedicated scholarly 

software has become very flexible and allows the user to dictate the terms of analysis, 

rather than superimpose certain routines (word lists, concordances, limited pattern 

matching) that would prejudice the analysis. 

Early computer-based studies suffered greatly from hardware and software constraints, 

and as a result software tools were developed that addressed the specific requirements of 

scholarly computing. Although these tools proved remarkably effective and efficient 

given that the hardware available for humanities computing was rather slow and basic, it 

still took considerable expertise to prepare electronic texts and convert them into 

machine-readable form. As no standardized form of encoding existed until the Text 

Encoding Initiative (TEI) was formed, most scholars adopted some system of markup 

that reflected their particular research needs. Initially, these systems were nonstandardized, 

but later the majority of studies used COCOA tags for markup, but these 

systems for the scholarly encoding of literary texts needed to be adjusted to the specific 

software requirements of the programs used for the analysis. Accessing the results of 

computer-assisted studies in the form of printouts was equally cumbersome, and any 

statistical evaluation that extended the range of predefined options of standard software 

would have to be designed specifically for every individual application. Visualization, the 

plotting of graphs, or the formatting of tables required considerable expertise and 

expensive equipment and was thus mostly unavailable to one-person projects. 

In the light of these technical difficulties it seemed that once hardware limitations no 

longer existed and the computing infrastructure was up to the demands of scholarly 

computing, the electronic analysis of literature would become a major field of research. 

Methodological problems addressed in studies that wanted to but could not, for technical 

reasons, attempt more demanding tasks that required large sets of data, access to a

multitude of different texts and enough computing power to scan long texts for strings, 

for example, seemed a direct result of technical limitations. 

But the three basic requirements, seen as imperative for eventually putting literary 

computing on the map of mainstream scholarship, have been met since the early 1960s 

and 1970s: 

• virtually unlimited access to high-quality electronic texts; 

• sophisticated software that lets the user define the terms of analysis rather than vice 

versa; 

• powerful computing equipment that supplies unlimited computing power and storage 

capacity. 

Despite impressive advances in both hardware and software development, and although 

electronic texts with markup based on the TEI guidelines have become available on the 

net, literary computing still remains a marginal pursuit. Scholarly results are presented at 

international conferences organized by the Association for Literary and Linguistic 

Computing (ALLC) and the Association for Computers and the Humanities (ACH) that 

are designed to inform humanists with a background in the discipline. The results are 

published in scholarly journals (L&LC, Literary and Linguistic Computing;, and CHum, 

Computers and the Humanities) but rarely make an impact on mainstream scholarship. 

This dilemma has been commented on repeatedly: Thomas Corns, Rosanne Potter, Mark 

Olsen, and Paul Fortier show that even the most sophisticated electronic studies of 

canonical works of literature failed to be seen as contributions to the discourse of literary 

theory and method. Computer-based literary criticism has not "escaped from the ghetto of 

specialist periodicals to the mainstream of literary periodicals", Corns writes, and 

continues that the "tables and graphs and scattergrams and word lists that are so 

characteristic of computer-based investigation are entirely absent from mainstream 

periodicals" (Corns 1991: 127). 

One reason for this, apart from a general aversion to all things electronic in traditional 

literary criticism, is described by Jerome McGann as the notion of relevance, because 

the general field of humanities education and scholarship will not take the use of digital 

technology seriously until one demonstrates how its tools improve the ways we explore 

and explain aesthetic works – until, that is, they expand our interpretational procedures. 

(McGann 2001: xii) 

It is important that computer-assisted studies position themselves in the field of recent 

scholarship, take up the theoretical issues of text and textuality, and convey to the field of 

non-experts that the results merit closer inspection. Computers are not used for the sake 

of using new tools, but computers can supplement the critic's work with information that 

would normally be unavailable to a human reader. Speed, accuracy, unlimited memory, 

and the instantaneous access to virtually all textual features constitute the strength of the

electronic tool. By tapping into the ever-growing pool of knowledge bases and by linking 

texts in ways that allow them to be used as huge repositories of textual material to draw 

on, traditional literary criticism can profit substantially from the knowledge and expertise 

accumulated in the search for a more rigorous analysis of literature as practiced in 

computer-based studies. 

By looking at the history of literary computing, however, one cannot fail to see that most 

contributions add significant insight in a very narrow spectrum of literary analysis – in 

the area of stylistic studies that focus on textual features. The input of computing in these 

studies is limited to the preparation and preparatory analysis of the material under 

consideration. No immediate result, of course, can be obtained by the computer, but data 

are collected that allow for and require further analysis and interpretation by the 

researcher. The results, however, are impressive. Numerous studies of individual, and 

collections of, texts show that empirical evidence can be used productively for literary 

analysis. The history of literary computing shows that the field itself is changing. Stylostatistical 

studies of isolated textual phenomena have become more common, even if the 

computing aspect does not always figure prominently. More and more scholars use 

electronic texts and techniques designed for computing purposes, but the resulting studies 

are embedded in the respective areas of traditional research. The methods, tools, and 

techniques have thus begun to influence literary criticism indirectly. 

Right from the very beginning, humanities computing has always maintained its multidimensional 

character as far as literary genre, socio-cultural context and historicgeographical 

provenance of literary texts is concerned. Studies have focused on poetry, 

drama, and narrative from antiquity to the present day. Although an emphasis on 

literature in English can be observed, texts in other languages have also been analyzed. 

The variety of approaches used to come to terms with heterogeneous textual objects, the 

multitude of theoretical backgrounds and models of literature brought to bear on studies 

that share as a common denominator neither one single technique nor one "school of 

thought", but the application of a common tool, are the strong points of studies of 

literature carried out with the help of the computer. Discussions of literary theory, 

textuality, and the interdisciplinary nature of computer-assisted literary analysis feature 

prominently in modern studies. In this respect, mainstream literary criticism is most open 

to contributions from a field that is, by its very nature, acutely aware of its own 

theoretical position. In the future, the discourse of meta-criticism, however, may be fused 

with innovative approaches to literary texts. As Jerome McGann points out: 

A new level of computer-assisted textual analysis may be achieved through programs that 

randomly but systematically deform the texts they search and that submit those 

deformations to human consideration. Computers are no more able to "decode" rich 

imaginative texts than human beings are. What they can be made to do, however, is 

expose textual features that lie outside the usual purview of human readers. 

(McGann 2001: 190–1) 


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Burrows, J. F. (1987). A Computation into Criticism. A Study of Jane Austen's Novels and 

an Experiment in Method. Oxford: Oxford University Press. 

Burrows, J. F. (1992). Computers and the Study of Literature. In C. S. Butler (ed.), 

Computers and Written Texts (pp. 167–204). Oxford: Blackwell. 

Busa, R. (1992). Half a Century of Literary Computing: Towards a "New" Philology. 


Corns, T. N. (1991). Computers in the Humanities: Methods and Applications in the 

Study of English Literature. Literary and Linguistic Computing 6: 127–30. 

Feldmann, D., F.-W. Neumann, and T. Rommel, (eds.) (1997). Anglistik im Internet. 

Proceedings of the 1996 Erfurt Conference on Computing in the Humanities. Heidelberg: 

Carl Winter. 

Finneran, R. J., (ed.) (1996). The Literary Text in the Digital Age. Ann Arbor: University 

of Michigan Press. 

Fortier, P. A. (1991). Theory, Methods and Applications: Some Examples in French 

Literature. Literary and Linguistic Computing 6, 192–6. 

Fortier, P. A., (ed.) (1993–4). A New Direction for Literary Studies? Computers and the 

Humanities 27 (special double issue). 

Hockey, S. (1980). A Guide to Computer Applications in the Humanities. London: 

Duckworth. 

Hockey, S. (2000). Electronic Texts in the Humanities. Principles and Practice. Oxford: 

Oxford University Press. 

Landow, G. P. and P. Delany, (eds.) (1993). The Digital Word: Text-Based Computing in 

the Humanities. Cambridge, MA: MIT Press. 

McGann, J. (2001). Radiant Textuality: Literature After the World Wide Web. New York: 

Palgrave. 

Miall, D. S., (ed.) (1990). Humanities and the Computer: New Directions. Oxford: 


Miller, J. H. (1968). Three Problems of Fictional Form: First-person Narration in David 

Copperfield and Huckleberry Finn. In R. H. Pearce (ed.), Experience in the Novel:

Selected Papers from the English Institute (pp. 21–48). New York: Columbia University 

Press. 

Opas, L. L. and T. Rommel, (eds.) (1995). New Approaches to Computer Applications in 

Literary Studies. Literary and Linguistic Computing 10: 4. 

Ott, W (1978). Metrische Analysen zu Vergil, Bucolica. Tübingen: Niemeyer. 

Potter, R. G., (ed.) (1989). Literary Computing and Literary Criticism: Theoretical and 

Practical Essays on Theme and Rhetoric. Philadelphia: University of Pennsylvania Press. 

Renear, A. (1997). Out of Praxis: Three (Meta) Theories of Textuality. In K. Sutherland 

(ed.), Electronic Textuality: Investigations in Method and Theory (pp. 107–26). Oxford: 


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computerunterstützter Textanalyse am Beispiel von Lord Byrons Don Juan. Tübingen: 

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Routledge. 

9. 

Music 

Ichiro Fujinaga and Susan Forscher Weiss 

Introduction 

Technological advances are often accompanied by growing pains. Following the advent 

of printing, texts were produced in an effort to reach wider audiences. Many of these, in a

wide variety of subjects, especially music, were based on the most popular source 

materials. As with many other disciplines, computer applications in music have been 

dramatically affected by the advent of the Internet. The best-known phenomenon is the 

proliferation of files – mostly popular music – in compressed format known as MP3. 

There are also peer-to-peer file sharing programs such as Napster. The availability of 

sample music through online music stores such as amazon.com and barnesandnoble.com 

as well as in other specialized sites such as the Classical Music Archives or the web 

pages of professors at numerous universities around the world has changed the lives of 

scholars and music listeners in general. In addition, music notation software has 

revolutionized the music publishing industry. It allows composers to produce 

professional-quality scores and scholars to prepare critical editions more efficiently and 

economically. Optical music recognition (OMR) is still in the process of developing 

adequate techniques. Once accomplished, OMR and other newer technologies will 

redefine the way musicians are able to analyze musical scores. 

Perhaps the technology that had the most profound influence on music scholarship has 

been the availability of e-mail, discussion lists, and society listservs, providing scholars 

and lay people with opportunities to exchange ideas and materials without the delay of 

traditional modes of communication. This trend is documented as late as 1995 (Troutman 

1995). 

E-mail has provided scholars with opportunities for communication and the sharing of 

ideas. It also has paved the way for the formation of national and international 

consortiums. Some professional listservs include the AMS (American Musicological 

Society) and SMT (Society of Music Theory) lists and their many offshoots. Hundreds of 

websites provide information to scholars in various music disciplines. Online versions of 

important music reference materials include the New Grove Dictionary of Music and 

Musicians, the Repertoire International des Sources Musicales (RISM), the Repertoire 

International de Litterature Musicale (RILM), the International Index to Music 

Periodicals (IIMP), the Music Index, Musical America, and Thesaurus Musicarum 

Latinarum, among countless others. 

The following review of computer applications in music scholarship will focus on the last 

decade, examining developments in a number of areas including notation software, MIDI 

(Musical Instrument Data Interchange), and MP3, databases, specialized resources, 

computer applications for music scholars, and music information retrieval (MIR) 

systems. For previous reviews of the discipline, see Alphonce (1989), Davis (1988), Bent 

and Morehen (1978), and Hewlett and Selfridge-Field (1991). 

Notation Software 

Music notation software has been developed over the course of the last two decades of 

the twentieth century, now allowing composers to prepare their own music without the 

necessity of hiring professional copyists. Before the advent of this technology, individual 

parts were manually copied from the score. Large ensemble pieces that require a score for 

the conductor as well as parts for individual musicians can now be produced in record

time and at a fraction of the cost. Once the master score is entered into the computer, the 

parts can be generated automatically with a minimal amount of manual editing. This 

software is also widely used in the music publishing industry and allows musicologists to 

produce scholarly editions of works much more accurately, efficiently, and economically 

than before. 

One drawback, and an explanation for the lack of widespread use, is the absence of 

standard music representation format, either as de facto proprietary (equivalent to Word 

in the text world) or as an open standard, such as ASCII. Despite numerous efforts since 

the advent of computer applications in music to create such a format (Hewlett and 

Selfridge-Field 1991), no such standard has emerged. Some may argue that the 

proprietary format of the very popular music notation software Finale is becoming a 

standard, but others contend that another proprietary format of an increasingly popular 

competitor called Sibelius may become the standard of the future. Even if one or both of 

these become the standard, without some open formats, equivalent to ASCII or RTF, 

these files will be too cumbersome for other purposes such as searching and analysis. 

Optical Music Recognition 

One way to create a computer-readable format of music notation is to use optical music 

recognition software, similar to optical character recognition technology. A variety of 

research protocols are being tested and commercial software is becoming available, but 

the accuracy of the recognition is highly dependent on the type and quality of the original 

document and on the complexity of the music. Newer techniques are being applied that 

will enable the recognition of lute tablature, medieval music notation, and music notation 

systems from other cultures (Selfridge-Field 1994b). 

MIDI and MP3 

Outside of music notation, there are two open formats for music that have emerged as 

standards: MIDI and MP3. Musical Instrument Data Interchange (MIDI) was developed 

by a consortium of music synthesizer manufacturers in the early 1980s. It has become 

extremely widespread, becoming the de facto standard for popular music and the world 

standard for exchanging information between electronic musical instruments and music 

software. 

Originally designed to capture the gestures of keyboard performers, it does have some 

limitations. A MIDI file contains data as to when, which, and how fast a key was 

depressed. It does not contain the timbre of the instrument or much notational 

information such as the precise pitch of a note (A♯ or B♭) or which notes are grouped 

together. Nevertheless, a large amount of music is available in MIDI format on the 

Internet and in commercial classical piano music recordings. (There are also acoustic 

pianos that can play back MIDI files.)

In general, most traditional music scholars base their research on scores. The majority of 

analytical methods and theories are dependent on notated music. Thus, the lack of crucial 

notational information in MIDI files renders this format less useful for scholarly work. 

The recent explosion in the proliferation of MP3 files can be explained as a convergence 

of various factors. Among these is the standardization of audio format, a higher 

bandwidth access to the Internet, a dramatic increase in computing power on the desktop, 

and the invention of clever compression methods. 

The representation of digital audio (as sound) was firmly established in the 1980s by the 

invention of compact discs (CDs). The standard requires that the sound is captured 

(digitized) 44,100 times per second and each sample is stored as a 16-bit integer. 

Although there are a number of competing file formats for audio, these are not difficult to 

interchange because they are basically a series of numbers. The storage required for one 

minute of stereo audio is about 10 MB (megabytes). Even with a relatively high-speed 

modem (28.8 KB), it takes a couple of hours to download a pop song of three minutes (30 

MB). The MP3 compression technology reduces the size of the audio files to up to onehundredth 

of its original size. Finally, a computer powerful enough to decompress and 

play back the downloaded MP3 file was needed. Home computers in the late 1980s could 

barely play back a CD-quality stereo audio. Thanks to an almost hundredfold increase in 

the computing power of desktop computers within the last decade, today almost any 

computer can download, decompress, and play back music while the users perform other 

tasks. 

Databases 

General bibliographical databases 

The searchable online bibliographical databases are among the best computer tools for all 

music scholars in the last decade. Two of the most useful and important bibliographical 

databases for music scholars are RILM (Repertoire International de Litterature Musicale) 

and Music Index. RILM Abstracts of Music Literature contains over 275,000 entries from 

over 500 scholarly periodicals from 60 countries with 140 languages and includes 

original-language titles; title translations in English; full bibliographic information; and 

abstracts in English. The Music Index: A Subject-Author Guide to Music Periodical 

Literature covers 650 publications from 1979 and covers a wider range of publications 

including popular music magazines. Both of these databases are subscription-based, but 

because of their importance they are available in most music libraries. International Index 

to Music Periodicals (IIMP) is a relatively new subscription service that indexes more 

than 370 international music periodicals with over 60 full-text titles. It also includes 

retrospective coverage from over 185 periodicals dating back as far as 1874. 

The most important music reference in English has been the Grove's Dictionaries for 

generations of music scholars. Now both The New Grove Dictionary of Music and 

Musicians and The New Grove Dictionary of Opera are available online and have greatly 

enhanced the usability of these valuable sources.

Also, The Concise Oxford Dictionary of Music, The Concise Oxford Dictionary of Opera, 

and Who's Who in Opera are now part of the Oxford Reference Online. The utility of this 

system is that items can be searched in other Oxford dictionaries. For example, query of 

"Beethoven" produces a quote by E. M. Forster: "It will be generally admitted that 

Beethoven's Fifth Symphony is the most sublime noise that has ever penetrated into the 

ear of man" (Howards End [1910], from The Oxford Dictionary of Twentieth Century 

Quotations). 

Other major resources include RISM (Répertoire International des Sources Musicales). 

"Music manuscripts after 1600" is an annotated index and guide to music manuscripts 

produced between 1600 and 1850 containing more than 380,000 works by over 18,000 

composers found in manuscripts over 595 libraries and archives in 31 countries. The 

music manuscript database contains over 506,000 searchable musical incipits, which can 

be viewed as musical scores. 

RIPM (Répertoire International de la Presse Musicale) contains over 410,000 annotated 

records from 127 volumes of nineteenth- and twentieth-century music periodicals from 

over 15 countries. Another important source is Doctoral Dissertations in Musicology 

(DDM) – Online, which is a database of bibliographic records for completed dissertations 

and new dissertation topics in the fields of musicology, music theory, and ethnomusicology, 

as well as in related musical, scientific, and humanistic disciplines. Available since 

1996, it contains more than 12,000 records. 

IIPA (International Index to the Performing Arts) Full Text contains 182,550 records 

drawn from over 210 periodicals and 33 full-text titles. It also contains retrospective 

coverage with citations dating back to 1864. 

The Canadian Music Periodical Index, although restricted to Canadian music periodicals, 

should be mentioned because, unlike other databases mentioned above, it is free. A 

service provided by the National Library of Canada, it includes nearly 30,000 entries on 

articles dating from the late nineteenth century to the present day. Some 500 Canadian 

music journals, newsletters, and magazines are represented here, almost 200 of which are 

currently active and continue to be indexed. Another free service provided by the 

National Library of Canada is an online Encyclopedia of Music in Canada, available both 

in English and French. 

Music databases 

The Scribe Medieval Music Database (La Trobe University) is a collection of 6,000 

musical scores, color images, texts, and bibliographic information on medieval music, 

searchable by text or melody. This includes the complete annual cycle of liturgical chant 

taken from original medieval sources, and the complete works of selected composers 

from the twelfth to the fifteenth centuries (Stinson 1992). 

CANTUS database, started at Catholic University in the 1980s and now hosted at the 

University of Western Ontario, contains indices of Latin ecclesiastical chants in over 70

selected manuscripts and early printed sources of the liturgical Office. It is searchable by 

first few words, keywords, chant identification number, or liturgical occasion. Further 

downloadable chant resources can be found at Cantus Planus Archiv at the University of 

Regensburg. 

In development since 1989, Thesaurus Musicarum Latinarum (TML) is a full-text 

searchable database (with ASCII text and associated image files) containing a large 

corpus of Latin music theory written during the Middle Ages and the Renaissance. A 

similar database of texts on Italian music theory and aesthetics is Saggi Musicali Italiani 

(SMI) and Texts on Music in English from the Medieval and Early Modern Eras (TME). 

Another source for Italian treatises, from the Renaissance and early Baroque, is 

Thesaurus Musicarum Italicarum (TMI). One of the features of this collection, not 

available in others, is that the many terms are hyperlinked. The CD-ROM version of this 

database contains complete facsimiles and they are being made available online. 

Themefinder is a collaborative project of Stanford University and Ohio State University. 

It consists of three databases: Classical Instrumental Music (10,000 themes), European 

Folksongs (7,000 folksongs), and Latin Motets from the sixteenth century (18,000 

incipits). A web-based searching interface is provided and the matched themes are 

displayed in graphical notation (Kornstdädt 1998). 

MELDEX, a music search engine developed at the University of Waikato, New Zealand, 

forms part of the New Zealand Digital Library. The database consists of 9,400 folk 

melodies. This is one of the first search engines that allows query-by-humming, where 

search can be accomplished by providing a sung audio file online (Bainbridge 1998). 

MuseData at Stanford University is a collection of full-text databases of music for several 

composers, including J. S. Bach, Beethoven, Corelli, Handel, Haydn, Mozart, Telemann, 

and Vivaldi. It currently contains 2,461 complete movements of 634 classical pieces, 

including 185 Bach chorales (Selfridge-Field 1994a). 

For scholars working with popular music there are a variety of large databases available. 

For MIDI data searchable by title and artist, there are sites that claim to index over 1 

million MIDI files and 95,000 lyrics. Within classical music there are sites with over 

20,000 MIDI files. 

One can also find opera librettos (RISM-US Libretto Database of over 13,000 libretti), 

old English ballads, or even hip-hop lyrics, where one can use "Rhymerator" to find 

rhyming words online. 

Specialized bibliography 

The Online Bach Bibliography was first launched on the Web in May 1997 by Tomita 

Yo, and ever since it has been updated regularly. At present, it contains over 18,000 

records of bibliographical references that are considered useful for the scholarly 

discussion of Bach's life and works.

The Beethoven Bibliography Database begun in 1990 currently includes 2,700 records 

for books and scores and is available from the Center for Beethoven Studies at San Jose 

State University (Elliott 1994). 

Other notable databases include: 

• OCLC (Online Computer Library Center) Music Library, which is a subset of the 

OCLC Online Union Catalogue; it provides citations to nearly 800,000 musical sound 

recordings. 

• National Sound Archive at the British Library, whose catalogue includes entries for 

almost 2.5 million recordings, published and unpublished, in all genres from pop, jazz, 

classical, and world music, to oral history, drama and literature, dialect, language, and 

wildlife sounds. 

• SONIC of Library of Congress. The Library of Congress Recorded Sound Collection 

contains some 2.5 million audio recordings including multiple copies. It currently has 

350,000 bibliographic records representing roughly 25 percent of the Library's entire 

sound recording holding of, among others, 78s, 45s, and radio broadcasts. 

• Ufficio Ricerca Fondi Musicali di Milano is a national catalogue of music manuscript 

and printed scores to 1900, containing over 300,000 entries (Gentili-Tedeschi 2001). 

Sheet music 

There are many large collections of North American sheet music available on the Web. 

Most of these are scanned images including the cover artwork and the musical score, with 

varying amount of metadata; for example, some have subjects for the cover art, and some 

have partial lyrics. For example, see the Library of Congress 1820–1885 (62,500 pieces 

registered at the Library for copyright), Brown University 1850–1920 (1,305 pieces of 

African-American sheet music), Duke University 1850–1920 (3,042 pieces), the Lester 

Levy Collection of Sheet Music at the Johns Hopkins University 1780–1960 (29,000 

pieces), Mississippi State University (22,000 pieces), University of California at Berkeley 

1852–1900 (2,000 pieces published in California). The next step for these collections is 

to provide audio, and full-text/music searching capabilities. In order to achieve these 

goals, automated optical music recognition system would be required. 

Computer Application 

Perhaps the most intensive utilization of computers in music is by composers who use 

them for various functions. The machines may be used to create new sounds, create new 

instruments and interfaces, imitate existing instruments with fine control, generate new 

composition, or train computers to listen to music for interaction with human performers. 

Other research activities in this field include: pitch recognition, tempo/beat induction, and 

expressive performance, where the computer attempts to imitate musicians. For thorough 

introduction and history in this area, see the excellent book by Roads (1996). Similar

esearch topics are also investigated by music psychologists and music educators, where 

computers are used to model human musical cognition and abilities. Educators are also 

interested in using computers to aid in music teaching. Music theorists have also actively 

used computers for analysis of notated music. Applications include melodic and 

harmonic analysis, key-finder, segmentation, and development of interactive graphic 

analysis tools. 

Music historians have used computers for a range of tasks. Samples of published works 

can be divided by subject area. Medieval and Renaissance applications include examples 

of computer-assisted examination and analysis of notational signs in twelfth-century 

manuscripts (Loos 1996), of the oral transmission of Old Roman chant (Haas 1997), and 

a computer program to compare a number of motets with questionable attributions 

(Thomas 1999). There are a number of computer analyses of motets by William Byrd and 

other English composers in an effort to confirm authorship (see Morehen 1992; Wulstan 

1992, 1995). There are also systematic studies of vertical sonorities and melodic features 

of works by Italian Renaissance theorists and composers (Moll 1996). One example took 

Palestrina's Masses from the Casimiri edition, entered them into a database without text 

using a MIDI synthesizer, examined by customized programs, and manipulated by 

spreadsheet software. Areas of investigation included prolations and contexts of note 

spans and rests, including metric and textural aspects; the distribution of pitch classes, 

including the effects of mode; and the distribution of individual pitches correlated with 

voice ranges. Conclusions revealed that Palestrina's pitch-class collection was 

conservative and common voice ranges were narrower than previously reported (Miller 

1992). 

Computer applications in Baroque music include a database, music processor, and coding 

system for analyzing the music of (Telemann Lange 1995); software to analyze modality 

versus tonality in Bach's four-part chorales (Rasmussen 1996); a dissertation that used 

statistics and computer routines to resolve conflicting attributions in works by Bach's 

sons (Knoll 1998); the use of spreadsheets for statistical analysis in text critical study of 

Bach's Well-Tempered Clavier (Tomita 1993); and a computer analysis of the variable 

role of dissonance and contrapuntal techniques in Corelli Trio Sonatas (Peiper 1996). 

Studies of Classical and Romantic era composers include analysis of melodic affinities 

and identical melodic elements in music by Mozart and Siissmayr to refute claims of the 

latter's authorship of portions of Mozart's Requiem (Leeson 1995); programs to detect 

melodic patterns and search text in Schubert's lieder (Nettheim 2000; Yi 1990); and an 

analytical comparison, based on statistics, using relational database and spreadsheet 

software, of the final movements of Brahms's symphonies nos. 3 and 4 and Bruckner's 

symphonies nos. 3 and 4, with findings that contradict a number of related previous 

studies (McArtor 1995). 

Musicologists and theorists are applying computer programs to study tonal, atonal, and 

post-tonal music of the last century. An example is a study of excerpts from Bartók's Ten 

Easy Pieces in which the key is ambiguous. A program was created to determine the key

(as recorded in MIDI) on the basis of the relative frequency and duration of pitch classes 

(Cooper 1998). 

In the area of musical images and iconography, work has been done with the superimposition 

of divergent sixteenth- and seventeenth-century prints, such as those in the 

William Byrd edition (Brett and Smith 2001). Other studies focusing on images include 

one that describes some post-processing procedures for scanned images in re-establishing 

the content of medieval sources for sacred vocal music preserved in the Vatican Library 

(Planchart 2001). 

Another, the Digital Image Archive of Medieval Music (DIAMM), provides a new 

resource for scholars desiring to digitize, archive, and make available images of primary 

sources and to develop techniques of digital image enhancement, or "virtual restoration", 

to retrieve lost data or improve the legibility of materials that cannot at present be read 

(Wathey et al. 2001). 

Another area of research concerns the transcription of tablatures. Software has been 

developed to facilitate the assignment of voices when transcribing tablature, lute and 

guitar, into modern notation (Charnasse 1991; Kelly 1995; Derrien-Peden et al. 1991). 

Performance practice is an area in need of further investigation. There is one study that 

automatically assigns proper fingering for English keyboard music (1560–1630), 

examining sixty surviving manuscripts, half of which contain fingerings. Ten important 

works with fingerings are stored and analyzed and then applied to those without 

fingerings (Morehen 1994). Composer sketches is another area just beginning to benefit 

from the application of digital techniques. A recent study describes photographic 

techniques used in an electronic facsimile of the sketches for Alban Berg's Wozzeck. By 

showing various stages of sketches for one scene from this opera, one of the most 

prominent works of the twentieth century, the paper suggests typical uses of an electronic 

facsimile for a large body of sketches. The archaeological nature of the task involves the 

capture of pencil drafts and paper paste-overs as well as the penetration of lacquer 

occlusions (Hall 2001). 

Perhaps one of the most exciting of the recent articles in Computing in Musicology is a 

study of watermarks by Dexter Edge. The traditional methods used by music historians 

for imaging watermarks are problematic. Freehand tracing is inherently prone to 

inaccuracy, the Dylux method often seems ineffective with musical manuscripts, and 

beta-radiography has become prohibitively expensive. Using a scanner equipped with a 

transparency adapter and manipulating the resulting images, Edge is able to create digital 

images of watermarks with good results. 

Folksong is one category that has inspired a number of studies. A paper on the mapping 

of European folksong discusses issues related to encoding, displaying, and analyzing 

geographical information pertaining to music (Aarden and Huron 2001). Other studies 

examine pulse in German folksong, melodic arch in Western folksongs, and contour 

analysis of Hungarian folk music (Nettheim 1993; Huron 1996; Juhasz 2000; Pintér

1999). For further information on application in musicology see Nettheim (1997) and 

Selfridge-Field (1992). 

Music Information Retrieval 

As more and more music is becoming available on the Internet, it has become evident 

that there is a need for a method of searching for the required music information. 

Although this branch of research is in its initial stage, it is growing rapidly. With 

increased interest in digital libraries, in general, preservation and retrieval of musical 

material both new and old is necessary. Although searching for textual information is 

becoming easier, as with image searching, searching for music, in audio format or in 

notated format, is still difficult (Byrd and Crawford 2001). 

Topics in this field include: query-by-humming, automatic genre and style classification, 

music indexing, melodic similarity, and automatic transcription. The latter, especially 

transcribing polyphonic music, remains the "holy grail" of audio engineers. 

The copyright issue is still under intense debate (Anthony et al. 2001). 

Although most of these issues have been the academic concerns of music scholars, there 

is also an ever-increasing commercial interest in knowing more about music. Record 

companies are eager for buyers to find music more easily and for the companies to be 

able to classify musical styles and genre so that they can match listeners' preferences. To 

accomplish this, companies are asking questions such as "What is a melody or theme?" 

and "Where do they occur?" Knowing the answers will aid in discovering new music that 

a consumer would like. Thus, with funding from the commercial sector, there may now 

be additional research opportunities for music investigators. 

Conclusions 

This is a very exciting time for music. Technology is making new sounds and 

unprecedented access to music. As more and more musical data become readily available, 

musical research methodology may undergo a fundamental change. As David Huron 

hints, the discipline will go from a "data-poor" field to a "data-rich" field (Huron 1999). 

As of now, significant research has been limited to subjects such as composer 

attributions, image enhancements, determination of provenance based on watermarks, 

and some notational studies. More needs to be done. Also, with pressure from music 

information retrieval (MIR) areas, there may be greater need for scholars from different 

musical disciplines to work together to solve a variety of musical puzzles. Whether it is 

for musicologists and theorists or members of the business and lay communities, we will 

need to develop more sophisticated analytical technologies and programs for search and 

retrieval of an ever-expanding mass of information and sound. 


Aarden, B. and D. Huron (2001). Mapping European Folksong: Geographical 

Localization of Musical Features. Computing in Musicology 12: 169–83. 

Alphonce, B. (1989). Computer Applications in Music Research: A Retrospective. 

Computers in Music Research 1: 1–74. 

Anthony, D., C. Cronin, and E. Selfridge-Field (2001). The Electronic Dissemination of 

Notated Music: An Overview. Computing in Musicology 12: 135–66. 

Bainbridge, D. (1998). MELDEX: A Web-based Melodic Locator Service. Computing in 

Musicology 11: 223–9. 

Bent, I. and J. Morehen (1978). Computers in Analysis of Music. Proceedings of the 

Royal Society of Music 104: 30–46. 

Brett, P. and J. Smith (2001). Computer Collation of Divergent Early Prints in the Byrd 

Edition. Computing in Musicology 12: 251–60. 

Byrd, D. and T. Crawford (2001). Problems of Music Information Retrieval in the Real 

World. Information Processing and Management 38, 2: 249–220. 

Charnassé, H. (1991). ERATTO Software for German Lute Tablatures. Computing in 


Cooper, D. (1998). The Unfolding of Tonality in the Music of Béla Bartók. Music 

Analysis 17, 1: 21–220. 

Davis, D. S. (1988). Computer Applications in Music. Madison, WI: A-R Editions. 

Derrien-Peden, D., I. Kanellos, and J.-F. Maheas (1991). French Sixteenth-century Guitar 

Tablatures: Transcriptions and Analysis. Computing in Musicology 7: 62–4. 

Edge, D. (2001). The Digital Imaging of Watermarks. Computing in Musicology 12: 261– 

74. 

Elliott, P. (1994). Beethoven Bibliography Online. Computing and Musicology 9: 51–2. 

Gentili-Tedeschi, M. (2001). Il lavoro dell'Ufficio Ricerca Fondi Musicali di Milano. 

Libreria Musicale Italiana 18: 483–7. 

Haas, M. (1997). Mündliche Überlieferung und altrömischer Choral: Historische und 

analytische computergestützte Untersuchungen. Bern: Lang. 

Hall, P. (2001). The Making of an Electronic Facsimile: Berg's Sketches for Wozzeck. 

Computing in Musicology 12: 275–82.

Hewlett, W B. and Selfridge-Field, E. (1991). Computing in Musicology, 1966–91. 

Computers and the Humanities 25, 6: 381–92. 

Huron, D. (1996). The Melodic Arch in Western Folksongs. Computing in Musicology 10: 

3–23. 

Huron, D. (1999). The New Empiricism: Systematic Musicology in a Postmodern Age. 

1999 Ernest Bloch Lecture, University of California, Berkeley. 

Juhász, Z. (2000). Contour Analysis of Hungarian Folk Music in a Multidimensional 

Metric-Space. Journal of New Music Research 29, 1: 71–220. 

Kelly, W. (1995). Calculating Fret Intervals with Spreadsheet Software. American 

Lutherie 43: 46–7. 

Knoll, M. W. (1998). Which Bach Wrote What? A Cumulative Approach to Clarification 

of Three Disputed Works. PhD dissertation, University of Michigan. 

Kornstädt, A. (1998). Themefinder: A Web-based Melodic Search Tool. Computing in 


Lange, C. (1995). Die Telemann Datenbank: Aspekte der Datenspeicherung und der 

Nutzungsmöglichkeiten [The Telemann Database: Aspects of data storage and possible 

uses]. Magdeburger Telemann Studien 13: 128–44. 

Leeson, D. N. (1995). Franz Xaver Süssmayr and the Mozart Requiem: A Computer 

Analysis of Authorship Based on Melodic Affinity. Mozart-Jahrbuch: 111–53. 

Loos, I. (1996). Ein Beispiel der Computeranalyse mittelalterlicher Neumen: Das 

Quilisma im Antiphonar U 406 (12. Jh.) [An example of computer analysis of Medieval 

neumes: the Quilisma in Antiphoner U 406 (12th c.)]. Musicologica Austriaca 14–15: 

173–81. 

McArtor, M. J. (1995). Comparison of Design in the Finales of the Symphonies of 

Bruckner and Brahms. DMA thesis, Arizona State University. 

Miller, E. J. (1992). Aspects of Melodic Construction in the Masses of Palestrina: A 

Computer-assisted Study. PhD dissertation, Northwestern University. 

Moll, K. N. (1996). Vertical Sonorities in Renaissance Polyphony: A Music-analytic 

Application of Spreadsheet Software. Computing in Musicology 10: 59–77. 

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A. Martin (eds.), Byrd studies (pp). 51–62). Cambridge: Cambridge University Press.

Morehen, J. (1994). Aiding Authentic Performance: A Fingering Databank for 

Elizabethan Keyboard Music. Computing in Musicology 9: 81–92. 

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schöne Müllerin. Computing in Musicology 11: 159–64. 

Peiper, C. E. (1996). Dissonance and Genre in Corelli's Trio Sonatas: A LISP-based 

Study of Opp. 1 and 2. Computing in Musicology 10: 34–8. 

Pintér, I. (1999). Computer-aided Transcription of Folk Music. Studia Musicologica 

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10. 

Multimedia 

Geoffrey Rockwell and Andrew Mactavish 

How do we think through the new types of media created for the computer? Many names 

have emerged to describe computer-based forms, such as digital media, new media, 

hypermedia, or multimedia. In this chapter we will start with multimedia, one possible 

name that captures one of the features of the emerging genre. 

What is Multimedia? 

Thinking through a definition starts with a name. Definitions help bring into view limits 

to that about which you think. Here are some definitions of "multimedia": 

A multimedia computer system is one that is capable of input or output of more than one 

medium. Typically, the term is applied to systems that support more than one physical 

output medium, such as a computer display, video, and audio. 

(Blattner and Dannenberg 1992: xxiii) 

Blattner and Dannenberg further make the observation that "multimedia systems strive to 

take the best advantage of human senses in order to facilitate communication" (1992: 

xix). Embedded in their discussion is a view of communication where the communicator 

chooses to combine the media best suited to her communicative goals; therefore, 

multimedia, which encompasses other media, provides the greatest breadth of 

communicative possibilities.

The Encyclopaedia Britannica Online defines "Interactive Multimedia" as "any 

computer-delivered electronic system that allows the user to control, combine, and 

manipulate different types of media." In this definition the emphasis is placed on 

interactivity and the computer control over the delivery of information in different media. 

This control includes the release of control to the reader or viewer so that they can 

participate in the development of meaning through interaction with a multimedia work. 

While similar, what is interesting in these definitions is what they are defining. The first 

defines a "multimedia system" while the second specifies "interactive multimedia." This 

chapter proposes a third and shorter definition that combines many of the features in the 

others with a focus on multimedia as a genre of communicative work. 

A multimedia work is a computer-based rhetorical artifact in which multiple media are 

integrated into an interactive whole. 

We can use the parts of this definition to analyze multimedia. 

Computer-based 

The word "multimedia" originally referred to works of art that combined multiple 

traditional art media, as in a multimedia art installation. By defining multimedia as 

"computer-based" such mixed-media works are deliberately excluded. In other words, a 

multimedia work is a digital work that is accessed through the computer even if parts 

were created in analogue form and then digitized for integration on the computer. This 

definition also excludes works that might have been created on a computer, like a desktop 

publishing file, but are accessed by readers through an analogue medium like print. 

Rhetorical artifact 

A multimedia work is one designed to convince, delight, or instruct in the classical sense 

of rhetoric. It is not a work designed for administrative purposes or any collection of data 

in different media. Nor is it solely a technological artifact. This is to distinguish a 

multimedia work, which is a work of human expression, from those works that may 

combine media and reside on the computer, but are not designed by humans to 

communicate to humans. 

Multiple media 

Central to all definitions of multimedia is the idea that multimedia combines types of 

information that traditionally have been considered different media and have therefore 

had different traditions of production and distribution. Digitization makes this possible as 

the computer stores all information, whatever its original form, as binary digital data. 

Thus it is possible to combine media, especially media that are incompatible in other 

means of distribution, like synchronous or time-dependent media (audio and video) and 

asynchronous media (text and still images).

Integrated … artistic whole 

A multimedia work is not just a random collection of different media gathered 

somewhere on the system. By this definition the integration of media is the result of 

deliberate artistic imagination aimed at producing a work that has artistic unity, which is 

another way of saying that we treat multimedia as unified works that are intended by their 

creator to be experienced as a whole. Likewise, consumers of multimedia treat such 

works as integrated in their consumption. The art of multimedia consists in how you 

integrate media. 

Interactive 

One of the features of multimedia is the interactivity or the programming that structures 

the viewer's experience. Some level of interactivity is assumed in any computer-based 

work, but by this definition interactivity becomes a defining feature that helps weave the 

multiplicity into a whole. Interactivity is thus important to the artistic integrity of 

multimedia. We might go further and say that interactivity, in the sense of the 

programming that structures the work, is the form that integrates the others. 

The names given for multimedia works emphasize different characteristics of these 

works. "New media" emphasizes the experience of these works as "new" and different 

from existing forms of entertainment and instruction, but new media can also refer to 

media new to the twentieth century, including electronic (but not necessarily digital) 

media like television. "Hypermedia" evolved out of "hypertext" and emphasizes the way 

these works are multi-linear labyrinths of information that the user navigates. This name, 

however, suggests that all new media are organized as hypertexts with nodes and links, 

which is not the case for works like arcade games. While "hypermedia" is a useful term 

for those works that make use of hypertext features, "multimedia" emphasizes the 

combination of traditional media into rhetorical unities. 

Defining multimedia as a way of thinking about the new medium made possible by the 

computer runs the risk of fixing a moving target inappropriately. It could turn out that 

multimedia works are not a new form of expression, but that they are remediated forms of 

existing genres of expression (Bolter and Grusin 1999). These traditional forms, when 

represented digitally, are transformed by the limitations and capabilities of the computer. 

They can be processed by the computer; they can be transmitted instantaneously over the 

Internet without loss of quality; they can be extended with other media annotations; they 

can be transcoded from one form to another (a text can be visualized or read out as 

synthesized audio). 

The ways in which traditional media are created, distributed and consumed are also 

transformed when represented digitally. Multimedia books are not only bought at 

bookstores and read in bed, they can be distributed over the Internet by an e-text library 

for your PDA (personal digital assistant) and consumed as concordances with text 

analysis tools. In short, even if we think of multimedia as a way of digitally re-editing 

(re-encoding) traditional works, there are common limitations and possibilities to the

digital form. Multimedia works, whether born digital or remediated, share common 

characteristics including emerging modes of electronic production, distribution, and 

consumption. They can be defined as multimedia for the purposes of thinking through the 

effects of the merging of multiple media into interactive digital works to be accessed on 

the computer. 

What are the Types of Multimedia? 

Classifying is a second way of thinking through multimedia, and one that involves 

surveying the variety of the phenomena. It is also a common move in any discussion of 

multimedia to give examples of these types of multimedia, especially to make the point 

that these types are no longer academic experiments inaccessible to the everyday 

consumer. The challenge of multimedia to the humanities is thinking through the variety 

of multimedia artifacts and asking about the clusters of works that can be aggregated into 

types. Here are some examples: 

Web hypermedia 

The first multimedia works to be considered seriously in humanities computing circles 

were hypertexts like The Dickens Web by George P. Landow, a work created to explore 

the possibilities for hypertext and multimedia in education. It was an exemplary 

educational hypertext that illustrated and informed Landow's theoretical work around 

hypertext theory. With the evolution of the World Wide Web as a common means for 

distributing and accessing hypertextual information, we now have thousands of 

educational and research Web hypertexts, some of which combine multiple media and 

can be called hypermedia works. The early technologies of the Web, like HTML, have 

been extended with technologies like XML and the Macromedia Flash file format (SWF 

for Shockwave-Flash) that make sophisticated interactive graphics and animation 

possible. 

Computer games 

By far the most commercially successful multimedia works are computer games, whose 

short but rich history is interwoven with the development of multimedia technologies. 

Games like Myst (Cyan) introduced consumers of all ages to the effective use of images, 

animations, and environmental sound to create a fictional world characterized by 

navigation and puzzle-solving. More recently, advancements in hardware and software 

technologies for graphics, audio, animation, and video, and sophisticated artificial 

intelligence and physics models are making game worlds look and act more convincing. 

Games are normally distributed on CD-ROM or DVD, but the Web is frequently used for 

distributing software updates and game demos. 

Digital art 

Artists have been using multimedia to create interactive installations that are controlled 

by computers and use multiple media. An example would be David Rokeby's Very

Nervous System (1986–90), an interactive sound installation where the user or a 

performer generates sound and music through body movement. These playful works are 

exhibited in galleries and museums as works of art that bring multimedia into the 

traditions of art exhibition. Other digital artists have created Web works that are 

submitted to online exhibitions like those mounted by the San Francisco Museum of 

Modern Art in their E•SPACE, which collects and commissions Web art objects. 

Multimedia encyclopedia 

Multimedia has been used widely in education and for the presentation of research. A 

common form of educational and reference multimedia is the multimedia encyclopedia, 

like the Encyclopaedia Britannica Online and Microsoft's Encarta (on CD-ROM). 

Multimedia encyclopedias are the logical extension of the print genre, taking advantage 

of the computer's capability to play time-dependent media like audio, animation, and 

video to enhance the accessibility of information. 

These are but examples of types of multimedia. A proper topology would be based on 

criteria. For example, we could classify multimedia works in terms of their perceived use, 

from entertainment to education. We could look at the means of distribution and the 

context of consumption of such works, from free websites that require a high-speed 

Internet connection, to expensive CD-ROM games that require the latest video cards to 

be playable. We could classify multimedia by the media combined, from remediated 

works that take a musical work and add synchronized textual commentary, to virtual 

spaces that are navigated. Other criteria for classification could be the technologies of 

production, the sensory modalities engaged, the type of organization that created the 

work, or the type of interactivity. 

What is the History of Multimedia? 

A traditional way of thinking through something that is new is to recover its histories. 

The histories of multimedia are still being negotiated and include the histories of different 

media, the history of computing, and the history of the critical theories applied to 

multimedia. One history of multimedia is the history of the personal computer as it 

evolved from an institutional machine designed for numerical processing to a multimedia 

personal computer that most of us can afford. The modern computer as it emerged after 

World War II is a general-purpose machine that can be adapted to new purposes through 

programming and peripherals. The history of the computer since the ENIAC (1946) can 

be seen as the working out of this idea in different ways, including the techniques for 

managing different media. While the first computers were designed solely to do scientific 

and applied numerical calculations, they were eventually extended to handle 

alphanumeric strings (text), raster and vector graphics, audio, moving pictures (video and 

animation), and finally, three-dimensional objects and space. Today's personal computer 

can handle all these media with the appropriate peripherals, making multimedia 

development and consumption available to the home user.

Numbers and text 

If the first computers were designed for number crunching and data processing for 

military, scientific, and then business applications, they soon became adapted to text 

editing or the manipulation of alphanumeric strings. The first commercial word processor 

was the IBM MT/ST (magnetic tape / Selectric typewriter), which was marketed by IBM 

as a "word processor" and released in 1964. It stored text on a tape for editing and 

reprinting through a Selectric typewriter. A word processor, as opposed to a text editor, 

was meant for producing rhetorical documents while text editors were for programming 

and interacting with the system. By the late 1970s, personal computers had primitive 

word processing programs that allowed one to enter, edit, and print documents. MicroPro 

International's WordStar (1979) was one of the first commercially successful word 

processing programs for a personal computer, expanding the media that could be handled 

by a home user from numbers to text. 

Images 

The next step was access to graphics on a personal computer, a development that came 

with the release of the Apple Macintosh in 1984. The Macintosh (Mac), which made 

innovations from the Xerox Palo Alto Research Center accessible on a commercially 

successful personal computer, was designed from the start to handle graphics. It came 

bundled with a "paint" program, MacPaint, and a mouse for painting and interacting with 

the graphical user interface (GUI). While it was not the first computer with graphical 

capabilities, it was the first widely available computer with standard graphical 

capabilities built-in so that anyone could paint simple images, edit them, print them or 

integrate them into other documents like word processing documents created with Mac- 

Write, a WYSIWIG (what-you-see-is-what-you-get) word processor also bundled with 

the early Macs. 

Desktop publishing 

In 1986, the capabilities of the Macintosh were extended with the release of the Mac 

Plus, Aldus PageMaker and the PostScript capable Apple LaserWriter. The combination 

of these three technologies made "desktop publishing" accessible on the personal 

computer where before it had been limited to very expensive specialized systems. While 

MacPaint was a playful tool that could not compete with commercial graphics systems, a 

designer outfitted with PageMaker and a LaserWriter could compete with professional 

designers working on dedicated typesetting systems for low-end, monochrome publishing 

jobs like manuals and newsletters. It was not long before a color-capable Macintosh was 

released (the Mac II), which, when combined with image-editing software like Adobe 

PhotoShop, helped the Mac replace dedicated systems as the industry standard for 

graphic design and publishing. Now, just about any publication, from newspapers to 

glossy annual reports, is created, edited, and proofed on personal computer systems. The 

only components still beyond the budget of the home user are the high-resolution digital 

cameras, scanners, and printers necessary to produce top-quality publications. But even 

these components are slowly moving into the reach of everyday computer users.

Desktop publishing is the precursor to multimedia, even though desktop publishing aims 

at rhetorical artifacts that are not viewed on a computer. Computer-aided graphic design 

and desktop publishing are arts that use computers instead of traditional technologies to 

produce rhetorical artifacts that combine media, such as text and images. The challenge 

of combining two media, each with different creative and interpretative traditions, 

predates desktop publishing – designers before the computer struggled to design the word 

and image. What was new, however, was that the personal computer user now had the 

opportunity to experiment with the design and placement of content in two-dimensional 

space. The initial result was a proliferation of horrid, over-designed newsletters and 

posters that frequently exhibited unrestrained use of fonts and visual styles. 

Authoring environments 

Further, the desktop publishing tools were themselves multimedia environments that 

provided for the direct manipulation of images and text. Desktop publishing was a 

precursor to multimedia; desktop publishers typically spent most of their time viewing 

the for-print documents they manipulated on the interactive screen, not on paper. Graphic 

designers comfortable with design for print (but on a screen) were ready when the first 

authoring tools became available for the design of screen-based media. They knew how 

to work with images and text in the two-dimensional screen space and were competent 

with the graphics tools needed to lay out and create computer graphics. When Apple 

released HyperCard in 1987, the graphics community was positioned to take advantage of 

their new skills in screen-based design. HyperCard, developed by the creator of MacPaint 

(Andy Hertzfield), was an immediate success, especially since it came free with every 

Macintosh and allowed multimedia authors to distribute HyperCard stacks without 

licensing costs to other Macintosh users. Given the high penetration of Macs in schools, it 

is not surprising that within a year of the release of HyperCard there were thousands of 

simple educational multimedia works that combined text, images, simple animations, and 

simple interactivity. 

Authoring environments like HyperCard are important to the growth of multimedia as 

they were easier to learn than the programming languages needed previously to create 

multimedia, and they were designed specifically for the combination of media into 

interactive works. HyperCard, as its name suggests, was inspired by hypertext theory. 

The metaphor of HyperCard was that authors created a stack of cards (nodes of 

information), which could have text, graphics, and buttons on them. The buttons were the 

hypertext links to other cards. HyperCard had a scripting language with which one could 

create more complex behaviors or add extensions to control other media devices like 

audio CDs and videodisk players. One of the most popular computer games of its time, 

Myst (1993), was first developed on HyperCard. The card stack metaphor was quickly 

imitated by Asymetrix ToolBook, one of the more popular multimedia authoring 

environments for the IBM PC. ToolBook's metaphor was a book of pages with text, 

graphics, and buttons and it added color capability. 

Today, the most popular authoring environments other than HTML editors such as 

Dreamweaver and GoLive are tools like Macromedia Director and Macromedia Flash.

Both Director and Flash use a cell and timeline metaphor that evolved out of animation 

environments. Flash is used extensively to add animations and interactive components to 

websites while Director is used for more complex projects that are typically delivered on 

a CD-ROM. The Flash file format (SWF) has been published so that other tools can 

manipulate SWF. 

Sound 

The Macintosh also incorporated sound manipulation as a standard feature. The first 

Macs released in the mid-1980s had built-in sound capabilities beyond a speaker for 

beeps. The 128K Mac had 8-bit mono sound output capability. By 1990, Apple was 

bundling microphones with standard Macs. HyperCard could handle audio, though it 

could not edit it. The standard Macintosh thus had simple audio capabilities suitable for 

interactive multimedia. With the addition of Musical Instrument Digital Interface (MIDI) 

controllers and software, Macintoshes became popular in the electronic music community 

along with the now discontinued Atari ST (1985), which came with a built in MIDI port. 

One of the first multimedia works to make extensive use of audio was Robert Winter's 

interactive Beethoven's Ninth Symphony. This 1989 work came with HyperCard stacks 

on floppy disk, which could control a commercial audio CD of Beethoven's Ninth 

Symphony. The user could navigate the audio and read critical notes that were 

synchronized to the symphony. 

Digital video 

The latest media threshold to be overcome in affordable personal computers is digital 

video. The challenge of multimedia is to combine not just asynchronous media like text 

and images, neither of which need to be played over time, but also time-dependent media 

like audio, animation, and video. Video puts the greatest stress on computer systems 

because of the demands of accessing, processing, and outputting the 29.97 frames-persecond 

typical of television-quality video. Only recently, with the introduction of 

computers with Fire Wire or IEEE-1394 ports, has it become easy to shoot video, 

download it to the personal computer for editing, and transfer it back to tape, CD, or 

DVD, or even to stream it over the Internet. Given the challenge of integrating video, 

there have been some interesting hybrid solutions. One of the first multimedia works, the 

Aspen Movie Map (1978), by Andrew Lippman (and others) from what is now called the 

MIT Media Lab, combined photographs on a videodisk with computer control so that the 

user could wander through Aspen, going up and down streets in different seasons. With 

the release of digital video standards like MPEG (MPEG-1 in 1989, MPEG-2 in 1991) 

and Apple QuickTime (1991), it became possible to manage video entirely in digital 

form. An early published work that took advantage of QuickTime was the Voyager CD- 

ROM of the Beatles' A Hard Day's Night (1993). This was built around a digital video 

version of the innovative Beatles' music movie. It is now common for multimedia works 

to include low-resolution digital video elements.

Virtual space and beyond 

Current multimedia systems present the user with a two-dimensional graphical user 

interface. While such systems can manipulate three-dimensional information (3-D), they 

do not typically have the 3-D input and output devices associated with virtual reality 

(VR) systems. Is VR the next step in the evolution of the multimedia computer and user 

interface? In the 1990s it seemed that cyberspace, as described by William Gibson in 

Neuromancer (1984), was the next frontier for multimedia computing. Gibson's vision 

was implemented in systems that combine head-tracking systems, data gloves, and 3-D 

goggles to provide an immersive experience of a virtual space. The metaphor for 

computing would no longer be the desktop, but would be virtual spaces filled with 

avatars representing people and 3-D objects. The relationship between user and computer 

would go from one of direct manipulation of iconographic representations to immersion 

in a simulated world. Space and structure were the final frontier of multimedia. 

While this projected evolution of the multimedia interface is still the subject of academic 

research and development, it has been miniaturization and the Internet that have driven 

the industry instead. The desktop multimedia systems of the 1990s are now being 

repackaged as portable devices that can play multiple media. The keyboard and the 

mouse are being replaced by input devices like pen interfaces on personal digital 

assistants (PDAs). Rather than immersing ourselves in virtual caves, we are bringing 

multimedia computing out of the office or lab and weaving it in our surroundings. The 

challenge to multimedia design is how to scale interfaces appropriately for hand-held 

devices like MP3 players and mobile phones. 

What are the Academic Issues in the Study of 

Multimedia? 

How can we study multimedia in the academy? What are the current issues in multimedia 

theory and design? The following are some of the issues that the community is thinking 

through. 

Best practices in multimedia production 

The academic study of multimedia should be distinguished from the craft of multimedia. 

Learning to create multimedia works is important to the study of multimedia in applied 

programs, but it is possible to study digital media in theory without learning to make it. 

That said, a rich area of academic research is in the study of appropriate practices in 

multimedia design. For example, the field of Human Computer Interface (HCI) design is 

one area that crosses computer science, information science, psychology, and design. 

HCI tends to be the scientific study of interface and interactivity. In art and design 

schools the issue of interface tends to be taken up within the traditions of visual design 

and the history of commercial design. An important issue for computing humanists 

building multimedia is digitization – what to digitize, how to digitally represent evidence, 

and how to digitize evidence accurately.

Game criticism and interactivity 

If the practice of digitization creates the media that make up multimedia, it is the practice 

of combining multiple media into rhetorically effective works that is the play of 

multimedia. The possibilities of interactivity are what characterize computer-based 

media. In particular, interactive game designers have created complex systems for 

interaction with media. For this reason, the emerging field of Digital Game Criticism that 

attempts to study computer games seriously as popular culture and rhetoric is important 

to the study of multimedia. What is a game and how can we think of games as forms of 

human art? What makes an effective or playable game? What are the possibilities for 

playful interaction through the computer? The interactive game may be the paradigmatic 

form of multimedia, or for that matter, the paradigmatic form of expression in the digital 

age. 

Theories and histories of multimedia 

The study of multimedia as a form of expression has yet to develop a theoretical tradition 

of its own. Instead, critical theories from existing disciplines are being applied with 

increasing ingenuity from film studies to literary theory. The very issue of which existing 

theoretical traditions can be usefully applied to multimedia is a source of debate and 

discussion. This essay has taken a philosophical/historical approach, asking questions 

about how to think through multimedia. Theorists like Brenda Laurel (Computers as 

Theatre, 1991) look at multimedia as dramatic interactions with users. George Landow, 

in Hypertext: The Convergence of Contemporary Critical Theory and Technology (1992), 

has applied literary theory to computing. Lev Manovich, in The Language of New Media 

(2001), looks at the historical, social, and cultural continuity of film and new media. In 

Hamlet on the Holodeck: The future of Narrative in Cyberspace (1997), Janet H. Murray 

considers the new aesthetic possibilities of multimedia within the context of narrative 

tradition. 

The intersection of technology, communication, and culture has also been a topic of wide 

interest. Marshall McLuhan, in Understanding Media (1964), popularized an approach to 

thinking about the effects of technology and media on content. He and others, like Walter 

Ong (Orality and Literacy, 1982), draw our attention to the profound effects that changes 

in communications technology can have on what is communicated and how we think 

through communication. Influential industry magazines like Wired take it as a given that 

we are going through a communications revolution as significant as the development of 

writing or print. There is no shortage of enthusiastic evangelists, like George Gilder (Life 

After Television, 1992) and critics like Neil Postman (Technopoly, 1993). There are also 

influential popular works on personal computing and media technology – works that have 

introduced ideas from the research community into popular culture, like those of Stewart 

Brand (The Media Lab, 1987), Howard Rheingold (Tools for Thought, 1985, and Virtual 

Communities, 1994), and Nicholas Negroponte (Being Digital, 1995). 

Conclusion

There are two ways we can think through multimedia. The first is to think about 

multimedia through definitions, histories, examples, and theoretical problems. The 

second way is to use multimedia to think and to communicate thought. The academic 

study of multimedia is a "thinking-about" that is typically communicated through 

academic venues like textbooks, articles, and lectures. "Thinking-with" is the craft of 

multimedia that has its own traditions of discourse, forms of organization, tools, and 

outcomes. To think-with multimedia is to use multimedia to explore ideas and to 

communicate them. In a field like multimedia, where what we think about is so new, it is 

important to think-with. Scholars of multimedia should take seriously the challenge of 

creating multimedia as a way of thinking about multimedia and attempt to create 

exemplary works of multimedia in the traditions of the humanities. 


This bibliography is organized along the lines of the chapter to guide readers in further 

study. 

Introduction to Multimedia 

Ambron, Sueann and Kristina Hooper, (eds.) (1988). Interactive Multimedia: Visions of 

Multimedia for Developers, Educators, and Information Providers. Redmond, WA: 

Microsoft Press. 

Blattner, Meera M. and Roger B. Dannenberg, (eds.) (1992). Multimedia Interface 

Design. New York: ACM Press. 

Buford, John F. Koegel, (ed.) (1994). Multimedia Systems. New York: Addison-Wesley. 

Cotton, Bob, and Richard Oliver (1994). The Cyberspace Lexicon: An Illustrated 

Dictionary of Terms from Multimedia to Virtual Reality. London: Phaidon. 

Cotton, Bob, and Richard Oliver (1997). Understanding Hypermedia 2.000: Multimedia 

Origins, Internet Futures. London: Phaidon. 

Elliot, John and Tim Worsley, (eds.) (1996). Multimedia: The Complete Guide. Toronto: 

Élan Press. 

Encyclopaedia Britannica. Interactive multimedia. Encyclopaedia Britannica Online. 

URL: http://www.search.eb.com/bol/topic?eu=146l&sctn=1. Accessed October 1999. 

Haykin, Randy, (ed.) (1994). Multimedia Demystified: A Guide to the World of 

Multimedia from Apple Computer, Inc. New York: Random House. 

Hofstetter, Fred T. (1995). Multimedia Literacy. New York: McGraw-Hill.

Keyes, Jessica (ed) (1994). The McGraw-Hill Multimedia Handbook. New York: 

McGraw-Hill. 

Nielsen, Jakob (1995). Multimedia and Hypertext: The Internet and Beyond. Boston: AP 

Professional. 

Nyce, J. M. and P. Kahn, (eds.) (1991). From Memex to Hypertext. Boston: Academic 

Press. (Includes As We May Think, by Vannevar Bush). 

Reisman, Sorel, (ed.) (1994). Multimedia Computing: Preparing for the 21st Century. 

Harrisburg and London: Idea Group Publishing. 

Tannenbaum, Robert S. (1998). Theoretical Foundations of Multimedia. New York: 

Computer Science Press. 

Theories and Multimedia 

Barrett, Edward, (ed.) (1992). Sociomedia: Multimedia, Hypermedia, and the Social 

Construction of Knowledge. Cambridge, MA: MIT Press. 

Benjamin, Walter (1968). The Work of Art in the Age of Mechanical Reproduction, tr. 

Harry Zohn. In. Hannah Arendt, (ed.), Illuminations (pp. 217–51). New York: Schocken 

Books. 

Bolter, Jay David and Richard Grusin (1999). Remediation. Cambridge, MA: MIT Press. 

Landow, George P. (1992). Hypertext: The Convergence of Contemporary Critical 

Theory and Technology. Baltimore: Johns Hopkins University Press. 

Landow, George P., (ed.) (1994). Hyper/Text/Theory. Baltimore: Johns Hopkins 


Laurel, Brenda (1991). Computers as Theatre. New York: Addison-Wesley. 

Lévy, Pierre (1997). Cyberculture; Rapport au Conseil de l'Europe. Paris: Edition Odile 

Jacob/Editions du Conseil de l'Europe. 

Liestøl, Gunnar, et al., (eds.) (2003). Digital Media Revisited: Theoretical and 

Conceptual Innovations in Digital Domains. Cambridge, MA: MIT Press. 

McLuhan, Marshall (1964). Understanding Media: The Extensions of Man. London: 

Routledge. 

Manovich, Lev (2001). The Language of New Media. Cambridge, MA: MIT Press.

Mitchell, William J. (1992). The Reconfigured Eye: Visual Truth in the Post- 

Photographic Era. Cambridge, MA: MIT Press. 

Ong, Walter J. (1982). Orality and Literacy: The Technologhing of the Word. New York: 

Routledge. 

Stephens, Mitchell (1998). The Rise of the Image and the Fall of the Word. Oxford: 


Interactivity, Interface, and Game Criticism 

Aarseth, Espen J. (1997). Cybertext: Perspectives on Ergotic Literature. Baltimore, MD: 

Johns Hopkins University Press. 

Baecker, Ronald M., et al., (eds.) (1995). Human – Computer Interaction: Toward the 

Year 2000, 2nd edn. San Francisco: Morgan Kaufmann. 

Birringer, Johannes (1998). Media and Performance: Along the Border. Baltimore: Johns 

Hopkins University Press. 

Burnham, Van (2001). Supercade: A Visual History of the Videogame Age 1971–1984. 

Cambridge, MA: MIT Press. 

Cohen, Scott (1984). Zap! The Rise and Fall of Atari. New York: McGraw-Hill. 

Crawford, C. The Art of Computer Game Design. URL: http://www.erasmataz2.com/. 

Accessed December 2002. 

Huizinga, Johan (1950). Homo Ludens: A Study of the Play-Element in Culture. Boston: 

Beacon Press. 

King, Geoff and Tanya Krzywinska, (eds.) (2002). Screen Play: 

Cinema/Videogames/Interfaces. New York: Wallflower Press. 

King, Lucien, (ed.) (2002). Game On: The History and Culture of Video Games. New 

York: Universe Publishing. 

Laurel, Brenda, (ed.) (1990). The Art of Human–Computer Interface Design. New York: 

Addison-Wesley. 

Murray, Janet H. (1997). Hamlet on the Holodeck: The Future of Narrative in 

Cyberspace. Cambridge, MA: MIT Press. 

Norman, Donald (1988). The Psychology of Everyday Things. New York: Basic Books.

Preece, Jenny et al., (eds.) (1994). Human–Computer Interaction. New York: Addison- 

Wesley. 

Provenzo, Eugene E, Jr. (1991). Video Kids: Making Sense of Nintendo. Cambridge, MA: 

Harvard University Press. 

Rada, Roy (1995). Interactive Media. New York: Springer-Verlag. 

Ryan, Marie-Laure (1997). Interactive Drama: Narrativity in a Highly Interactive 

Environment. Modern Fiction Studies 43, 3: 677–707. 

Wolf, Mark J., (ed.) (2001). The Medium of the Video Game. Austin: University of Texas 

Press. 

History of Computing and Multimedia 

Atomic Rom. Writing for Multimedia: Great Moments in Multimedia. URL: 

http://www.home.earthlink.net/~atomic_rom/moments.htm. Accessed December 2002. 

Brand, Stewart (1987). The Media Lab: Inventing the Future at MIT. New York: Viking. 

Ceruzzi, Paul E. (1983). Reckoners: The Prehistory of the Digital Computer, from Relays 

to the Stored Program Concept, 1935–1945. Westport, CT: Greenwood Press. 

Ceruzzi, Paul E. (1998). A History of Modern Computing. Cambridge, MA: MIT Press. 

Freiberger, Paul and Michael Swaine (1984). Fire in the Valley: The Making of the 

Personal Computer. Berkeley, CA: Osborne/McGraw-Hill. 

Ifrah, Georges (2000). The Universal History of Computing: From the Abacus to the 

Quantum Computer, tr. E. F. Harding. New York: John Wiley. 

Kahney, Leander. HyperCard Forgotten, but not Gone. Wired News (August 14, 2002). 

URL: http://www.wir(ed.)com/news/mac/0,2125,54365,00.html. Accessed December 

2002. 

Rheingold, Howard (1985). Tools far Thought: The People and Ideas behind the Next 

Computer Revolution. New York: Simon and Schuster. 

Digital Art and Design 

Lunenfeld, Peter (2000). Snap to Grid: A User's Guide to Arts, Media, and Cultures. 


Marcus, A. (1991). Graphic Design for Electronic Documents and User Interfaces. New 

York: ACM Press/Addison-Wesley.

New Media Encyclopedia. URL: http://www.newmediaarts.org/sommaire/english/sommaire.htm. 

Accessed December 2002. 

Rokeby, David, http://homepage.mac.com/davidrokeby/home.html. Accessed December 

2002. 

Rush, Michael (1999). New Media in Late 20th-century Art. New York: Thames and 

Hudson. 

Schwarz, Hans-Peter (1997). Media-Art-History. Munich: Prestel-Verlag. 

Velthoven, Willem and Jorinde Seijdel, (eds.) (1996). Multimedia Graphics: The Best of 

Global Hyperdesign. San Francisco: Chronicle Books. 

Wilson, Stephen (2002). Information Arts: Intersections of Art, Science, and Technology. 


Cyberculture and Multimedia 

Gibson, William (1984). Neuromancer. New York: Ace Science Fiction Books. 

Gilder, George (1992). Life after Television: The Coming Transformation of Media and 

American Life. New York: W. W. Norton. 

Gray, Chris H., (ed.) (1995). The Cyborg Handbook. New York: Routledge. 

Heim, Michael (1993). The Metaphysics of Virtual Reality. Oxford: Oxford University 

Press. 

Negroponte, Nicholas (1995). Being Digital. New York: Alfred A. Knopf. 

Postman, Neil (1993). Technopoly: The Surrender of Culture to Technology. New York: 

Vintage Books. 

Rheingold, Howard (1994). Virtual Communities: Homesteading on the Electronic 

Frontier. New York: Harper Perennial. 

Woolley, Benjamin (1992). Virtual Worlds: A Journey in Hype and Hyperreality. 

Oxford: Blackwell. 

Selected Technologies and Multimedia Works Mentioned 

Adobe. GoLive. URL: http://www.adobe.com/products/golive/main.html. Accessed 

December 2002. 

Apple. QuickTime. URL: http://www.apple.com/quicktime/. Accessed December 2002.

Click2learn. Toolbook. URL: http://www.asymetrix.com/en/toolbook/index/asp. 

Accessed December 2002. (Formerly Asymetrix Toolbook.). 

Cyan. Myst. URL: http://www.riven.com/home.html. Accessed December 2002. 

Macromedia. Flash File Format (SWF). URL: 

http://www.macromedia.com/software/flash/open/licensing/fileformat/. Accessed 

December 2002. 

Macromedia (for information on Dreamweaver, Flash, and Director). URL: 

http://www.macromedia.com. Accessed December 2002. 

Voyager. A Hard Day's Night. URL: 

http://voyager.learntech.com/cdrom/catalogpage.cgiPahdn. Accessed December 2002. 

11. 

Performing Arts 

David Z. Saltz 

Computers and the performing arts make strange bedfellows. Theater, dance, and 

performance art persist as relics of liveness in a media-saturated world. As such, they 

stand in defiant opposition to the computer's rapacious tendency to translate everything 

into disembodied digital data. Nonetheless, a number of theorists have posited an inherent 

kinship between computer technology and the performing arts (Laurel 1991; Saltz 1997). 

While "old" media such as print, film, and television traffic in immaterial representations 

that can be reproduced endlessly for any number of viewers, the interactivity of "new" 

media draws them closer to live performance. Every user's interaction with a computer is 

a unique "performance", and moreover it is one that, like theater, typically involves an 

element of make-believe. When I throw a computer file in the "trash" or "recycling bin", I 

behave much like an actor, performing real actions within an imaginary framework. I 

recognize that the "trash bin" on my screen is no more real than a cardboard dagger used 

in a play; both are bits of virtual reality. Indeed, theater theorist Antonin Artaud coined 

the term "virtual reality" to describe the illusory nature of characters and objects in the 

theater over fifty years before Jaron Lanier first used that term in its computer-related 

sense (Artaud 1958: 49). 

It is no wonder, then, that performance scholars and practitioners have looked to digital 

technology to solve age-old problems in scholarship, pedagogy, and creative practice. 

This chapter will begin with a review of significant scholarly and pedagogical 

applications of computers to performance, and then turn to artistic applications. 

Database Analysis

In 1970, with the number of computers in the world still totaling less than 50,000, 

Lawrence McNamee touted the potential of computers to aid theater research. The 

examples that he points to as models for the future now seem mundane: a concordance of 

Eugene O'Neill's plays and a database of theater dissertations, both published in 1969- 

Twelve years later, in his Presidential Address to the American Society of Theater 

Research, Joseph Donohue highlighted the still largely hypothetical opportunities 

computers offered to theater scholars, emphasizing the ability of computers to order 

textual data into "clear, pertinent and discrete" patterns (1981: 139). In the 1980s theater 

historians increasingly turned to computers to help organize and analyze historical data. 

Edward Mullaly, for example, describes his use of an Apple lie and the database software 

DB Master to reconstruct the career of a minor nineteenth-century American actor/ 

manager from newspaper accounts (1987: 62). These early attempts by performance 

scholars to tap the power of computers relied on computers' ability to crunch textual and 

numeric data, such as dramatic dialogue, critical texts about performance, names, 

locations, and dates. Such applications do not tackle challenges specific to the performing 

arts. Using a computer to analyze a play or a performance event is no different from 

using it to analyze any other kind of literary work or historical phenomenon. 

Hypermedia 

The performing arts, however, are not exclusively, or even primarily, textual. A work of 

dance, theater, or performance art is a visual, auditory, and, most of all, corporeal event. 

Only in the 1980s, when low-cost personal computers acquired the ability to store and 

manipulate images, sounds, and finally video, did computers begin to offer an effective 

way to represent the phenomenon of performance. Larry Friedlander's Shakespeare 

Project anticipated many subsequent applications of digital technology to performance 

pedagogy. Friedlander began to develop the Shakespeare Project in 1984 using an IBM 

Info Window system. He adopted HyperCard in 1987 while the software was still in 

development at Apple. Because personal computers then had very crude graphics 

capabilities and no video, Friedlander adopted a two-screen solution, with the computer 

providing random access to media stored on a laserdisk. The laserdisk contained 

hundreds of still images and, more important, six video segments, including two 

contrasting filmed versions of one scene each from Hamlet, Macbeth, and King Lear. The 

Shakespeare Project used this video material in three ways. In a Performance area, 

students could read the Shakespearean text alongside the video, switch between film 

versions at any time, jump to any point in the text, and alternate between a film's original 

audio track and a recording of Friedlander's interpretation of the actors'"subtext." In a 

Study area, students participated in interactive tutorials covering aspects of 

Shakespearean performance such as characterization and verse. Finally, in a Notebook 

area, students could extract digital video excerpts to incorporate into their own essays. In 

each case, the computer made it possible for students to read a performance almost as 

closely and flexibly as they could a printed text. 

CD-ROM editions of plays released in the 1990s – most notably the 1994 Voyager 

edition of Macbeth and the 1997 Annenberg/CPB edition of Ibsen's A Doll's House - 

incorporate core elements of Friedlander's design, keying the play's script to multiple

filmed versions of select scenes. In addition, these CD-ROMs provide a rich assortment 

of critical resources and still images. The Voyager Macbeth also includes an audio 

recording of the entire play by the Royal Shakespeare Company and a karaoke feature 

that allows the user to perform a role opposite the audio. These CD-ROMs take 

advantage of the ability acquired by personal computers in the 1990s to display video 

directly, obviating the need for a laserdisk player and second monitor. This approach is 

far more elegant, compact, and cost-efficient than using laserdisks, but the video in these 

early CD-ROM titles is much smaller and lower in quality than that of a laserdisk. By 

2000, faster computer processors and video cards, along with more efficient video 

compression schemes and widespread DVD technology, had finally closed the gap 

between personal computers and laserdisk players. 

Theater Models 

The projects considered so far rely on the multimedia capabilities of computers; that is, a 

computer's ability to store and retrieve text, images, and audio. Other projects have 

exploited the power of computers to generate complex simulations of 3-D reality. 

Performance scholars began to explore the use of 3-D modeling in the mid-1980s to 

visualize hypotheses about historical theater buildings and staging practices. In 1984, 

Robert Golder constructed a 3-D computer model of the 1644 Théatre du Marais, and 

Robert Sarlós used computer models to visualize staging strategies for a real-world 

recreation of the medieval Passion Play of Lucerne. This technique became more 

common in the 1990s when high-end computer-assisted design (CAD) software became 

available for personal computers. Theater historians used 3-D modeling software to 

reconstruct structures such as the fifth-century bce Theater of Dionysos (Didaskalia 

website) and Richelieu's Palais Cardinal theater (Williford 2000). One of the most 

ambitious of these projects is an international effort, led by Richard Beacham and James 

Packer, to reconstruct the 55 bce Roman theater of Pompey. The computer model is 

painstakingly detailed, with every contour of every column and frieze being modeled in 

three dimensions. As a result, even using state-of-the-art graphics workstations, a single 

frame takes approximately one hour to render at screen resolution (Denard 2002: 36). 

None of the 3-D modeling projects described above allow a user to navigate the virtual 

spaces in real time; the models are experienced only as a series of still images or prerendered 

animations. These projects are geared toward research, with the goal of 

generating new historical knowledge and testing hypotheses. Consequently, the quality of 

the data is more important than the experience of the user. When the emphasis is on 

teaching rather than research, however, the tendency is to make the opposite trade-off, 

favoring interactivity over detail and precision. The most significant effort along those 

lines is the THEATRON project, also under the direction of Richard Beacham, with 

funding from the European Commission. THEATRON uses Virtual Reality Modeling 

Language (VRML) to allow people to explore models of historically significant theater 

structures over the Web. The first set of walkthroughs, including such structures as the 

Ancient Greek theater of Epidauros, Shakespeare's Globe and the Bayreuth Festspielhaus, 

became commercially available in 2002.

The THEATRON walkthroughs provide an experience of immersion, conveying a clear 

sense of the scale and configuration of the theater spaces. These spaces, however, are 

empty and static, devoid of any sign of performance. Frank Mohler, a theater historian 

and designer, has adopted an approach that focuses not on the architecture per se, but on 

technologies used for changing scenery. Mohler has made effective use of simple 

animations to simulate the appearance and functioning of Renaissance and Baroque stage 

machinery. 

Performance Simulations 

Models of theaters and scenery, no matter how detailed, immersive, or interactive, 

simulate only the environment within which performances take place. There have also 

been attempts to use computer animation techniques to simulate the phenomenon of 

performance itself, both for pedagogical and scholarly purposes. Again, Larry 

Friedlander produced one of the earliest examples, a program called TheaterGame 

created in conjunction with the Shakespeare Project. This software was innovative for its 

time and attracted a good deal of press attention. TheaterGame allowed students to 

experiment with staging techniques by selecting crude two-dimensional human figures, 

clothing them from a limited palette of costumes, positioning set pieces on a virtual stage, 

and finally moving the virtual actors around the stage and positioning their limbs to form 

simple gestures. The goal was to allow students with no theater experience or access to 

real actors to investigate the effects of basic staging choices. 

At the same time Friedlander was developing TheaterGame, Tom Calvert began to 

develop a similar, but vastly more sophisticated, application geared toward 

choreographers. The project started in the 1970s as a dance notation system called 

Compose that ran on a mainframe computer and output its data to a line printer. In the 

1980s, Calvert replaced abstract symbols describing motions with 3-D human animations 

and dubbed the new program LifeForms. The human models in LifeForms are featureless 

wireframes, but the movements are precise, flexible, and anatomically correct. LifeForms 

was designed as a kind of word processor for dance students and practicing 

choreographers, a tool for composing dances. In 1990, the renowned choreographer 

Merce Cunningham adopted the software, bringing it to international attention. In the 

early 1990s, LifeForms became a commercial product. 

Motion capture technology offers a very different approach to performance simulation. 

Motion capture uses sensors to track a performer's movements in space and then maps 

those movements onto a computer-generated model. While applications such as Life- 

Forms are authoring tools for virtual performances, motion capture provides a tool for 

archiving and analyzing real performances. The Advanced Computer Center for Art and 

Design (ACCAD) at Ohio State University maintains a high-end optical motion capture 

system dedicated to research in the performing arts. In 2001, ACCAD began to build an 

archive of dance and theater motion data by capturing two performances by the legendary 

mime Marcel Marceau. These data, which include subtle details such as the performer's 

breathing, can be transferred onto any 3-D model and analyzed in depth.

I am currently working with a team of researchers at seven universities on a National 

Science Foundation project that combines many elements of the projects discussed 

above: 3-D modeling of theater architecture, animated scenery, performance simulation 

using motion capture, along with simulations of audience interactions and hypermedia 

content. This project, called Virtual Vaudeville, is creating a computer simulation of late 

nineteenth-century American vaudeville theater. The goal is to develop reusable 

strategies for using digital technology to reconstruct and archive historical performance 

events. Virtual Vaudeville strives to produce the sensation of being surrounded by human 

activity on stage, in the audience, and backstage. Viewers enter the virtual theater and 

watch the animated performances from any position in the audience, and are able to 

interact with the animated spectators around them. Professional actors are recreating the 

stage performances, and these performances are being transferred to 3-D models of the 

nineteenth-century performers using motion and facial capture technology. The program 

is being built with a high-performance game engine of the sort usually used to create 

commercial 3-D action games. 

Computer simulations of performance spaces and performers are powerful research and 

teaching tools, but carry inherent dangers. Performance reconstructions can encourage a 

positivist conception of history (Denard 2002: 34). A compelling computer simulation 

conceals the hypothetical and provisional nature of historical interpretation. Moreover, 

vividly simulated theaters and performances produce the sensation that the viewer has 

been transported back in time and is experiencing the performance event "as it really 

was." But even if all of the physical details of the simulation are accurate, a present-day 

viewer's experience will be radically different from that of the original audience because 

the cultural context of its reception has changed radically. Some projects, such as the 

Pompey Project and Virtual Vaudeville, are making a concerted effort to counteract these 

positivistic tendencies, primarily by providing hypermedia notes that supply contextual 

information, provide the historical evidence upon which the reconstructions are based, 

and offer alternatives to the interpretations of and extrapolations from the historical data 

used in the simulation. Whether such strategies will prove sufficient remains to be seen. 

Computers in Performance 

My focus so far has been on applications of computers to teaching and research in the 

performing arts. Digital technology is also beginning to have a significant impact on the 

way those art forms are being practiced. For example, the computer has become a routine 

part of the design process for many set and lighting designers. Throughout the 1990s, a 

growing number of designers adopted CAD software to draft blueprints and light plots 

and, more recently, employed 3-D modeling software (sometimes integrated into the 

CAD software) to produce photorealistic visualizations of set and lighting designs. 

Computers are also being incorporated into the performances themselves. The earliest 

and most fully assimilated example is computer-controlled stage lighting. Computerized 

light boards can store hundreds of light cues for a single performance, automatically 

adjusting the intensity, and in some cases the color and direction, of hundreds of lighting 

instruments for each cue. This technology was introduced in the late 1970s, and by the

1990s had become commonplace even in school and community theaters. Similarly, set 

designers have used computerized motion control systems to change scenery on stage – 

though this practice is still rare and sometimes has disastrous results. For example, the 

initial pre-Broadway run of Disney's stage musical Aida featured a six-ton robotic 

pyramid that changed shape under computer control to accommodate different scenes. 

The pyramid broke down on opening night and repeatedly thereafter (Elliott 1998). 

Disney jettisoned the high-tech set, along with the production's director and designer, 

before moving the show to Broadway. 

Computer-controlled lighting and scenery changes are simply automated forms of precomputer 

stage technologies. A growing number of dance and theater artists have 

incorporated interactive digital media into live performance events. Such performances 

can have a profound impact on the way the art forms are conceived, collapsing the neat 

ontological divide that once separated (or seemed to separate) the live performing arts 

from reproductive media such as film and video. The Digital Performance Archive, a 

major research project conducted by Nottingham Trent University and the University of 

Salford, has created a web-based database documenting hundreds of dance and theater 

performances produced in the 1990s that combined digital media with live performance. 

George Coates's Performance Works in San Francisco was one of the first and most 

prominent theater companies to combine digital media with live performance to create 

stunning, poetic visual spectacles. In 1989, George Coates founded SMARTS (Science 

Meets the Arts), a consortium including companies such as Silicon Graphics, Sun 

Microsystems, and Apple Computer, to acquire the high-end technology required for his 

productions. In a series of productions starting with Invisible Site: A Virtual Sbo in 1991, 

Coates perfected a technique for producing the vivid illusion of live performers fully 

integrated into a rapidly moving 3-D virtual environment. The spectators wear polarized 

glasses to view huge, high-intensity stereographic projections of digital animations. The 

projections that surround the revolving stage cover not only the back wall but the stage 

floor and transparent black scrims in front of the performers. The digital images are 

manipulated interactively during the performances to maintain tight synchronization 

between the live performers and the media. 

Another pioneer in the use of virtual scenery is Mark Reaney, founder of the Institute for 

the Exploration of Virtual Realities (i.e. VR) at the University of Kansas (Reaney 1996; 

Gharavi 1999). In place of physical scenery, Reaney creates navigable 3-D computer 

models that he projects onto screens behind the performers. The perspective on Reaney's 

virtual sets changes in relation to the performers' movements, and a computer operator 

can instantly transform the digital scenery in any way Reaney desires. In 1995, i.e. VR 

presented its first production, Elmer Rice's expressionist drama The Adding Machine. For 

this production, Reaney simply rear-projected the virtual scenery. For Wings in 1996, 

Reaney had the spectators wear low-cost head-mounted displays that allowed them to see 

stereoscopic virtual scenery and the live actors simultaneously. Starting with Telsa 

Electric in 1998 Reaney adopted an approach much like Coates's, projecting stereoscopic 

images for the audience to view through polarized glasses. Nonetheless, Reaney's 

approach differs from Coates's in a number of important ways. While Coates authors his

own highly associative works, Reaney usually selects pre-existing plays with linear 

narratives. Reaney's designs, while containing stylized elements, are far more literal than 

Coates's, and the technology he employs, while more advanced than that available to 

most university theaters, is far more affordable than the state-of-the-art technology at 

Coates's disposal. 

A number of dance performances have experimented with interactive 3-D technology 

similar to that used by Coates and Reaney. One of the earliest and most influential 

examples is Dancing with the Virtual Dervish/Virtual Bodies, a collaboration between 

dancer and choreographer Yacov Sharir, visual artist Diane Gromala, and architect 

Marcos Novak first presented at the Banff Center for the Arts in 1994. For this piece, 

Sharir dons a head-mounted display and enters a VR simulation of the interior of a 

human body, constructed from MRI images of Gromala's own body. The images that 

Sharir sees in the display are projected onto a large screen behind him as he dances. 

The three examples of digitally-enhanced performance considered above are radically 

different in their aesthetics and artistic goals, but all establish the same basic relationship 

between the media and live performers: in each case, the media functions as virtual 

scenery, in other words, as an environment within which a live performance occurs. 

There are, however, many other roles that media can assume in a performance 1 . For 

example, the media can play a dramatic role, creating virtual characters who interact with 

the live performers. A number of choreographers, including prominent figures such as 

Merce Cunningham and Bill T Jones, have enlisted motion capture technology to lend 

subtle and expressive movements to virtual dance partners (Dils 2002). Often, as in the 

case of both Cunningham's and Jones's work, the computer models themselves are highly 

abstract, focusing the spectators' attention on the motion itself. In a 2000 production of 

The Tempest at the University of Georgia, the spirit Ariel was a 3-D computer animation 

controlled in real time by a live performer using motion capture technology (Saltz 

2001b). Claudio Pinhanez has applied artificial intelligence and computer-vision 

techniques to create fully autonomous computer characters. His two-character play It/I, 

presented at MIT in 1997, pitted a live actor against a digital character (Pinhanez and 

Bobick 2002). 

A key goal of Pinhanez's work is to produce an unmediated interaction between the live 

performer and digital media. While this goal is unusual in theater, it is becoming 

increasingly common in dance, where there is less pressure to maintain a coherent 

narrative, and so creating an effective interaction between the performer and media does 

not require sophisticated artificial intelligence techniques. Electronic musicians created a 

set of technologies useful for creating interactive dance in the 1980s in the course of 

exploring sensors for new musical instrument interfaces. The Studio for Electro- 

Instrumental Music, or Steim, in the Netherlands was an early center for this research, 

and continues to facilitate collaborations between dancers and electronic musicians. A 

number of dancers have created interactive performances using the Very Nervous System 

(VNS), a system first developed by the Canadian media artist David Rokeby in 1986. The 

VNS uses video cameras to detect very subtle motions that can trigger sounds or video. 

One of the first dance companies formed with the explicit goal of combining dance with

interactive technology was Troika Ranch, founded in 1993 by Mark Coniglio and Dawn 

Stoppiello. Troika Ranch has developed a wireless system, MidiDancer, which converts a 

dancer's movements into MIDI (Musical Instrument Digital Interface) data, which can be 

used to trigger sounds, video sequences, or lighting. 

One of the most complex and successful applications of this kind of sensor technology 

was the 2001 production L'Universe (pronounced "loony verse") created by the Flying 

Karamazov Brothers, a troupe of comedic jugglers, in conjunction with Neil Gershenfeld 

of the Physics and Media Group at MIT. Gershenfeld created special juggling clubs with 

programmable displays, and used sonar, long-range RF links and computer vision to track 

the positions and movements of the four performers. This technology was used to create a 

complex interplay between the performers and media, with the jugglers' actions 

automatically triggering sounds and altering the color of the clubs. 

Brenda Laurel and Rachel Strickland's interactive drama Placeholder is one of the bestknown 

attempts to create a performance in which the spectators interact directly with the 

technology. As two spectators move through a ten-foot diameter circle wearing headmounted 

displays, they interact with a series of digital characters, with a character called 

the Goddess controlled by a live performer, and, to a limited extent, with each other 

(Ryan 1997: 695–6). The challenge of creating a rich and compelling narrative within this 

kind of interactive, open-ended structure is immense. While a number of writers have 

tackled this challenge from a theoretical perspective (see, for example, Ryan 1997; 

Murray 1997), the promise of this new dramatic medium remains largely unfulfilled. 

Telematic Performance 

In 1932, in a remarkable anticipation of Internet culture, theater theorist and playwright 

Bertolt Brecht imagined a future in which radio would cease to be merely a one-way 

"apparatus for distribution" and become "the finest possible communication apparatus in 

public life, a vast network of pipes" (Brecht 1964: 52). By the early 1990s, it had become 

possible to stream video images over the Internet at very low cost, and performance 

groups were quick to exploit video streaming technologies to create live multi-site 

performance events. In a 1994 production of Nowhere Band, George Coates used free 

CU-SeeMe video-conferencing software to allow three band members at various 

locations in the Bay Area to perform live with a Bulgarian bagpipe player in Australia for 

an audience in San Francisco (Illingworth 1995). In 1995, Cathy Weiss used the same 

software to create an improvised dance performance at The Kitchen in New York with 

the real-time participation of a video artist in Prague and a DJ in (Santa Monica Saltz 

2001a). In 1999 the Australian Company in Space created a live duet between a dancer in 

Arizona and her partner in Australia (Birringer 1999: 368–9). In 2001, the opera The 

Technophobe and Madman took advantage of the new high-speed Internet2 network to 

create a multi-site piece of theater. Half of the performers performed at Rensselaer 

Polytechnic University, while the other half performed 160 miles away at New York 

University. Two separate audiences, one at each location, watched the performance 

simultaneously, each seeing half the performers live and the other half projected on a 

large projection screen (see Mirapaul 2001). The Gertrude Stein Repertory Theater

(GSRT) is a company dedicated to developing new technologies for creating theater. In 

their production of The Making of Americans (2003), adapted from Gertrude Stein's 

novel, performers in remote locations work together in real time to create live 

performances in both locations simultaneously, with the faces and bodies of actors in one 

location being projected via videoconferencing on masks and costumes worn by actors in 

the second location. The GSRT draws a parallel between this process, which they call 

"Distance Puppetry", and Japanese performance traditions such as bunraku and ningyo 

buri that also employ multiple performers to portray individual characters. 

Telematic performance acquires its greatest impact when spectators interact directly with 

people at the remote site and experience the uncanny collapse of space first-hand. In the 

1990s, new media artist Paul Sermon created a series of interactive art installations 

joining physically separated viewers. For example, in Telematic Dreaming a viewer lies 

down on one side of a bed and on the other side sees a real-time video projection of a 

participant lying down on a second, identical bed in a remote location. Other installations 

place the remote participants on a couch, around a dining room table and at a seance table 

(Birringer 1999: 374). A more provocative example of a performance event that joins a 

live performer to the Internet is Stelarc's 1996 Ping Body: An Internet Actuated and 

Uploaded Performance, in which a muscle stimulator sent electric charges of 0–60 volts 

into Stelarc to trigger involuntary movements in his arms and legs proportionate to the 

ebb and flow of Internet activity. 

Conclusions and Queries 

The use of computers in the performing arts does not merely add a new tool to an old 

discipline. It challenges some of our most basic assumptions about performance. First, it 

blurs the boundaries between performance disciplines. When we watch a performer 

automatically triggering recorded fragments of dialogue as she moves across a stage, are 

we witnessing a piece of music, dance, or theater? Second, it blurs the boundaries 

between scholarship and creative practice. Is someone who extrapolates a complete set 

design, script, and performance from shreds of historical evidence to create a virtual 

performance simulation an artist or a scholar? When someone develops new artificial 

intelligence algorithms in order to create a dramatic interaction between a digital 

character and a live actor, is that person functioning as a computer scientist or an artist 

whose medium just happens to be computers? Finally, digital technology is challenging 

the very distinction between "liveness" and media. When a live performer interacts with a 

computer-generated animation, is the animation "live"? Does the answer depend on 

whether the animation was rendered in advance or is being controlled in real time via 

motion capture or with artificial intelligence software? Or do we now live in a world, as 

performance theorist Philip Auslander suggests, where the concept of liveness is losing 

its meaning? 

Note

1 Elsewhere I have distinguished between twelve types of relationships a production can 

define between digital media and live performers (Saltz 2001b). 


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12. 

"Revolution? What Revolution?" Successes and Limits 

of Computing Technologies in Philosophy and Religion 

Charles Ess 

Who can foresee the consequences of such an invention? 

Lady Ada Lovelace 

Introduction 

Computing technologies – like other technological innovations in the modern West – are 

inevitably introduced with the rhetoric of "revolution." Especially during the 1980s (the 

PC revolution) and 1990s (the Internet and Web revolutions), enthusiasts insistently 

celebrated radical changes – changes ostensibly inevitable and certainly as radical as 

those brought about by the invention of the printing press, if not the discovery of fire.

These enthusiasms now seem very "1990s" – in part as the revolution stumbled with the 

dot.com failures and the devastating impacts of 9/11. Moreover, as I will sketch out 

below, the patterns of diffusion and impact in philosophy and religion show both 

tremendous success, as certain revolutionary promises are indeed kept – as well as 

(sometimes spectacular) failures. Perhaps we use revolutionary rhetoric less frequently – 

because the revolution has indeed succeeded: computing technologies, and many of the 

powers and potentials they bring us as scholars and religionists have become so 

ubiquitous and normal that they no longer seem "revolutionary" at all. At the same time, 

many of the early hopes and promises – instantiated in such specific projects as Artificial 

Intelligence and anticipations of virtual religious communities – have been dashed 

against the apparently intractable limits of even these most remarkable technologies. 

While these failures are usually forgotten, they leave in their wake a clearer sense of what 

these new technologies can, and cannot do. 

To see this, I highlight historical and current examples of how computing technologies 

are used in philosophy and religion. We will see that philosophers have been engaged 

with computing from its beginnings in the dreams of Leibniz in the seventeenth century 1 

and the earliest implementations of electronic computers in the 1940s, 1950s, and 1960s. 

And, perhaps because of the clear connections between computing technologies and a 

range of classical philosophical practices (logic) and fields (epistemology, ontology, 

ethics, political philosophy, etc.), computation has enjoyed an increasingly central place 

in the philosophical literature of the past fifty years. Indeed, many philosophers speak of 

a "computational turn" – referring to ways in which computing technologies have given 

philosophers new kinds of laboratories for testing and refining classical debates and 

hypotheses. 

Similarly, religious studies scholars learned early to exploit the new tools – beginning 

with Father Roberto Busa's pioneering use of computers in the 1940s to analyze complex 

texts. More sophisticated versions of these early innovations gradually developed and 

became commonplace on today's desktops and even palm-held computers. In addition, 

the impact of computation in religion seems still more powerful in the larger domain of 

religious practice. In ways consistent with earlier technological innovations – especially 

such mass media as the radio and television – it is especially the religiously marginalized 

and proselytizers who have benefited from computation as instantiated in computer 

networks, i.e., the Internet and the World Wide Web. 

In both domains we will see the same general pattern: an early period of enthusiasm, one 

that rode high on revolutionary – even apocalyptic – promises of radical transformation, 

followed by a quieter period of diffusion and incorporation of computing technologies 

within philosophical and religious domains. Hegel would remind us that it is in this 

relative quiet, aided now by a rich history of examples, that we are able to evaluate more 

critically and carefully the strengths and limits of these remarkable technologies. 

Philosophy

"love of wisdom" (ϕιλoσoϕ α) – systematic and rational inquiry into what is 

(metaphysics, ontology), how we may know (epistemology), how we may think cogently 

and avoid error (logic), and, especially given some account of who we are as human 

beings and our relation(s) to the larger world (possibly including divinity/ies), how we 

should behave individually and in community (ethics, politics). 

The computational turn: logic, AI, ethics 

Arguably, computing is philosophy – specifically, the branch of philosophy concerned 

with logic. Our computing devices depend on the logical system developed by George 

Boole in the 1840s and 1850s; prior to Boole, a number of philosophers in the modern era 

– most notably, Leibniz – concerned themselves with the possibilities of machines that 

might automate reasoning as the manipulation of symbols (Dipert 2002: 148). Hence, it is 

perhaps not surprising that some of the earliest applications of computing technology in 

philosophy were precisely in the area of logic – in efforts both to exploit the computer as 

a logical calculating device (e.g., for assessing argument validity and generating valid 

conclusions from specified premises; ibid.) as well as to automate the teaching of logic 

(perhaps most notably by Patrick Suppes, beginning in 1963: see Suppes, Home page). 

Moreover, as computers automate and expand our ability to undertake logical analyses, 

they not only offer new ways of accomplishing classical logical tasks (from truth-table 

analysis through proofs to advanced logical applications – e.g., Tarski's World, etc.: see 

Barwise and Etchemendy 1999), they further open up distinctively new ways of 

exploring classical philosophical questions. One of the earliest, and most obvious, 

examples of this "computational turn" in philosophy is the rise of Artificial Intelligence 

(AI) – the effort to replicate human consciousness and reasoning through computing 

devices, prominent especially in the 1950s through the early 1990s. Computers provided 

philosophers with a laboratory in which to empirically test and refine hypotheses about 

the nature of reason and consciousness. Initially, so-called "hard" AI proponents believed 

and argued that machines would quickly outstrip human intelligence. It appears, however, 

that the hard AI agenda has largely moved to the margins – primarily because of repeated 

failures to live up to early promises. As in the natural sciences, however, both successes 

and failures are instructive: the successful efforts to instantiate at least some components 

of human reasoning in machines has helped philosophers sharpen their sense of how far 

computation and consciousness overlap – while the failures help demarcate how 

computation and human reasoning remain intractably distinct from one another. Indeed, 

there is a recent turn in philosophy – in fields as diverse as hermeneutics, 

phenomenology, and feminism – towards embodiment as a key theme of exploration: 

philosophers such as Albert Borgmann and Hubert Dreyfus use an understanding of who 

we are as embodied creatures to explore the strengths and limits of technology, including 

computing technologies, in contemporary life. In particular, Dreyfus recognizes the 

(limited) achievements of AI and the (limited) pedagogical advantages of distance 

learning via the Web and the Internet, but he further argues that these technologies do not 

help us acquire our most distinctive and important human capacities – those of making 

commitments, taking risks, and exercising phronesis (Aristotle's term for practical 

judgment and wisdom) in our ethical and political lives.

Indeed, the creators of modern computing technology have been aware from the outset 

that these new devices raise, precisely, questions of ethics with regard to the use of these 

systems, politics with regard to their potential social and political impacts, etc. As Bynum 

points out (2001), Norbert Wiener, in what amounts to the first book of computer ethics 

(Wiener 1950), recognized that computing technologies will impact, for better and for 

worse, life and health, knowledge and science, work and wealth, creativity and happiness, 

democracy and freedom, and inter/national concerns with peace and security. In Bynum's 

view, moreover, Wiener sets the agenda for the field of computer ethics – a field that 

begins to emerge only slowly in the 1960s and 1970s, but now includes an extensive 

literature, including the journal Ethics and Information Technology. More broadly, the 

computational turn is now well documented not only in AI and ethics, but also, for 

example, in philosophy of mind, philosophy of science, epistemology, ontology, and so 

forth (Bynum and Moor 1998, 2003). Indeed, the late 1990s and early twenty-first 

century saw the emergence of philosophy of information as one way to provide a 

systematic overview of the impact of computing in virtually every domain of philosophy 

(Floridi 2003). 

Hypertext 

In the 1980s, both philosophers and other humanities scholars were excited by a new 

form of "non-linear" text made possible by computing technologies – hypertext. While 

the theoretical reflections and software experiments of such hypertext pioneers as Jay 

David Bolter and George Landow were most prominent – at least some philosophers 

were likewise quite interested in the potential of hypertext to open up at least alternative 

forms of argument. The best known of these, David Kolb, has in fact argued that 

hypertexts will make possible the recovery of argument forms (e.g., Hegel's dialectic) 

which are only awkwardly expressed in the (largely) linear frameworks of print. 

CD-ROM databases (ethics, history of philosophy) 

After appearing in the 1980s, CD-ROMs seemed the perfect medium for realizing 

extensive hypermedia programs and databases. Two examples can be mentioned here. 

First, effective hypermedia resources have been developed for exploring and teaching 

ethics (e.g., Cavalier et al. 1998). These resources are especially useful as they provide 

video documentary and interviews to bring more abstract discussion of general principles 

down to the fine-grained contexts of real persons facing specific ethical problems in 

particular contexts. 

Second, while originally defined as a Greek classics project – i.e., focused on collecting 

primary and secondary literary and historical texts, as well as architectural, historical, and 

cultural resources (including extensive visual images of important sites, artifacts, etc.) - 

the second edition of the Perseus Project (Perseus 2.0 1996) also included both the Greek 

and English texts of Plato and Aristotle. Like its counterparts in Biblical studies, the 

Perseus CD-ROM not only provides an electronic library of these primary texts, but also 

allows for text searches and analyses. For example, I can find – more or less instantly – 

every example of Plato's use of the term ("cybernetes", a steersman, or

pilot, and thus figuratively, the Divine as directing the destiny of humans – and the root, 

not coincidentally, of "cybernetics") as well as related terms. Through hypertext linking, 

the program can further take me to each example in the Platonic text, as well as provide 

morphological analyses. Such searches, of course, accomplish in minutes what might 

otherwise take weeks, months, or years of reading. 

At the same time, however, while these are extraordinary resources and tools, they are 

also quite limited. While the primary and secondary texts made available on CD-ROM 

are extensive – and supplemented, of course, by a constantly growing electronic library 

on the Web – only a relatively small percentage of the literatures important to 

philosophers has been digitized. Moreover, word searches and morphological analyses 

are important components of scholarship – but they are only a very small component of 

our research, reflection, and writing. As with the specific history of AI, philosophers 

(and, it appears, religion scholars) are gaining a more realistic and nuanced appreciation 

of the strengths and limits of computing technologies in their disciplines, precisely as the 

successful resources and tools simultaneously mark out what the technologies cannot do 

(at least so far). 

Computer-mediated communication 

The sorts of communication most familiar to us in terms of the Internet and the Web also 

serve as a philosophical laboratory, one that allows philosophers to revisit classical 

questions in the domains of ontology, epistemology (including semiotics, hypertext, and 

logic), the meaning of identity and personhood (including issues of gender and 

embodiment), and ethical and political values (especially those clustering about the claim 

that these technologies will issue in a global democracy vs. the correlative dangers of 

commercialization and a "computer-mediated colonization": see Ess 2003). 

The Internet and the World Wide Web 

Interestingly enough, major projects in both philosophy (Perseus) and religious studies 

(Diana Eck's On Common Ground) that began on CD-ROM have migrated to the Web to 

join additional "electronic libraries" and online search engines. Two significant examples 

of online philosophical resources are the Hippias search engine and website, and 

Lawrence Hinman's Ethics Updates site. 

Finally, these computer networks have made possible what is now the presumed 

environment of philosophical scholarship – namely e-mail, listservs, extensive online 

databases and search engines, and countless websites (of varying quality) that collect and 

"publish" (often) significant resources. It is commonly observed that, in contrast with the 

high-profile projects and experiments, in the end, it is the relatively low-tech tools that 

make the most difference – in this case, e-mail and listservs. Despite the explosion of 

scholarly resources and specialized tools available online and on CD-ROM, it remains 

debatable as to whether the computer revolution has markedly improved philosophical 

debate, scholarship, or insight (Dipert 2002). Nonetheless, the ability for scholars, 

geographically remote from one another and positioned at institutions of varying

esources and prestige, to communicate directly with one another through e-mail and 

listservs organized by interests and specialties is arguably "revolutionary" indeed. At the 

same time, however, this ability is now a commonplace. 

Religion 

"rebinding" – religio – between the human and the sacred, expressed both individually 

and in community in terms of beliefs, values, practices, rituals, etc.; religious studies – 

academic studies of multiple aspects of religion, including studies of scriptures and 

beliefs, as well as through a range of disciplines such as sociology, psychology, 

philosophy, philology, history, etc. 

While philosophers were closely involved with the development and early uses of 

computing technologies because of their disciplinary focus on logic, religious scholars 

were among the first to explore applications of computing in textual analysis. More 

recently, however – beyond the use of the Web and the Internet by believers and 

discussants – there appear to be comparatively fewer religiously oriented computing 

projects (perhaps because religious studies are among the most marginalized and 

underfunded in the academy?). As a representative example: the Institute for Advanced 

Technology in the Humanities at the University of Virginia – arguably one of the most 

important centers for developing computing-based humanities applications – lists only 

two projects (out of some fifty or so) that focus on some aspect of religious studies. On 

the other hand, the open communicative environments of the Internet and the Web, while 

appropriated in much the same way among religious scholars as among philosophers, by 

contrast have been taken up with explosive energy by more or less every religious 

tradition whose representatives enjoy Internet access. 

The use of computing technologies in religious scholarship 

In the 1940s, Father Roberto Busa began developing techniques for encoding complex 

texts in ways that could be manipulated by computers – first of all, to develop a 

concordance for the corpus of Thomas Aquinas, followed by a more extensive project to 

develop a hypertextual edition of Aquinas that allowed for multiple levels of linking and 

comparison (Thomae Aquinatis Opera Omnia cum hypertextibus in CD-ROM). At least 

one Bible concordance (for the then new Revised Standard Version of the Bible) was also 

generated in the 1950s using a computer. As computers became (somewhat) less 

expensive and more widespread through the 1960s and 1970s, religious scholars began to 

develop ways of exploiting the computer's storage and processing abilities to undertake 

complex text analyses (Harbin 1998). Just as philosophers hoped to make the machine 

take over foundational but repetitive tasks of logical calculation, so their colleagues in 

religious studies sought to use the machine to take on the tedium of word counts and 

comparisons. 

As microcomputers and CD-ROMs made processing and storage increasingly 

inexpensive in the 1980s, descendants of these early text-base and concordance projects 

moved to the desktops and laptops of religious scholars. First of all, Bible database

projects flourished – i.e., collections of Bible texts, translations, and commentaries, with 

basic computing features (indexing, word searches, note-taking) that support both 

elementary and more advanced sorts of grammatical and textual analysis. These range 

from the historically oriented History of the English Bible (Beam and Gagos 1997) to 

such significant resources as Bible Works. These new technologies further allow other 

sorts of databases – for example, Diana Eck's On Common Ground CD-ROM and 

subsequent website that documents religious diversity in the USA. 

On the one hand, these resources fulfill some of the fondest dreams of scholars. 

BibleWorks, for example, allows for complex searches through multiple Bibles – 

translations as well as critical editions in Greek and Hebrew. A scholar can accomplish in 

minutes an inquiry that would otherwise take weeks or months. This is the analogue to 

the way computing technologies have indeed revolutionized mathematics, the sciences, 

and logic by turning over to the machine repetitive tasks that would otherwise take 

humans months, years, and lifetimes to perform (cf. Hardmeier 2000). On the other hand, 

as in philosophy, the impact of these new resources and abilities on the quality and 

quantity of scholarship remains a very open question. In particular, computer-adept 

scholars observe that these resources exploit only the most basic potentials of the 

computer, and – echoing the Socratic critique of the technology of writing as leading to 

the appearance, but not the substance, of wisdom – run the danger of giving the untutored 

amateur the appearance, if not conviction, that s/he now knows as much as any Bible 

scholar. 

More fundamentally, the emergence of these new technologies themselves – again, 

especially as interpreted through the lenses of postmodernism – all but required scholars 

in religion to consider and respond to what many began to see as the emerging 

"secondary orality of electronic culture" (so one of the premier theologically oriented 

theorists of new media: Walter Ong, 1988: 135–8, cited in O'Leary and Brasher 1996: 

246). In response, the American Bible Society (after bringing out one of the first CD- 

ROM Bible databases) undertook an ambitious series of projects to "transmediate" 

important Christian narratives – i.e., to translate these, using scholarly approaches and 

principles of translation, into the multimedia environments made possible by computing 

technologies; to synthesize music, visual images, and scholarly resources as an 

environment for "telling the story" in a way intended to be attractive especially to 

younger people, who are oriented primarily to electronic visual media (American Bible 

Society 1995). Despite considerable investment and remarkable talent, however, these 

projects have met with only limited success in the religious marketplace. 

Religious scholarship on the Web 

As for philosophers, the Internet and the Web have diffused into the commonplace 

practices and environment of religious scholars. Beyond the explosive development of 

sites on the Web by diverse faith communities, there is also to be found a reasonably 

extensive but largely pedestrian use of the Internet and the Web to support 

• listservs on scholarly topics of interest to religion scholars and lay persons;

• sites for religious studies professionals (e.g., the American Academy of Religion) that 

offer relatively little in terms of use or pointers towards use of computing technolo gies, 

but rather make use of the Web – sensibly – as an easily updateable archive for such 

things as their online syllabus project, etc.; and 

• portal sites (e.g., Oxford University [Fraser 2000], the Society of Biblical Literature's 

Electronic Publications and Technology Resources for Biblical Studies) that list both 

institu tionally based resources and often very rich and helpful sites put up by individual 

scholars. 

As with philosophers, these now relatively low-tech uses of the computing technology 

may have the most significance as they expand access to resources and the ability to 

communicate with geographically distant scholars who share similar interests. 

The Internet, the Web, and religious communities 

Whatever the potentials and impacts of computing technologies for religious scholars, 

religious communities have exploited the Internet and the Web with extraordinary 

energy. This religious colonization of cyberspace began first of all as the Internet and the 

Web afforded safe havens for those otherwise at the margins of North American religious 

life, e.g., Wiccans, Pagans, New Age seekers, etc., as well as (if more gradually) 

representatives of the world traditions such as Islam, Buddhism, Hinduism, etc. (O'Leary 

and Brasher 1996; Larsen 2001). 

This enthusiasm was fueled (as elsewhere) by the rise of postmodernism, especially as 

postmodernism was theoretically conjoined with the technologies of hypertext and then 

the Web through such theorists as Jay David Bolter and George Landow. As 

postmodernism made its way into religious scholarship and theology, it was embraced 

especially by Evangelicals and Pentecostals, as postmodernism promised to unseat 

modern rationalism – and thus shift epistemological legitimacy and authority to emotive 

experience (e.g., the feeling of being saved), and undermine rationalist approaches to 

Scripture such as the historical-critical method, thereby eliminating the chief nemesis of 

Fundamentalist interpretation. More broadly, this trajectory has led to a number of 

insightful analyses of the relationship between new media and religion – including texts 

that both celebrate the media's ostensibly liberatory/revolutionary potentials (e.g., 

Careaga 2001) and those that directly challenge postmodernist communication theories 

by documenting how traditional religious beliefs and assumptions have shaped and 

constrained the development and use of the new technologies (e.g., Davis 1998; see Ess 

2001, 2004 for an overview of this development and relevant literature). 

Currently, the plethora of sites on the Web devoted to religion staggers the imagination: a 

Google search on "religion", for example, will turn up some 13–8 million hits. As yet, 

there is no authoritative study of this massive inventory. Two studies in progress suggest, 

however, an interesting pattern:

Evangelical/Pentecostal/Fundamentalist sites more fully exploit the interactive nature of 

online communication to proselytize; while sites representing the online "face" of more 

mainstream and conservative traditions – including the Roman Catholic Church and the 

Greek Orthodox Church – largely provide extensive databases of authoritative texts and 

pronouncements, and relatively little interactive opportunity. 

This pattern, moreover, is consistent with other studies that show, for example, that initial 

grassroots efforts to exploit the Internet and the Web for political activism are soon 

squeezed out as extant power centers learn, if somewhat more slowly, how to use the 

Web and the Net to re-establish their dominance and centrality online. Similarly, more 

ecumenical proponents of online religion hope that a global Internet may facilitate a 

global, dialogical culture that fosters the many voices of diverse religious traditions in a 

new pluralism. But the emerging patterns of use of the Internet, while giving a certain 

advantage to previously marginalized traditions, rather largely reflect and preserve the 

existing religious landscape, i.e., one marked far more often by allegiance to one's own 

tradition and proselytizing on its behalf. 

Concluding Remarks 

Should this overview be even approximately correct, it is then notable for two reasons. 

First of all, through the larger perspectives of research (including cross-cultural research) 

on computer-mediated communication (CMC), this curve from initial enthusiasm to more 

pedestrian applications fits the larger pattern of development from the 1980s and 1990s to 

the late 1990s and early noughties (so the British say) – i.e., from the heyday of 

postmodernism to a "post-post-modern" period that represents more of a hybrid between 

postmodernism and whatever came before it. Secondly, this pattern suggests that, indeed, 

the revolution has succeeded in certain remarkable ways – so much so that we no longer 

regard computer-based resources and tools as "revolutionary", but simply as "normal" 

elements of our lives – while at the same time, the multiple failures in philosophy and 

religion to exploit computing technologies have left a significant portion of our work and 

lives relatively untouched. 

In my own case: I still marvel at having nearly instantaneous access to a remarkable 

library of important texts – both Biblical and philosophical – not only on my desktop 

computer, but also my palm-held computer, and that I can undertake searches that help 

me locate a familiar quote, and perhaps uncover new patterns and insights. In these ways, 

these computer-based resources and tools certainly enhance my scholarship and teaching. 

At the same time, as a number of contemporary observers of technology caution, the 

affordances of these technologies – what they make easy for me to do – thereby 

encourage me to pursue the paths they facilitate, and perhaps thereby discourage other 

aspects of scholarship and teaching that, as yet unreduced to computational algorithms, 

remain comparatively more difficult. As well, the very ubiquity and commonplace 

character of these technologies may discourage us from attending more carefully to 

whatever more subtle and long-term consequences they may have for us as scholars and 

as human beings.

But even these critical reflections, finally, may be an indication of the success and 

maturity of the computing revolution in philosophy and religion: perhaps like other 

revolutions we now take for granted, the computing revolution has proceeded far enough 

along to allow us to critically evaluate both its strengths and its limits. 

See also chapter 4: Classics and the Computer; chapter 8: Literary Studies; chapter 10: 

Multimedia. 

Notes 

1 In the Science Museum (London) exhibition "Charles Babbage and his Calculating 

Engine", mounted on the occasion of the bicentennial of Babbage's birth and the 

Museum's realization of Babbage's "Difference Engine No. 2", the following is attributed 

to Leibniz in 1685: "It is unworthy for excellent men to lose hours like slaves in the labor 

of calculation which could safely be relegated to anyone else if machines were used." 


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13. 

How the Computer Works 

Andrea Laue 

Computing humanists engage digital technologies in their studies of humanistic artifacts. 

In this chapter, we will engage the computer as artifact, seeking a better understanding of 

how histories of culture and of technology converge in our personal computers (PCs). 

First, we will explore how computers "work", how their largely electrical mechanisms 

process data. Second, we will consider how computers "function", how in theory and in 

practice humans figure their working relationships with these machines. The computer 

was built to be a sophisticated device for the manipulation of symbols, and, at several 

levels, it is just that. But this description does not acknowledge that computers are also 

symbols, nor does it reveal the extent to which computers employ technologies symbolic 

of larger historical and cultural trends. In order to understand how computers function, 

we must understand how humans use computers to manipulate symbols but also how the 

mechanisms within the machine manipulate human users. I will consider here "Wintel" 

machines, desktop PCs running on technology largely developed by Microsoft and Intel. 

These systems draw extensively on John von Neumann's stored-program architecture 

(1945) and incorporate primarily Douglas Engelbart's input devices. While popular 

rhetoric encourages users to imagine digital technologies outside of various histories of 

technology, of culture and of labor, I argue here that those histories are essential to a 

proper understanding of how a computer functions. 

How the Computer Works 

In 1945 John von Neumann described a radical new architecture for computers, and 

although he was not imagining personal computers, his design largely informs the 

machines we use today. I will adopt his concept of the five logical parts of a computer – 

control, processing, storage, output, and input – and discuss them in the context of a more 

mundane narrative – just what happens when one types "Hi!" as the first line of an email. 

There is not a one-to-one correspondence between von Neumann's logical parts and 

the hardware components present in modern machines, so I will demarcate a section for 

each logical part, providing a basic explanation of the role of that part, and explain in 

greater detail how one or two hardware components perform the operations characteristic 

of that logical part. My choice of components and sequence of discussion will be guided 

by the framing narrative, the story of how one types and sends an e-mail that says "Hi!" 

but I will occasionally offer asides rather than omit an essential component. 

Input 

Contemporary computers offer a range of input devices, including keyboards, the mouse, 

floppy disk drives, modems, and USB (universal serial bus) ports. Such devices are often 

called peripherals, a term that describes any component, internal or external, which adds

hardware capabilities to the basic design of the system. Each of these peripherals offers a 

means of transferring data to the machine, an operation that may seem mundane now but 

was key for von Neumann as previous computers used only punchcards and paper tape. 

Modern input devices utilize several different standards and media, but the principles 

behind the transfer of data share fundamental characteristics. Since the user is likely to 

use a keyboard to input the characters "H", "I", and "!", we will start there. 

Although most keyboards look quite similar from the outside, there are at least five 

different "insides" common today. These differ in the mechanisms engaged by the 

striking of a key, and the most common setup, the rubber dome keyboard, will be 

described here. Looking inside this keyboard we find three plastic sheets: one with 

conductive tracks, one that acts as a separator, and a final conductive layer. Two of these 

together form the key matrix, a circuit board in two layers – a bottom layer containing 

gaps that are completed when the top layer is pressed down on to it. The third layer is 

shaped like the keyboard, with pliable rubber domes corresponding to each rigid plastic 

key. These domes contain a hard carbon center that, when depressed, completes a circuit. 

When released, the rubber dome regains its original shape, breaking the circuit. 

Keyboards have simple microprocessors that constantly scan the circuit board looking for 

completed circuits. These detect both an increase and a decrease in current, the pressing 

and the releasing of a key. When a completed circuit is found, the microprocessor 

compares the completed circuit with its programmed character map, a sort of seating 

chart of characters. If more than one circuit is completed, the microprocessor checks to 

see if that combination of keys is recorded in the character map. The character map points 

to a specific scan code for the completed circuit, and it is this scan code that is sent to the 

computer for translation into the appropriate ASCII bit string. The scan codes are 

standard, but the character map is specific to a particular keyboard. The real significance 

of the character map lies in the fact that it separates the function of the keyboard from the 

letters printed on the plastic keys. If one reprograms the character map, striking the key 

labeled "H" might actually signal the microprocessor to send the code for the letter "o" to 

the machine. This means that computer keyboards can be reprogrammed for almost any 

arrangement of keys; although the peripheral is stamped with a QWERTY keyboard, 

nothing prevents one from actually using a Dvorak arrangement. 

The keyboard stores the scan code in its memory while it sends an interrupt to the 

computer's BIOS (basic input/output system). This interrupt tells the computer to stop 

what it is doing and to receive the queued signals from the keyboard. The keyboard 

transmits the scan code to the keyboard controller, a circuit in the BIOS that forwards 

keyboard data to the operating system (OS). It is the keyboard controller that decides if 

the signal represents a system-level command or data intended for the application 

currently in use. In our case, the OS will send our input to an e-mail client. 

But what exactly is sent between the keyboard and the BIOS, between the BIOS and the 

OS? While the characters appearing on the screen as you type match the characters 

printed on the keyboard, the machine never knows them as letters and punctuation marks. 

All data pass through the computer as nothing more than pulses of electricity; the

machine recognizes two states: with charge and without charge. We often represent these 

two states with two digits, "1" and "0" respectively. (This actually reverses the historical 

representation: logical models of computing devices used binary logic previous to the 

actual construction of electromechanical or purely electrical machines.) This is also the 

origin of the term "bit" and by extension "byte." One bit of information is one switch, one 

1 or 0, one "place"; eight bits is one byte. So our keyboards and our computers 

communicate via electricity, and mathematicians and logicians have developed methods 

for translating language (symbols) and many thought processes into binary code. 

The most pertinent example for our purposes is the American Standard Code for 

Information Interchange (ASCII). The ASCII character set maps the 256 most common 

characters to binary equivalents, a unique string of eight digits. This is also the origin of 

the byte as a measurement of storage capacity, as one byte of information is sufficient for 

representing any character in the ASCII set. Thus, ASCII translates the Western alphabet 

into code that can be transmitted as pulses of electricity. While you type into your e-mail 

client, the characters are actually being stored as bit strings, as sequences of Is and Os. 

Let us return to our example, "Hi!"; in binary code: 101000 1101001 0100001. 

Standards-based e-mail clients and terminals understand all characters as bit strings, and 

it is bit strings that are transmitted from the keyboard to the computer. 

Control 

We will begin our discussion of the control unit at the motherboard, the physical 

component that houses the control and processing units. Most of the control unit is 

located in the chipset, a collection of microprocessors contained in the CPU (central 

processing unit). This chipset includes most essential components of the control unit and 

is usually visible on the motherboard. The motherboard is an intricate printed circuit 

board with miles of copper circuit paths called traces and numerous sockets for 

connecting peripherals. Together the traces constitute the bus, the metaphorical highway 

system that carries electricity between the processor, the memory, and the various 

peripherals. Buses are the physical connections that transfer data as electronic pulses, and 

these connections may be a fraction of an inch in length within the CPU or several inches 

long when stretching from the processor to an expansion slot. As we will learn later, the 

bus is an important factor of a computer's overall processing speed. 

Let us return to our depressed keys, our scan codes waiting in the BIOS, the component 

responsible for making sure that the various parts of the computer work together. 

Actually a bit of software code, the BIOS is stored in the chipset. Most computers 

employ three levels of software: the OS; the device drivers that include instructions for 

operating particular peripherals; and the BIOS that translates and transmits instructions 

and data between the OS and the drivers. The BIOS handles the transmission of the 

electrical signals sent between the various peripherals. So the BIOS receives our scan 

code, translates it to the appropriate ASCII code, and sends it to the OS. 

Before we move on from the BIOS, let us review its role in the boot process, the routine 

followed each time the computer is powered on or restarted. Most likely, the boot process

is the only time you'll have any direct experience with the BIOS, as you can watch its 

work on the screen as your computer starts up. During the boot process, the PC checks its 

components and prepares the machine to load the OS. The first step in the boot process is 

the power-on self-test (POST), which begins when the pressing of the power button sends 

the first electrical pulse to the microprocessor, telling it to reset its registers and to initiate 

the boot program stored in the BIOS. This boot program invokes a series of system 

checks, testing to see that components such as the system bus and clock are functioning 

properly. Next, the BIOS contacts the CMOS (complementary metal oxide 

semiconductor), a tiny bit of memory located on the motherboard containing basic 

information about the components of the computer. After that, the video adapter is 

checked and, if all is well, something appears on the monitor. Next is the test of the 

system RAM, during which the machine will seem to count each bit of memory. Then the 

POST continues through the various system components, checking functionality and, 

when appropriate, loading a device driver into the BIOS, ensuring proper and rapid 

functioning when that device is called by the OS. In most cases, the BIOS will then print 

to the screen a summary of the system configuration, which will flash by just before the 

OS starts. Then the POST contacts the CMOS again looking for storage devices tagged as 

boot devices, pieces of hardware that might contain an OS. Finally, the BIOS checks the 

boot sector of those devices for a master boot record that will take control of the PC for 

the remainder of the startup. 

Another important element of the control unit is the system clock. The clock is really an 

oscillator, a tiny quartz crystal that vibrates when electricity is applied to it. Thus the 

fundamental mechanism is the same as that which drives most digital watches and 

electronic clocks. The vibrations of the clock cause pulses of electricity to flow through 

the motherboard, moving bits from place to place. Thus the rate of vibration is the 

shortest time in which an operation can be executed, and this rate is often called the clock 

speed. Many modern computers operate at speeds greater than 1 GHz (gigahertz), or 

more than 1 billion cycles per second. The clock is necessary because different parts of 

the computer, even different operations within the processor, operate at different rates. It 

is the job of the clock to act as a master control, to synchronize the flow of bits through 

the machine. The bits that compose our greeting will be transmitted according to the 

rhythm enforced by the oscillator, by the "ticking" of the clock. Our three bytes of 

information will take a minimum of twenty-four cycles – an unimaginably small fraction 

of a second – for each "step" between the keyboard and the modem. 

Processor 

The microprocessor is probably the most recognizable component of the computer. Intel's 

first microprocessor, the 8080 introduced in 1974, had 6,000 transistors; Intel's most 

recent microprocessor, the Pentium 4, includes 42 million transistors. The 

microprocessor, also called the central processing unit (CPU), has three basic 

capabilities: to perform basic mathematical operations, move data from one memory 

location to another, make decisions to jump from one instruction to another. At the heart 

of the processor is the arithmetic/logic unit (ALU), which performs very simple 

operations such as adding, subtracting, multiplying, dividing, and completing some basic

logical operations. And each of these operations is performed on bit strings like the one 

that constitutes our e-mail greeting. Data and instructions are all translated into Is and Os, 

pulses of electricity, and the processor is a mass of transistors, of switches, that can hold 

and manipulate these charges. Our e-mail greeting will flow through the CPU even 

though it will not be manipulated during its passing. 

Storage 

When we use the word "storage" we usually mean hard drives, floppy disks, or other 

relatively permanent but slow means of storing data. There are four major types of 

internal storage in modern computers, listed in order of decreasing speed and cost: 

registers, cache, random access memory (RAM), and hard disk drives (HDD). Registers 

and cache are located in the CPU or on the motherboard and come in relatively small 

amounts, say 512 MB (megabytes). In contrast, hard drives often have 80 GB (gigabytes) 

of storage for a fraction of the cost of the cache. We will learn a bit about RAM and hard 

drives, two locations where our greeting might be stored. 

A RAM chip is an integrated circuit made of millions of pairs of transistors and 

capacitors that are linked to create a memory cell. Each cell can hold one bit of data. A 

capacitor holds a charge, and the transistor acts as a switch, allowing the control circuitry 

to change the state of the capacitor. These capacitors and transistors are arranged as a 

grid, with the columns connected by bitlines and the rows connected by wordlines. In 

both cases, a "line" is a microscopic strand of electrically conductive material etched into 

the RAM chip. When a burst of electricity is transmitted through a bitline, every closed 

transistor, each switch that is "on", charges its capacitor. The combination of bitlines and 

wordlines defines an address for each switch and capacitor. This also accounts for why 

this type of memory is called random access: you can access any memory cell directly if 

you know its address. Most RAM is volatile, or dynamic, meaning that the capacitors are 

always leaking their charge and thus must be refreshed constantly. RAM is inserted 

directly into the motherboard, most often in the form of a dual in-line memory module 

(DIMM) that holds up to 128 MB of memory. 

A hard disk drive is one of the few computer components that is mechanical as well as 

electronic. Hard drives contain a collection of platters and heads arranged so that the gaps 

between them are smaller than a human hair; thus hard drives are very sensitive to dust 

and jostling. The round platters are made of aluminum or glass coated with a magnetic 

material. The surface of the platter is divided two ways, as a series of concentric circles 

and a collection of wedges, and the resulting pieces are called sectors. Each sector 

contains a set number of bytes, usually 256 or 512. A single hard drive may contain as 

many as eight platters, and platters are stacked on a central motorized spindle that spins 

the platters so that the disk heads can read and write data. The heads write data by 

aligning the magnetic particles on the surface of the platter; the heads read data by 

detecting the magnetic charge of particles that have already been aligned. Again, all data 

is stored as a charge, as a 0 or a 1.

How does the drive manage those eight disks, keeping track of where files are stored and 

not inadvertently overwriting important data? Every Windows hard drive has a virtual file 

allocation table (VFAT), a sort of database of addresses for files. The VFAT records the 

sectors holding any saved file and offers empty sectors for writing. When you delete a 

file, the hard drive doesn't actually modify the data sectors. Rather, it edits the VFAT, 

removing the entry for that file. Thus the file is invisible but not erased. Depending on 

our e-mail client, our greeting has either been saved to RAM or to the hard drive while it 

waits to be sent. 

Output 

Output devices include monitors, printers, modems, Ethernet cards, and speakers. We 

will forgo a discussion of monitors in favor of a brief account of modems, the hardware 

device through which most of us were introduced to the Internet. The modem is 

particularly interesting because it is a point of union, of conversion between the digital 

and the analogue, between new and (relatively) old technologies of communication. The 

modem gets its name from its primary tasks: modulating and demodulating. As we 

continue to discover, the computer operates exclusively with Is and Os, with a digital 

code. By contrast, phone lines work with an analogue signal, an electronic current with 

variable frequency and strength. It is the job of the modem to demodulate data from a 

phone line, to convert the wave to a series of Is and Os, and to modulate the data 

generated by the computer, to convert the Is and Os to a continuous wave. This process 

accounts in part for the limited speed of modems. The analogue signal in the phone line 

cannot change frequencies as quickly as a computer can send bits of information, yet the 

signal needs to change for every bit sent. Clever engineers devised techniques for 

avoiding this bottleneck, including defining four frequencies and four associated 

combinations of bits – 00, 11, 01, 10 – and experimenting with variations in amplitudes, 

but the physical medium of the telephone line still poses material constraints on the speed 

of transmissions. This means that our e-mail greeting, stored temporarily in RAM or 

perhaps saved to the hard drive, and displayed on the screen, will need to be translated to 

analogue before being sent to a friend. 

Review 

Let us review what just happened. We press a few keys on our keyboards, and each strike 

of a key completes a circuit mapped on the key matrix. Comparing the completed circuit 

to the key matrix, the keyboard's microprocessor selects a scan code for each character 

and transmits it to the computer's BIOS for decoding. After deciding that the keyboard 

data are bound for our e-mail client, the BIOS transmits the ASCII bit string for each 

character. These bit strings are stored temporarily in RAM or, if the e-mail has been 

saved, more permanently on our hard drive. When we send our e-mail, the ASCII bit 

strings will be sent to the modem, which will modulate the data before transferring it to 

the phone line. 

Ending our discussion of how a computer works with the modem emphasizes the 

significance of the peripheral as a transitional device. Modems operate at the boundary

etween analogue and digital technologies, between nineteenth- and twentieth-century 

technologies of communication, between transmission and simulation. At multiple levels 

the machine is involved in sophisticated translations of information, as is evidenced by 

the fact that you really do not need to understand how a computer works to use a 

computer. A great accomplishment of software developers is the extent to which we can 

"talk" to computers using our own language, the extent to which the code, the Is and Os, 

the electric pulses are hidden. Many pioneers in computing technologies aspired to build 

machines capable of sophisticated symbol manipulation, as they understood that such 

skill – basically the ability to use language – is essential to human intelligence. Yet in 

simulating this capacity they added several layers of symbols, several additional levels of 

abstraction: printed letter to ASCII codes to a series of pulses. 

How the Computer Functions 

To begin, I will restate my guiding distinction: "work" describes the electronic processing 

performed by the machine; "function" refers to the theoretical and practical relations 

between the computer and its human operators. To explore this distinction, I focus on the 

system clock and the keyboard, but my evidence could have included several other 

components, most notably computer memory (Douwe 2000; Locke 2000; Williams 

1997). I discuss the history of the adoption of and adaptation to these technologies, 

looking at how the computer as artifact reproduces metaphors of mind and incorporates 

bodies into mechanized routines. Relating the system clock and the computer keyboard to 

previous implementations of similar technologies, I explore motivations and rationales 

for the choices behind these particular mechanisms. Pioneers of digital technologies often 

thought their designs radical and their machines revolutionary, yet these inventions 

incorporate conventions and habits embedded in previous technologies. In this sense, 

digital technologies can be seen as extensions of numerous historical trends. 

System clock 

Let us begin by returning to John von Neumann, focusing this time on the biological 

metaphors that frame his understanding of his radical architecture. Trained in logic and 

mathematics, von Neumann's interest in information processing was strongly influenced 

by the work of Warren McCulloch and Walter Pitts, in particular the article "A Logical 

Calculus of the Ideas Immanent in Nervous Activity" (1943). Although he acknowledged 

that the McCulloch-Pitts model oversimplified neural activity, von Neumann was 

intrigued by the parallels proposed between man-made and organic systems (Aspray 

1990: 180–1). In 1944 von Neumann joined the likes of McCulloch, Pitts, and Norbert 

Wiener to form the Teleological Society, a research group "devoted on the one hand to 

the study of how purpose is realized in human and animal conduct and on the other hand 

how purpose can be imitated by mechanical and electrical means" (Aspray 1990: 315). 

Although the Teleological Society disbanded, many of its founding members initiated the 

Macy Conferences, probably the most famous early discussions of artificial intelligence. 

Looking at von Neumann's First Draft of a Report on the EDVAC (1945), the first 

publication describing the stored-program architecture, one notices his analogies to

organic systems. For instance, in this report the storage unit of a computer is first called 

"memory", and other logical parts are called "organs." Von Neumann understood organic 

systems to be highly complex, to employ parallel processing and to be adept at switching 

between analogue and digital modes of computation, and he planned to simulate the 

organic by implementing a few key principles – process hierarchies, serialization, 

regulation, and automation. Along with others in his field, von Neumann rationalized the 

organic and made discrete the continuous in an effort to mimic the intellect of man. 

Aware that current technology couldn't accomplish the sophistication of organic systems, 

von Neumann intended to design an architecture that could evolve sufficient complexity. 

He modeled the workings of his computer after the brain, hoping that the machine would 

function like – or at least with – man. In an attempt to replicate the functioning of the 

human mind, von Neumann built a model of the brain that worked in ways quite different 

than its organic precursor. The mechanics of the computer introduce several technologies 

foreign to the mind yet not so unfamiliar to the history of its acculturation and regulation. 

Our brief investigation of these mechanics will focus on the system clock, a device 

central both to the work and to the function of the computer. Historians of technology 

often point to the clock as a mechanism central to the modernization of the West. In his 

history of technology Technics and Civilization, Lewis Mumford (1934: 14) writes: "The 

clock, not the steam-engine, is the key-machine of the modern industrial age. For every 

phase of its development the clock is both the outstanding fact and the typical symbol of 

the machine: even today no other machine is so ubiquitous." Mumford published his 

book a decade before von Neumann designed his stored-program architecture, the first 

computer architecture regulated by a control unit called a "clock." In his Report, von 

Neumann describes the electromechanical devices used to control computers previous to 

the EDVAC and then proposes an alternative for his new machine: "Alternatively, 

[computing instruments] may have their timing impressed by a fixed clock, which 

provides certain stimuli that are necessary for its functioning at definite periodically 

recurrent moments" (1945: 24). Even at this early stage von Neumann suggested that this 

"clock" might actually be an electronic oscillator controlled by a crystal. Two years later, 

von Neumann, Goldstine, and Burks described dynamic circuits, regulated by electric 

pulses emitted from a central source called a "clock": "Since the timing of the entire 

computer is governed by a single pulse source, the computer circuits will be said to 

operate as a synchronized system" (Burks et al. 1947: 131). Thus, the clock is essential to 

the proper working of the computer, and in this section I will argue that it is key to 

understanding the functioning of the computer as well. 

The system clock lacks most mechanisms associated with clocks: it has neither gears nor 

hands, neither a face nor an audible signal to mark the minutes. Many advanced computer 

science books refer initially to a clock but later drop the metaphor and describe the 

oscillator, the tiny crystal whose vibrations regulate the flow of electricity through the 

motherboard. The etymology of "clock" relates our modern machines to medieval bells, 

in particular to bells that were struck to mark the hours (OED 1989). Samuel Macey 

extends this relation, figuring the clock as a sort of automaton: "the earliest mechanical 

clocks may be considered automated versions of the keeper of the bell or Glocke, which 

gave us the term clock in the first place" (Macey 1989: 1 1). Gerhard Dohrn-van Rossum,

historian and author of History of the Hour (1996), traces the history of man's relation to 

time-keeping devices, focusing less on the technology underlying the machines and more 

on man's evolving relation to the device. I draw on Dohrn-van Rossum's account, 

foregrounding his argument that time-keeping devices have functioned to synchronize, 

regulate, and abstract time. 

Well before mechanical clocks were sufficiently precise to reliably mark the hours, bells 

divided the day and synchronized the activities of a populace. Mumford points to the 

monasteries as the origin of modern notions of time-keeping, arguing that the 

Benedictines "helped to give human enterprise the regular collective beat and rhythm of 

the machine; for the clock is not merely a means of keeping track of the hours, but of 

synchronizing the actions of men" (1934: 14). Church bells also marked the hours outside 

the monastery, alerting laypersons to the hours for prayers and for mass, and by the 

fifteenth century many municipalities had acquired communal bells to signal secular 

meetings and events. Through variations in tone, duration, and number of rings, churches 

and municipalities developed elaborate codes to signal everything from mass to changing 

of the guards, from noontime break to opening of the courts. In both contexts – religious 

and secular – these bells announced hours that were irregular according to modern 

standards but nevertheless succeeded in improving the frequency with which monks and 

citizens were "on time" for various functions. In both contexts, the bells were functional 

well before devices for keeping time really worked. 

After bells, civic officials developed several time-keeping technologies to regulate 

everything from the marketplace to the postal system. Beginning in the thirteenth century, 

officials regulated the market with clocks, setting times at which particular merchants 

could do business and hours during which locals could shop. Previous to the fifteenth 

century, universities varied the time and duration of lectures according to content. By the 

fifteenth century, particularly in secondary schools, days were divided into discrete 

blocks during which specific subjects were taught, and these hours were measured by 

bells and sandglasses. In addition to measuring and dividing the day, timekeeping devices 

increasingly aided the rationalizing of space. Between the fifteenth and eighteenth 

centuries, postal and courier services increasingly abstracted distance by measuring it in 

units of time. Delivery times once measured in days were soon measured in hours, and 

soon promptness became the measure of quality: the normative codes "should thus be 

understood as concepts for systematically organized transportation and communication 

links, which, if we trace them over a longer period, reveal the growing importance of the 

techniques of time measurement and time control" (Dohrn-van Rossum 1996: 343). Even 

before railroads, the technology most associated with the strict enforcement of 

standardized time, the markets, the schools, and the postal systems were regulated by 

increasingly abstract notions of time (and space) (Dohrn-van Rossum 1996). 

It was not until the mid-seventeenth century that these functional time-keeping devices 

developed into working clocks. In 1656, Christiaan Huygens patented a pendulum clock 

that was accurate to within one minute a day, and by the late seventeenth century he had 

developed a balance wheel and spring assembly that could operate a pocket watch with 

an accuracy of 10 minutes per day. A substantial improvement over previous time-

keeping mechanisms, the pendulum was subject to inaccuracies due to changes in 

temperature, air pressure, and gravity. Some 200 years later, quartz crystals replaced 

pendulums in the 1920s, further enhancing the accuracy of clocks. Using an electric 

circuit to cause a crystal to vibrate, quartz clocks shed the gears and escarpments – the 

tangible mechanisms – that often led to variations in time. Just thirty some years after the 

quartz clock, an atomic clock that was accurate to one second in 2,000 years appeared in 

1955. The original definition of "second" was 1/86,400 of a mean solar day; in 1967 the 

natural frequency of the cesium atom – 9,192,631,770 oscillations of the atom's resonant 

frequency – was declared the international unit of time. Thus, by the late twentieth 

century measures of time had been abstracted to the point that they seemed to have little 

relation to the daily rhythms – solar or social – of man. 

Modern system clocks utilize quartz crystals, the same technology that runs many digital 

watches, and von Neumann first suggested using these oscillators in his 1945 Report. 

Following a brief overview of his entire architecture, von Neumann began his section 

titled "Elements, Synchronism Neuron Analogy" with a description of the control unit 

(1945: 23). He argued that all digital computing devices must have control "elements" 

with discrete equilibrium and that a synchronous "element" – a system clock – is the 

preferable control mechanism. Going to the trouble to define "element" as a synchronous 

device with discrete equilibrium, he proceeded to a biological analogy: "It is worth 

mentioning, that the neurons of the higher animals are definitely elements in the above 

sense. They have all-or-none character, that is two states: Quiescent and excited" (1945: 

24). Drawing on the work of MacCulloch and Pitts, von Neumann acknowledged that 

neurons were probably asynchronous but proposed that their behavior be simulated using 

synchronous systems (ibid.). Thus, von Neumann chose to implement a system clock 

because he was attempting to mimic the functioning of the human brain. Unable to build 

a machine that worked like a brain, he imagined constructing a computer that (apparently) 

functioned like a mind: "Von Neumann, especially, began to realize that what they were 

talking about was a general-purpose machine, one that was by its nature particularly well 

suited to function as an extension of the human mind" (Rheingold 2000: 86). Essential to 

this machine was a clock, a metronome that made regular, synchronous, and rational this 

model of the mind. Attempting to extend the capacities of the human mind, von 

Neumann's design actually recalled technologies previously used to control it. 

Paradoxically, von Neumann's architecture is at once flexible and rigid, is both an 

extension of and a constraint on our conceptual, cultural, and practical understanding of 

our minds. 

QWERTY keyboard 

In this section we will investigate the QWERTY keyboard, using a distinction between 

"machine" and "tool" to frame our discussion. Karl Marx in Capital (1867) and later 

Lewis Mumford in Technics and Civilization (1934) define these terms in reference to the 

history of labor and production. Without referring directly to Marx or Mumford, J. C. R. 

Licklider and Douglas Engelbart, early designers of computer interface and input devices, 

later defined a plan for man-computer symbiosis that echoes this tradition. Marx writes 

that the origin or impetus of movement is the essential difference between a tool and a

machine: with tools, movement originates in the laborer; with machines, movement 

issues from the mechanism itself (1867: 409). Working in an environment with machines 

requires education, that the body be trained to move along with the uniform and relentless 

motion of the mechanism (1867: 408). Conversely, the tool allows the laborer freedom of 

movement, an opportunity for motion independent of the mechanism. Working from 

Marx, Mumford associates tools with flexibility and machines with specialization. The 

essential difference, according to Mumford, is the degree of independence of operation. 

To summarize, a man works with a tool as an extension of his own body (and perhaps his 

own mind); in contrast, a machine employs a man as an extension of its own mechanism. 

Licklider (1960) essentially fears that computers are becoming machines rather than 

tools. Describing computers built previous to 1960 as "semi-automatic systems", he 

laments the trend toward automation at the expense of augmentation. He sees man 

incorporated into the system rather than being extended by it: "In some instances, 

particularly in large computer-centered information and control systems, the human 

operators are responsible mainly for functions that it proved infeasible to automate" 

(1960: 60). The human appears as a compromise required by the current state of 

technology, as a part that will be replaced once the memory is faster or the arithmetic unit 

more efficient. However, computers might act as extensions of humans, as devices for the 

augmentation of the intellect, as components in human-machine "symbiotic" systems. His 

proposed symbiosis is peculiar because he wants to introduce intuition and "trial-anderror 

procedures" into the system; as later expressed by Douglas Engelbart (1963), "we 

refer to a way of life in an integrated domain where hunches, cut-and-try, intangibles, and 

the human 'feel for a situation' usefully coexist with powerful concepts, streamlined 

terminology and notation, sophisticated methods, and high-powered electronic aids" 

(1963: 72). Licklider and Engelbart differ from von Neumann in that they do not want the 

computer to imitate the intellect but rather to enhance it. For both Licklider and 

Engelbart, organization is a component of intelligence, a function that could perhaps be 

better handled by a computer. But the essence of intelligence is a sort of synergy, a 

cooperative interplay between the human capabilities of organization and intuition, 

analysis and synthesis. While the language of progress and evolution employed by 

Licklider and Engelbart may be troubling, their visions of computers as extensions rather 

than imitations of man, as tools rather than machines, was revolutionary and remains 

relevant today. 

The computer keyboard exemplifies this tug-of-war between machine and tool. Most 

know that the modern computer keyboard continues the tradition of the typewriter and its 

less-than-intuitive arrangement of keys. Often referred to as the "QWERTY" keyboard, 

this arrangement of keys was devised by Christopher Sholes, and the first Remington 

typewriters sold in 1873 implemented his design. For decades after the first Remington, 

manufacturers experimented with alternative arrangements, including a circular layout 

and an arrangement that featured keys in alphabetical order. Probably the most famous 

redesign was the Dvorak Simplified Keyboard, patented in 1936. August Dvorak claimed 

that his keyboard led to increased speed, reduced fatigue, and more rapid learning. There 

is much dispute about the data used to support these claims (Liebowitz and Margolis 

1990), and more recent experimental evidence suggests that the Dvorak keyboard leads to

only a 10 percent improvement in speed (Norman 1990: 147). Yet the QWERTY 

keyboard remains a standard even though there exists an empirically superior option. Don 

Norman argues that such a small improvement does not warrant the institutional costs of 

conversion: "millions of people would have to learn a new style of typing. Millions of 

typewriters would have to be changed. The severe constraints of existing practice prevent 

change, even where the change would be an improvement" (1990: 150). Others add to 

this interpretation, citing the Dvorak keyboard as an example of the failure of the market 

economy (David 1985). Rather than entering this debate, I suggest that we accept parts of 

each argument – the Dvorak keyboard is not a marvelous improvement over the 

QWERTY arrangement, and the institutional cost of conversion is great – and then 

investigate the process through which QWERTY gained form and acceptance. 

While the QWERTY arrangement might not be the ideal arrangement of keys 

independent of mechanism used to print those keys, the arrangement was the most 

functional of Sholes's designs. The design problem faced by Sholes was that of 

overlapping rods: if a typist struck in sequence two adjacent keys, their rods would 

intersect and jam. Then the operator would have to stop typing and dislodge the 

mechanism, losing valuable time. Sholes's keyboard minimized the likelihood of 

jamming by separating keys for letters that frequently appear adjacent to one another in 

common words. The arrangement aims to achieve maximum speed through maximum 

synchronization of operator and machine. It is often stated that Sholes designed the 

keyboard to slow down operators of the typewriter; in fact, Sholes designed the keyboard 

so as to better coordinate the actions of the machine and its operator, not just to slow the 

operator but to develop a sequence and a rhythm that was manageable by both human and 

machine. Bliven (1954) comments on the sound of a room full of typists, on the particular 

rhythm distinct to adept typists. Sholes's design of the machine was the engineering of 

this rhythm, a reorganization of the interface of the typewriter to facilitate the most 

efficient use of the mechanism. 

Today we assume that good typists use all fingers, but this technique was not common 

until fifteen years after the typewriter went to market. In fact, there was much debate 

surrounding the feasibility of teaching this skill. Early typists used the "hunt and peck" 

method with remarkable success. In 1882, Mrs L. V. Longley issued a pamphlet 

proposing a method of typing that used all ten fingers. A typing instructor in Cincinnati, 

Mrs Longley met with great opposition in the trade press. Even five years after the 

publication of her pamphlet, trade magazines carried stories arguing the impracticality of 

her proposal, citing a lack of strength and dexterity in some fingers. Five years later, 

Frank McGurrin, a court stenographer from St Louis, again proposed a ten-finger method 

of typing. He added to his technique the skill of "touch typing", the ability to type without 

looking at the keyboard. He also met with resistance but silenced his adversaries when, in 

1888, he won a typing contest using his new method. McGurrin used a Remington 

typewriter, and his victory led to the adoption of two standards: the QWERTY keyboard 

and the touch-typing method (Norman 1990: 147). Following McGurrin's victory, the 

same publication that berated Mrs Longley endorsed touch-typing. It was accepted that 

typewriters might be more than twice as fast as writing by hand, and typists were 

retrained to use all fingers.

In the early twentieth century, typewriter companies employed championship typists to 

compete in national competitions. These racers were given a racing typewriter and a 

salary for practicing their skills eight hours a day. Racing typists netted 120 error-free 

words per minute in front of crowds of businessmen at the various trade shows in 

Madison Square Garden. Once most speed typists mastered the art of touch-typing, other 

inefficiencies in the process were targeted. Charles Smith, the coach of the famous 

Underwood Speed Training Group, developed the "right-hand carriage draw", a new 

method of exchanging paper that saved as much as a full second. This method required 

the typist to grab the roller knob and the filled sheet with one hand while grabbing a fresh 

sheet from a prepared stack of papers and inserting it without ever looking away from the 

copy. All of this in one-third of a second (Bliven 1954: 125)! Techniques introduced in 

the racing circuit gradually infiltrated the business world, producing typists with elegant 

and precise routines to synchronize their movements with the machine. Rhythm was key: 

"Above all, the mark of good typing is a continuous, even rhythm. An expert keeps 

going" (Bliven 1954: 143). Thus the touch-typing system prescribed routines for striking 

the keys and for manipulating the impacted media, thereby rendering the typists 

extensions of their typewriters, parts of their machines. 

In the late nineteenth century, business required improvements in technologies of 

communication and record-keeping, and commercial culture embraced the typewriter and 

its promise of increased speed and accuracy of transcription. But other cultures of use 

emerged as well. George Flanagan writes: "the idea of Art in typewriting has been sadly 

ignored" (1938: vi). Calling the machine "mechanically perfect" and typists "marvelous[ly] 

skilled", he writes a treatise in hopes of diverting attention from efficiency 

towards aesthetics. Flanagan recommends harmony and balance through the judicious 

arrangement of space and compactness (1938: 2). Utility should not be achieved at the 

expense of elegance; speed can be sacrificed for ornament. Inserting typewriting into the 

tradition of printing, Flanagan advises the typists to plan the layout of the page just as one 

plans the content of the paragraphs. He provides sample layouts – spacing, alignment, 

sizes for paragraphs – and includes formulas – combinations and sequences of keystrokes 

– for generating ornamental drop caps and borders. Although he recognizes the pressures 

of an office and the primacy of speed, Flanagan argues that typists should not ignore 

design. They might even profit from it: "Your work will become an endless romance, and 

we even suspect that your pay envelopes will grow fatter" (1938: vii). Seeing the typist as 

a craftsman rather than a laborer, Flanagan reshapes the machine as a tool. Although 

entertaining, the point isn't the aesthetics of ASCII art; rather, it is the revision of what 

had become a mechanistic relationship between a human and a machine. The idea of 

typing contests and of ornamental typing might seem equally trite today, but their 

historical value lies in the relationships they posit between typist and typewriter, between 

operator and tool or machine. 

Although most computer keyboards follow Sholes's design, Engelbart experimented with 

many different input devices that drew on various traditions of symbol manipulation. 

Most interesting in this context is the chord keyset, a group of five piano-like keys that 

were operated by one hand. The keys were not printed with any characters, and operators 

used single keys and combinations of keys to produce up to 32 different codes, each of

which represented a different character. Drawing on technologies developed by 

telegraphers, Engelbart expected that computer users would be trained in this code and 

would eventually surpass the speeds of typists using QWERTY keyboards. The chord 

keyset would also allow users to manipulate a keyset and a mouse at the same time 

without ever looking at their hands. Engelbart imagined a reincarnation of the Remington 

racer who could change paper without ever losing her place in the copy. His input devices 

would become extensions of the hands, and users would manipulate them without the aid 

of the other senses. Yet such facility would require learning another language, a new 

code, an additional symbol set that in its abstraction matched closely the binary set used 

by the computer itself. The boundary between man and machine seemed permeable, but 

man seemed to be the one crossing the line, the one adopting the characteristics of the 

other. 

Engelbart argued in general terms that users would need to be trained to use computers, 

and the retraining necessary to use the chord keyset was just one step in that process. Of 

course, the typist's keyboard was eventually chosen over the telegrapher's, largely due to 

the anticipated institutional costs of retraining, and Engelbart's demo now seems rather 

quaint. But the choice to adopt the QWERTY keyboard demonstrates a few interesting 

points. First, we no longer recognize the extent to which typing – even the hunt-and-peck 

method – requires training; the typist's keyboard seems somehow natural. Second, while 

we resist new technologies that require abstract, mechanistic motion, we willfully 

embrace old ones that require equally abstract and mechanistic routines. If the QWERTY 

keyset casts the computer as machine, perhaps it's difficult to argue that the chord keyset 

does otherwise. The latter requires complicated and coordinated movements and an 

internalized understanding of an abstract code. Yet Engelbart, seeking augmentation 

rather than automation, argued that this degree of training is necessary before the 

computer may function as a tool. Engelbart's notions of training, and the boundaries 

between the training of man and the training of the computer, deserve further exploration. 

Returning to Marx and Mumford, we must work to cast the computer as tool rather than 

as machine. 

Conclusion 

Despite the apparent contradictions in Engelbart's efforts to build input devices that 

would augment the human intellect, he and Licklider provide inspiration for, if not 

demonstration of, what the computer might someday be. Engelbart in his 1961 report 

"Program on Human Effectiveness" describes his plan for the augmentation of the 

individual problem solver. His objective is to "develop means for making humans 

maximally effective as comprehending solvers of problems", and he plans to do this by 

inserting computers into various levels of our hierarchy of problem-solving tools. Here 

again he returns to issues of training, proposing that humans be conditioned both to use 

his input devices and to understand the logical operations that make the computer work. 

Engelbart wants to build tools even if he must first build machines; he wants humans to 

train computers, but for now he must settle for computers that can train humans. Today, 

children use computers from such a young age that manipulating a mouse seems

"intuitive" and programming languages seem "natural." Half of Engelbart's program has 

been accomplished: we have been trained to use the computer. 

Even in 1960 Licklider was troubled by the extent to which scientists had been trained to 

use the computer. Disappointed that the computer was being used primarily to solve 

"preformulated problems" with "predetermined procedures" – "It is often said that 

programming for a computing machine forces one to think clearly, that it disciplines the 

thought process. If the user can think his problem through in advance, symbiotic 

association with a computing machine is not necessary" (1960: 61) – Licklider proposed 

that computers be employed to ask questions, not just to provide answers. Given the 

limits to technology, the first attempts at symbiotic systems will rely on the human 

operators to set the goals, formulate hypotheses, devise models, and so on (1960: 64). 

The operators will evaluate the output, judge the contribution of the machine, and fill in 

the gaps when the machine lacks the proper routine for handling some problem. Although 

Licklider imagines much more, his first iteration of the symbiotic system is an advanced 

information-handling machine. Consistent with his colleague, Engelbart also framed the 

problem of symbiosis as one of information management: a primary objective for his H- 

LAM/T project was "to find the factors that limit the effectiveness of the individual's 

basic information-handling capabilities in meeting the various needs of society for 

problem solving in the most general sense" (1963: 73). In practice, the symbiotic machine 

became a problem-solving rather than a problem-posing device. For the most part, that is 

how the computer continues to function. Licklider's dream remains largely unfulfilled. 

Perhaps transforming the computer from machine to tool, from a device that automates 

mundane mental tasks to one that augments critical and creative thought, is the task now 

facing computing humanists. 

See also chapter 29: Speculative Computing. 


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14. 

Classification and its Structures 

C. M. Sperberg-McQueen 

Classification is, strictly speaking, the assignment of some thing to a class; more 

generally, it is the grouping together of objects into classes. A class, in turn, is a 

collection (formally, a set) of objects which share some property. 

For example, a historian preparing an analysis of demographic data transcribed from 

census books, parish records, and city directories might classify individuals by sex, age, 

occupation, and place of birth. Places of birth might in turn be classified as large or small 

cities, towns, villages, or rural parishes. A linguist studying a text might classify each 

running word of text according to its part of speech, or each sentence according to its 

structure. Linguists, literary scholars, or social scientists might classify words occurring 

in a text by semantic category, organizing them into semantic nets. Classification serves 

two purposes, each important: by grouping together objects which share properties, it 

brings like objects together into a class; by separating objects with unlike properties into

separate classes, it distinguishes between things which are different in ways relevant to 

the purpose of the classification. The classification scheme itself, by identifying 

properties relevant for such judgments of similarity and dissimilarity, can make explicit a 

particular view concerning the nature of the objects being classified. 

Scope 

Since a classification may be based on any set of properties that can be attributed to the 

objects being classified, classification in the broad sense involves the correct 

identification of the properties of the objects of study and is hardly distinguishable from 

coherent discourse in general. (Like coherence, systematic classification is sometimes 

eschewed for aesthetic or ideological reasons and if overrated may descend into 

pedantry.) Information retrieval systems may be regarded, and are often described, as 

classifying records into the classes "relevant" and "not relevant" each time a user issues a 

query. Norms and standards like the XML 1.0 specification or Unicode may be 

understood as classification schemes which assign any data stream or program either to 

the class "conforming" or to the class "non-conforming." Laws may be interpreted as 

classifying acts as legal or illegal, censors as classifying books, records, performances, 

and so on. Any characteristic of any kind of thing, using any set of concepts, may be 

viewed as classifying things of that kind into classes corresponding to those concepts. In 

the extreme case, the property associated with a class may be vacuous: the members may 

share only the property of membership in the class. In general, classification schemes are 

felt more useful if the classes are organized around properties relevant to the purpose of 

the classification. Details of the concepts, categories, and mechanisms used in various 

acts of classification may be found in other chapters in this volume: see, for example, 

chapters 1, 7, 15, and 17. 

In the narrower sense, for computer applications in the humanities classification most 

often involves either the application of pre-existing classification schemes to, or the post 

hoc identification of clusters among a sample of, for example, texts (e.g. to describe the 

samples in language corpora), parts of texts (e.g., to mark up the structural constituents or 

non-structural features of the text), bibliography entries (for subject description in 

enumerative bibliographies or specialized libraries), words (for semantic characterization 

of texts), or extra-textual events or individuals (e.g., for historical work). The best known 

of these among the readers of this work are perhaps the classification systems used in 

libraries and bibliographies for classifying books and articles by subject; in what follows, 

examples drawn from these will be used where possible to illustrate important points, but 

the points are by no means relevant only to subject classification. 

Since classification relies on identifying properties of the object being classified, perfect 

classification would require, and a perfect classification scheme would exhibit, perfect 

knowledge of the object. Because a perfect subject classification, for example, locates 

each topic in a field in an n-dimensional space near other related topics and distant from 

unrelated topics, a perfect subject classification represents a perfect map of the 

intellectual terrain covered in the area being classified. For this reason, classification 

schemes can carry a great deal of purely theoretical interest, in addition to their practical

utility. Classification schemes necessarily involve some theory of the objects being 

classified, if only in asserting that the objects possess certain properties. Every ontology 

can be interpreted as providing the basis for a classification of the entities it describes. 

And conversely, every classification scheme can be interpreted with more or less ease, as 

the expression of a particular ontology. In practice, most classification schemes intended 

for general use content themselves with representing something less than a perfect image 

of the intellectual structure of their subject area and attempt with varying success to limit 

their theoretical assumptions to those most expected users can be expected to assent to. 

At the extreme, the assumptions underlying a classification scheme may become 

effectively invisible and thus no longer subject to challenge or rethinking; for purposes of 

scholarly work, such invisibility is dangerous and should be avoided. 

This chapter first describes the abstract structures most often used in classification, and 

describes the rules most often thought to encourage useful classification schemes. It then 

gives a purely formal account of classification in terms of set theory, in order to establish 

that no single classification scheme can be exhaustive, and indeed that there are infinitely 

more ways of classifying objects than can be described in any language. Finally, it turns 

to various practical questions involved in the development and use of classification 

systems. 

One-dimensional Classifications 

Very simple classification schemes (sometimes referred to as nominal classifications, 

because the class labels used are typically nouns or adjectives) consist simply of a set of 

categories: male and female; French, German, English, and other; noun, verb, article, 

adjective, adverb, etc. In cases like these, some characteristic of the object classified may 

take any one of a number of discrete values; formally, the property associated with the 

class is that of having some one particular value for the given characteristic. The different 

classes in the scheme are not ordered with respect to each other; they are merely discrete 

classes which, taken together, subdivide the set of things being classified. 

In some classifications (sometimes termed ordinal), the classes used fall into some sort of 

sequencing or ordering with respect to each other: first-year, second-year, third-year 

student; folio, quarto, octavo, duodecimo; upper-class, middle-class, lower-class. 

In still other cases, the underlying characteristic may take a large or even infinite number 

of values, which have definite quantitative relations to each other: age, height, number of 

seats in parliament, number of pages, price, etc. For analytic purposes, it may be 

convenient or necessary to clump (or aggregate) sets of distinct values into single classes, 

as when age given in years is reduced to the categories infant, child, adult, or to under-18, 

18–25, 25–35, over-35. 

All of the cases described so far classify objects based on the value of a single 

characteristic attributed to the object. In the ideal case, the characteristic can be readily 

and reliably evaluated, and the values it can take are discrete. The more borderline cases 

there are, the harder it is likely to be to apply the classification scheme, and the more

information is likely to be lost by analyses which rely on the classified data rather than 

the original data. 

Classification Schemes as N-dimensional Spaces 

In less simple classification schemes, multiple characteristics may be appealed to. These 

may often be described as involving a hierarchy of increasingly fine distinctions. The 

Dewey Decimal Classification, for example, assigns class numbers in the 800s to literary 

works. Within the 800s, it assigns numbers in the 820s to English literature, in the 830s to 

German literature, the 840s to French, etc. Within the 820s, the number 821 denotes 

English poetry, 822 English drama, 823 English fiction, and so on. Further digits after the 

third make even finer distinctions; as a whole, then, the classification scheme may be 

regarded as presenting the classifier and the user with a tree-like hierarchy of classes and 

subclasses, with smaller classes branching off from larger ones. 

In the case of the Dewey classification of literature, however, the second and third digits 

are (almost) wholly independent of each other: a third digit 3 denotes fiction whether the 

second digit is 1 (American), 2 (English), 3 (German), 4 (French), 5 (Italian), 6 (Spanish), 

7 (Latin), or 8 (Classical Greek), and 2 as a third digit similarly denotes drama, 

independent of language. 

We can imagine the literature classification of the Dewey system as describing a plane, 

with the second digit of the Dewey number denoting positions on the x axis, and the third 

digit denoting values along the y axis. Note that neither the sequence and values of the 

genre numbers, nor those of the language numbers, have any quantitative significance, 

although the sequence of values is in fact carefully chosen. 

Generalizing this idea, classification schemes are often regarded as identifying locations 

in an n-dimensional space. Each dimension is associated with an axis, and the set of 

possible values along any one axis is sometimes referred to as an array. Many salient 

characteristics of classification schemes may be described in terms of this n-dimensional 

spatial model. 

It should be noted that, unlike the dimensions of a Cartesian space, the different 

characteristics appealed to in a classification scheme are not always wholly independent 

of each other. A medical classification, for example, may well subdivide illnesses or 

treatments both by the organ or biological system involved and by the age, sex, or other 

salient properties of the patient. Since some illnesses afflict only certain age groups or 

one sex or the other, the two axes are not wholly independent. A classification of dialects 

based on the pronunciation of a given lexical item can only apply to dialects in which that 

lexical item exists. A facet in a social classification that distinguishes hereditary from 

non-hereditary titles is only relevant to that part of the population which bears titles, and 

only in countries with a nobility. The digits 2 for drama and 3 for fiction have these 

meanings in the Dewey classification for literature, but they are not applicable to the 900s 

(history) or the 100s (philosophy). And so on.

The idea of a classification as describing an n-dimensional Cartesian space is thus in 

many cases a dramatic simplification. It is nonetheless convenient to describe each 

characteristic or property appealed to in a classification as determining a position along 

an axis, even if that axis has no meaning for many classes in the scheme. Those offended 

by this inexactitude in the metaphor may amuse themselves by thinking of the logical 

space defined by such a classification not as a Cartesian or Newtonian one but as a 

relativistic space with a non-Euclidean geometry. 

Some Distinctions among Classification Schemes 

When the axes of the logical space are explicitly identified in the description of the 

classification scheme, the scheme is commonly referred to as a faceted classification, and 

each axis (or the representation of a given class's value along a specific axis) as a facet. 

The concept of facets in classification schemes was first systematized by Ranganathan, 

though the basic phenomena are visible in earlier systems, as the example from the 

Dewey classification given above illustrates. 

Faceted schemes are typically contrasted with enumerative schemes, in which all classes 

in the system are exhaustively enumerated in the classification handbook or schedule. In 

a typical faceted scheme, a separate schedule is provided for each facet and the facets are 

combined by the classifier according to specified rules; because the classifier must create 

or synthesize the class number, rather than looking it up in an enumeration, faceted 

schemes are sometimes also called synthetic (or, to emphasize that the task of synthesis 

must be preceded by analysis of the relevant properties of the object, analytico-synthetic) 

schemes. Both because of their intellectual clarity and because they can readily exploit 

the strengths of electronic database management systems, faceted classification schemes 

have become increasingly popular in recent years. 

Some classification schemes provide single expressions denoting regions of the logical 

space; in what follows these are referred to as (class) formulas. Formulas are convenient 

when the objects classified must be listed in a single one-dimensional list, as on the 

shelves of a library or the pages of a classified bibliography. In such schemes, the order 

in which axes are represented may take on great importance, and a great deal of ingenuity 

can be devoted to deciding whether a classification scheme ought to arrange items first by 

language, then by genre, and finally by period, or in some other order. 

In computerized systems, however, particularly those using database management 

systems, it is normally easier to vary the order of axes and often unnecessary to list every 

object in the collection in a single sequence, and so the order of axes has tended to 

become somewhat less important in multidimensional classification schemes intended for 

computer use. The provision of unique class formulas for each point in the scheme's 

logical space has correspondingly declined in importance, and much of the discussion of 

notation in pre-electronic literature on classification has taken on an increasingly quaint 

air. For those who need to devise compact symbolic formulas for the classes of a scheme, 

the discussions of notation in Ranganathan's Prolegomena (1967) are strongly 

recommended.

When each axis of the logical space can be associated with a particular part of the 

formula denoting a class, and vice versa, the notation is expressive (as in the portion of 

the Dewey system mentioned above). Fully expressive notations tend to be longer than 

would otherwise be necessary, so some classification schemes intentionally use 

inexpressive or incompletely expressive notation, as in most parts of the Library of 

Congress classification system. Expressive notations are advantageous in computer-based 

applications, since they make it easy to perform searches in the logical space by means of 

searches against class symbols. A search for dramas in any language, for example, can be 

performed by searching for items with a Dewey class number matching the regular 

expression "8.2." No similarly simple search is possible in inexpressive notations. 

Some classification systems describe classes using natural-language phrases, rather than 

by assigning them to specific locations in a class hierarchy; library subject headings are a 

well-known example, but there are many others. (Some classification theorists distinguish 

such alphabetical systems as indexing systems, as opposed to classification systems in the 

strict sense, restricting the latter term to systems that provide a formal notation other than 

natural language for their class formulas.) Typically, such systems arrange topics in 

alphabetical order, rather than a systematic order imposed by the structure of the 

classification scheme. At one extreme, such a system may use free-form textual 

descriptions of objects to "classify" them. Most alphabetically organized classification 

systems, however, differ from wholly free-form indices in one or more ways. First, in 

order to avoid or minimize the inconsistencies caused by the use of different but 

synonymous descriptions, such systems normally use controlled vocabularies rather than 

unconstrained natural-language prose: descriptors other than proper nouns must be 

chosen from a closed list. In the ideal case, the controlled vocabulary has exactly one 

representative from any set of synonyms in the scope of the classification scheme. 

Second, as part of the vocabulary control alphabetic systems often stipulate that certain 

kinds of phrases should be "inverted", so that the alphabetical listing will place them near 

other entries. In some schemes, particular types of descriptors may be subdivided by 

other descriptors in a hierarchical fashion. Thus the Library of Congress subject heading 

for Beowulf will be followed by "Beowulf - Adaptations", "Beowulf - Bibliography", 

"Beowulf - Criticism, textual", "Beowulf - Study and teaching", "Beowulf - Translations – 

Bibliographies", "Beowulf- Translations – History and criticism", and so on. The phrases 

after the dashes are, in effect, an array of possible subdivisions for anonymous literary 

works; the Library of Congress Subject Headings (LCSH) provide a prescribed set of 

such expansions for a variety of different kinds of object: anonymous literary works, 

individuals of various kinds, theological topics, legislative bodies, sports, industries, 

chemicals, and so on. Third, most systems which use controlled vocabularies also provide 

a more or less systematic set of cross-references among terms. At a minimum, these 

cross-references will include see references from unused terms to preferred synonyms. In 

more elaborate cases, see-also references will be provided to broader terms, narrower 

terms, coordinate terms (i.e., other terms with the same broader term), partial synonyms, 

genus/species terms, and so on. The links to broader and narrower terms allow the 

alphabetically arranged scheme to provide at least some of the same information as a 

strictly hierarchical scheme. Like the LCSH, the New York Times Thesaurus of 

Descriptors (1983) described by Mills provides a useful model for work of this kind.

The fineness of distinction carried by the classification – that is, the size of the regions in 

the logical space that the classification allows us to distinguish – is called (mixing 

metaphors) the depth of the classification scheme. Some classification schemes provide a 

fixed and unvarying depth; others allow variable depth. Depth may be added either by 

adding more axes to the classification, as when a library using the Dewey system 

subdivides 822 (English drama) by period, or by adding more detail to the specification 

of the value along an axis already present. Faceted classification schemes often allow 

facets to vary in length, so as to allow the depth of classification to be increased by 

providing a more precise value for any facet. Notations with fixed-length facets, by 

contrast, like the part of Dewey described above, cannot increase the specificity of facets 

other than the last without creating ambiguity. 

Whether they use expressive notation or not, some classification schemes provide 

notations for each node in their hierarchy (e.g., one formula for "literature" and another 

for "English literature", and so on); in such cases, the categories of the classification are 

not, strictly speaking, disjoint: the broader classes necessarily subsume the narrower 

classes arranged below them. One advantage of expressive notation is that it makes this 

relationship explicit. Other schemes provide notations only for the most fully specified 

nodes of the hierarchy: the hierarchical arrangement may be made explicit in the 

description of the scheme, but is collapsed in the definition of the notation, so that the 

classification gives the impression of providing only a single array of values. Commonly 

used part-of-speech classification systems often collapse their hierarchies in this way: 

each tag used to denote word-class and morphological information denotes a complete 

packet of such information; there is no notation for referring to more general classes like 

"noun, without regard for its specific morphology." Markup languages similarly often 

provide names only for the "leaves" of their tree-like hierarchies of element types; even 

when a hierarchy of classes is an explicit part of the design, as in the Text Encoding 

Initiative (TEI), there may be no element types which correspond directly to classes in 

the hierarchy. 

When combinations of terms from different axes are specified in advance, as part of the 

process of classifying or indexing an object, we speak of a pre-coordinate system. When 

a classification system limits itself to identifying the appropriate values along the various 

axes, and values may be combined at will during a search of the classification scheme, 

we speak of a post-coordinate system. Printed indices that list all the subject descriptors 

applied to the items in a bibliography, in a fixed order of axes, for example, present a 

kind of pre-coordinate classification scheme. Online indices that allow searches to be 

conducted along arbitrary combinations of axes, by contrast, provide a post-coordinate 

scheme. It is possible for printed indices to provide free combination of terms, but postcoordinate 

indexing is easier for computer systems. Post-coordinate indexing allows 

greater flexibility and places greater demands on the intelligence of the user of the index. 

When the axes and the values along each axis are specified in advance, and items are 

classified in terms of them, we can speak of an a priori system. When the axes and their 

values are derived post hoc from the items encountered in the collection of objects being 

classified, we may speak of an a posteriori or data-driven system. Author-specified

keywords and free-text searching are simple examples of data-driven classification. 

Citation analysis, and in particular the study of co-citation patterns in scholarly literature, 

as described by Garfield, is another. 

In some cases, the identification of axes in a data-driven system may involve 

sophisticated and expensive statistical analysis of data. The technique of latent semantic 

analysis is an example: initially, the occurrence or non-occurrence of each word in the 

vocabulary of all the documents in the collection being indexed is treated as an axis, and 

a statistical analysis is performed to collapse as many of these axes together as possible 

and identify a useful set of axes which are as nearly orthogonal to each other as the data 

allow. In a typical application, latent-semantic analysis will identify documents in a space 

of 200 or so dimensions. It is sometimes possible to examine the dimensions and 

associate meaning with them individually, but for the most part data-driven statistical 

methods do not attempt to interpret the different axes of their space individually. Instead, 

they rely on conventional measures of distance in n-dimensional spaces to identify items 

which are near each other; when the classification has been successful, items which are 

near each other are similar in ways useful for the application, and items which are distant 

from each other are dissimilar. 

A priori systems may also be interpreted as providing some measure of similarity among 

items, but it is seldom given a numerical value. 

Unconscious, naive, or pre-theoretic classification (as seen, for example, in naturallanguage 

terminology for colors) may be regarded as intermediate between the a priori 

and a posteriori types of classification systems described above. 

Some data-driven systems work by being given samples of pre-classified training 

material and inducing some scheme of properties which enables them to match, more or 

less well, the classifications given for the training material. Other data-driven systems 

work without overt supervision, inducing classifications based solely on the observed 

data. 

A priori systems require more effort in advance than data-driven systems, both in the 

definition of the classification scheme and in its application by skilled classifiers. The 

costs of data-driven systems are concentrated later in the history of the classification 

effort, and tend to involve less human effort and more strictly computational effort. Datadriven 

classification schemes may also appeal to scholars because they are free of many 

of the obvious opportunities for bias exhibited by a priori schemes and thus appear more 

nearly theory-neutral. It must be stressed, therefore, that while the theoretical 

assumptions of data-driven systems may be less obvious and less accessible to inspection 

by those without a deep knowledge of statistical techniques, they are nonetheless 

necessarily present. 

Rules for Classification

Some principles for constructing classification schemes have evolved over the centuries; 

they are not always followed, but are generally to be recommended as leading to more 

useful classification schemes. 

The first of these is to avoid cross-classification: a one-dimensional classification should 

normally depend on the value of a single characteristic of the object classified, should 

provide for discrete (non-overlapping) values, and should allow for all values which will 

be encountered: perhaps the best-known illustration of this rule lies in its violation in the 

fictional Chinese encyclopedia imagined by Jorge Luis Borges, in which 

it is written that animals are divided into: (a) those that belong to the Emperor, (b) 

embalmed ones, (c) those that are trained, (d) suckling pigs, (e) mermaids, (f) fabulous 

ones, (g) stray dogs, (h) those that are included in this classification, (i) those that tremble 

as if they were mad, (j) innumerable ones, (k) those drawn with a very fine camel's-hair 

brush, (1) others, (m) those that have just broken a flower vase, (n) those that resemble 

flies from a distance. 

(Borges 1981) 

One apparent exception to this rule is often found in schemes which seek to minimize the 

length of their class formulas: often two characteristics are collapsed into a single step in 

the classification hierarchy, as when a demographic classification has the classes infant 

(sex unspecified), infant male, infant female, child (sex unspecified), boy, girl, adult (sex 

unspecified), man, woman. 

Other desirable attributes of a classification scheme may be summarized briefly (I 

abbreviate here the "canons" defined by Ranganathan). Each characteristic used as the 

basis for an axis in the logical space should: 

1 distinguish some objects from others: that is, it should give rise to at least two 

subclasses; 

2 be relevant to the purpose of the classification scheme (every classification scheme has 

a purpose; no scheme can be understood fully without reference to that purpose); 

3 be definite and ascertainable; this means that a classification scheme cannot be 

successfully designed or deployed without taking into account the conditions under 

which the work of classification is to be performed; 

4 be permanent, so as to avoid the need for constant reclassification; 

5 have an enumerable list of possible values which exhausts all possibilities. Provision 

should normally be made for cases where the value is not ascertainable after all: it is 

often wise to allow values like unknown or not specified. In many cases several distinct 

special values are needed; among those sometimes used are: unknown (but applicable), 

does-not-apply, any (data compatible with all possible values for the field), approximate

(estimated with some degree of imprecision), disputed, uncertain (classifier is not certain 

whether this axis is applicable; if it is applicable, the value is unknown). 

In classification schemes which provide explicit class symbols, it is useful to provide a 

consistent sequence of axes in the construction of the class symbol (if the subject 

classification for literature divides first by country or language and then by period, it is 

probably wise for the subject classification for history to divide first by country and then 

by period, rather than vice versa). The sequence of values within an array of values for a 

given axis should also be made helpful, and consistent in different applications. Patterns 

often suggested include arranging the sequence for increasing concreteness, increasing 

artificiality, increasing complexity, increasing quantity, chronological sequence, 

arrangement by spatial contiguity, arrangement from bottom up, left-to-right 

arrangement, clockwise sequence, arrangement following a traditional canonical 

sequence, arrangement by frequency of values (in bibliographic contexts this is called 

literary warrant), or as a last resort alphabetical sequence. 

Many classification schemes appeal, at some point, to one of a number of common 

characteristics in order to subdivide a class which otherwise threatens to become too 

large (in bibliographic practice, it is often advised to subdivide a class if it would 

otherwise contain more than twenty items). Subdivision by chronology, by geographic 

location, or by alphabetization are all commonly used; standard schedules for subdivision 

on chronological, geographic, linguistic, genre, and other grounds can be found in 

standard classification schemes and can usefully be studied, or adopted wholesale, in the 

creation of new schemes. 

Classification schemes intended for use by others do well to allow for variation in the 

depth of classification practiced. Library classification schemes often achieve this by 

allowing class numbers to be truncated (for coarser classification) or extended (for finer); 

markup languages may allow for variable depth of markup by making some markup 

optional and by providing element types of varying degrees of specificity. 

It is also desirable, in schemes intended for general use, to provide for semantic extension 

and the addition of new concepts; this is not always easy. Library classification schemes 

often attempt to achieve this by providing standard schedules for subdividing classes by 

chronology, geographic distribution, and so on, to be applied according to the judgment 

of the classifier; the Colon Classification goes further by defining an array of abstract 

semantic concepts which can be used when subdivision by other standard axes is not 

feasible or appropriate. It provides a good illustration of the difficulty of providing useful 

guidance in areas not foreseen by the devisers of the classification scheme: 

1 unity, God, world, first in evolution or time, one-dimension, line, solid state, … 

2 two dimensions, plane, cones, form, structure, anatomy, morphology, sources of 

knowledge, physiography, constitution, physical anthropology, …

3 three dimensions, space, cubics, analysis, function, physiology, syntax, method, social 

anthropology, … 

4 heat, pathology, disease, transport, interlinking, synthesis, hybrid, salt, … 

5 energy, light, radiation, organic, liquid, water, ocean, foreign land, alien, external, 

environment, ecology, public controlled plan, emotion, foliage, aesthetics, woman, sex, 

crime, … 

6 dimensions, subtle, mysticism, money, finance, abnormal, phylogeny, evolution, … 

7 personality, ontogeny, integrated, holism, value, public finance, … 

8 travel, organization, fitness. 

In markup languages, semantic extension can take the form of allowing class or type 

attributes on elements: for any element type e, an element instance labeled with a class or 

type attribute can be regarded as having a specialized meaning. In some markup 

languages, elements with extremely general semantics are provided (such as the TEI div, 

ab, or seg elements, or the HTML div and span elements), in order to allow the greatest 

possible flexibility for the use of the specialization attributes. 

Any new classification scheme, whether intended for general use or for use only by a 

single project, will benefit from clear documentation of its purpose and (as far as they can 

be made explicit) its assumptions. For each class in the scheme, the scope of the class 

should be clear; sometimes the scope is sufficiently clear from the name, but very often it 

is essential to provide scope notes describing rules for determining whether objects fall 

into the class or not. Experience is the best teacher here; some projects, like many large 

libraries, keep master copies of their classification schemes and add annotations or 

additional scope notes whenever a doubtful case arises and is resolved. 

A Formal View 

From a purely formal point of view, classification may be regarded as the partition of 

some set of objects (let us call this set0) into some set of classes (let us call this set of 

classes C, or the classification scheme). 

In simple cases (nominal classifications), the classes of C have no identified relation to 

each other but serve merely as bins into which the objects in 0 are sorted. For any finite 0, 

there are a finite number of possible partitions of 0 into non-empty pair-wise disjoint 

subsets of 0. As a consequence, there are at most a finite number of extensionally distinct 

ways to classify any finite set 0 into classes; after that number is reached, any new 

classification must reconstitute a grouping already made by some other classification and 

thus be extensionally equivalent to it. Such extensionally equivalent classifications need 

not be intensionally equivalent: if we classify the four letters a, b, l, e according to their 

phonological values, we might put a and e together as vowels, and b and l as consonants.

If we classed them according to whether their letter forms have ascenders or not, we 

would produce the same grouping; the two classifications are thus extensionally 

equivalent, though very different in intension. In practice, the extensional equivalence of 

two classifications may often suggest some relation among the properties appealed to, as 

when classifying the syllables of German according to their lexicality and according to 

their stress. 

In some cases, the classes of C can be related by a proximity measure of some kind. In 

such a classification, any two adjacent classes are more similar to each other than, say, a 

pair of non-adjacent classes. If such a classification scheme relies on a single scalar 

property, its classes may be imagined as corresponding to positions on, or regions of, a 

line. If the classification schema relies on two independent properties, the classes will 

correspond to points or regions in a plane. In practice, practical classification schemes 

often involve arbitrary numbers of independent properties; if n properties are used by a 

classification scheme, individual classes may be identified with positions in an ndimensional 

space. The rules of Cartesian geometry may then be applied to test similarity 

between classes; this is simplest if the axes are quantitative, or at least ordered, but 

suitably modified distance measures can be used for purely nominal (unordered, 

unquantitative) classifications as well: the distance along the axis may be 0, for example, 

if two items have the same value for that axis, and 1 otherwise. 

If we imagine some finite number of classes, and conceive of a classification scheme as 

being defined by some finite-length description (say, in English or any other natural 

language) of how to apply those classes to some infinite set of objects, then it may be 

noted that there are an infinite number of possible groupings which will not be generated 

by any classification scheme described in our list. The proof is as follows: 

1 Let us label the classes with the numbers 1 to n, where n is the number of classes. 

2 Let us assume that the objects to be classified can be placed in some definite order; the 

means by which we do this need not concern us here. 

3 Then let us place the descriptions of possible classifications also into a definite order; it 

is easy to see that the list of descriptions is likely to be infinite, but we can nevertheless 

place them into a definite order. Since we imagine the descriptions as being in English or 

some other natural language, we can imagine sorting them first by length and then 

alphabetically. In practice, there might be some difficulty deciding whether a given text 

in English does or does not count as a description of a classification scheme, but for 

purposes of this exercise, we need not concern ourselves with this problem: we can list all 

English texts, and indeed all sequences of letters, spaces, and punctuation, in a definite 

sequence. (If we cannot interpret the sequence of letters as defining a rule for assigning 

objects to classes, we can arbitrarily assign every object to class 1.) 

4 Now let us imagine a table, with one row for each description of a classification scheme 

and one column for each object to be classified. In the cell corresponding to a given

scheme and object, we write the number of the class assigned to that object by that 

classification scheme. Each row thus describes a grouping of the objects into classes. 

5 Now, we describe a grouping of the objects into classes which differs from every 

grouping in our list: 

(a) Starting in the first row and the first column, we examine the number written there. If 

that number is less than n, we add one to it; if it is equal to n, we subtract n - 1 from it. 

(b) Next, we go to the next row and the next column, and perform the same operation. 

(c) We thus describe a diagonal sequence of cells in the table, and for each column we 

specify a class number different from the one written there. The result is that we have 

assigned each object to a class, but the resulting grouping does not correspond to any 

grouping listed in the table (since it differs from each row in at least one position). 

We are forced, then, to conclude that even though our list of finite-length descriptions of 

classification schemes was assumed to be infinite, there is at least one assignment of 

objects to classes that does not correspond to any classification scheme in the list. (The 

list contains only the schemes with finite-length descriptions, but the classification we 

have just described requires an infinitely large table for its description, so it does not 

appear in the list.) There are, in fact, not just the one but an infinite number of such 

classifications which are not in the list. 

Since the list contains, by construction, every classification scheme that has a finitelength 

description, we must infer that the classifications described by the diagonal 

procedure outlined above do not have any finite-length description; let us call them, for 

this reason, ineffable classifications. 

The existence of ineffable classifications is not solely of theoretical interest; it may also 

serve as a salutary reminder that no single classification scheme can be expected to be 

"complete" in the sense of capturing every imaginable distinction or common property 

attributable to the members of 0. A "perfect" classification scheme, in the sense described 

above of a scheme that perfectly captures every imaginable similarity among the objects 

of 0, is thus a purely imaginary construct; actual classification schemes necessarily 

capture only a subset of the imaginable properties of the objects, and we must choose 

among them on pragmatic grounds. 

Make or Find? 

Whenever systematic classification is needed, the researcher may apply an existing 

classification scheme or else devise a new scheme for the purpose at hand. Existing 

schemes may be better documented and more widely understood than an ad hoc scheme 

would be; in some cases they will have benefited from more sustained attention to 

technical issues in the construction of a scheme than the researcher will be able to devote 

to a problem encountered only incidentally in the course of a larger research project.

Being based on larger bodies of material, they may well provide better coverage of 

unusual cases than the researcher would otherwise manage; they may thus be more likely 

to provide an exhaustive list of possible values for each axis. And the use of a standard 

classification scheme does allow more direct comparison with material prepared by 

others than would otherwise be possible. 

On the other hand, schemes with broad coverage may often provide insufficient depth for 

the purposes of specialized research (just as the thousand basic categories of the Dewey 

Decimal System will seldom provide a useful framework for a bibliography of secondary 

literature on a single major work or author), and the studied theoretical neutrality of 

schemes intended for wide use may be uncongenial to the purpose of the research. 

In the preparation of resources intended for use by others, the use of standard existing 

classification schemes should generally be preferred to the ad hoc concoction of new 

ones. Note that some existing classification schemes are proprietary and may be used in 

publicly available material only by license; before using an established classification 

scheme, researchers should confirm that their usage is authorized. 

For work serving a particular research agenda, no general rule is possible; the closer the 

purpose of the classification to the central problem of the research, the more likely is a 

custom-made classification scheme to be necessary. Researchers should not, however, 

underestimate the effort needed to devise a coherent scheme for systematic classification 

of anything. 

Some Existing Classification Schemes 

Classification schemes may be needed, and existing schemes may be found, for objects of 

virtually any type. Those mentioned here are simply samples of some widely used kinds 

of classification: classification of documents by subject or language variety, classification 

of words by word class or semantics, classification of extra-textual entities by socioeconomic 

and demographic properties, and classification of images. 

The best-known subject classification schemes are those used in libraries and in major 

periodical bibliographies to provide subject access to books and articles. The Dewey 

Decimal Classification (DDC) and its internationalized cousin the Universal Decimal 

Classification (UDC) are both widely used, partly for historical reasons (the Dewey 

system was the first widely promoted library classification scheme), partly owing to their 

relatively convenient decimal notation, and because their classification schedules are 

regularly updated. In the USA, the Library of Congress classification is now more widely 

used in research libraries, in part because its notation is slightly more compact than that 

of Dewey. 

Less widely used, but highly thought of by some, are the Bliss Bibliographic 

Classification, originally proposed by Henry Evelyn Bliss and now thoroughly revised, 

and the Colon Classification devised by Shiyali Ramamrita Ranganathan, perhaps the 

most important theorist of bibliographic classification in history (Melvil Dewey is surely

more influential but can hardly be described as a theorist). Both are fully faceted 

classification schemes. 

The controlled vocabulary of the Library of Congress Subject Headings may also be 

useful; its patterns for the subdivision of various kinds of subjects provide useful arrays 

for subordinate axes. 

Researchers in need of specialized subject classification should also examine the subject 

classifications used by major periodical bibliographies in the field; Balay (1996) provides 

a useful source for finding such bibliographies. 

The creators of language corpora often wish to classify their texts according to genre, 

register, and the demographic characteristics of the author or speaker, in order to 

construct a stratified sample of the language varieties being collected and to allow users 

to select subcorpora appropriate for various tasks. No single classification scheme 

appears to be in general use for this purpose. The schemes used by existing corpora are 

documented in their manuals; that used by the Brown and the Lancaster-Oslo/Bergen 

(LOB) corpora is in some ways a typical example. As can be seen, it classifies samples 

based on a mixture of subject matter, genre, and type of publication: 

• A Press: reportage 

• B Press: editorial 

• C Press: reviews 

• D Religion 

• E Skills, trades, and hobbiesz 

• F Popular lore 

• G Belles lettres, biography, essays 

• H Miscellaneous (government documents, foundation reports, industry reports, 

college catalogue, industry house organ) 

• J Learned and scientific writings 

• K General fiction 

• L Mystery and detective fiction 

• M Science fiction 

• N Adventure and western fiction

• P Romance and love story 

• R Humor 

Several recent corpus projects have produced, as a side effect, thoughtful articles on 

sampling issues and the classification of texts. Biber (1993) is an example. (See also 

chapter 21, this volume.) Some recent corpora, for example the British National Corpus, 

have not attempted to provide a single text classification in the style of the Brown and 

LOB corpora. Instead, they provide descriptions of the salient features of each text, 

allowing users to select subcorpora by whatever criteria they choose, in a kind of postcoordinate 

system. 

Some language corpora provide word-by-word annotation of their texts, most usually 

providing a single flat classification of words according to a mixture of word-class and 

inflectional information (plural nouns and singular nouns, for example, thus being 

assigned to distinct classes). A variety of word-class tagging schemes is in use, but for 

English-language corpora the point of reference typically remains the tag set defined by 

the Brown Corpus of Modern American English, as refined by the Lancaster-Oslo/Bergen 

(LOB) Corpus, and further refined through several generations of the CLAWS 

(Constituent Likelihood Automatic Word-tagging System) tagger developed and 

maintained at the University of Lancaster (Garside and Smith 1997). When new wordclass 

schemes are devised, the detailed documentation of the tagged LOB corpus 

(Johansson 1986) can usefully be taken as a model. 

Semantic classification of words remains a topic of research; the classifications most 

frequently used appear to be the venerable work of Roget's Thesaurus and the newer 

more computationally oriented work of Miller and colleagues on WordNet (on which see 

Fellbaum 1998) and their translators, imitators, and analogues in other languages (on 

which see Vossen 1998). 

In historical work, classification is often useful to improve the consistency of data and 

allow more reliable analysis. When systematic classifications are applied to historical 

sources such as manuscript census registers, it is generally desirable to retain some 

account of the original data, to allow consistency checking and later reanalysis (e.g., 

using a different classification scheme). The alternative, pre-coding the information and 

recording only the classification assigned, rather than the information as given in the 

source, was widely practiced in the early years of computer applications in history, since 

it provides for more compact data files, but it has fallen out of favor because it makes it 

more difficult or impossible for later scholars to check the process of classification or to 

propose alternative classifications. 

Historians may find the industrial, economic, and demographic classifications of modern 

governmental and other organizations useful; even where the classifications cannot be 

used unchanged, they may provide useful models. Census bureaus and similar 

governmental bodies, and archives of social science data, are good sources of information 

about such classification schemes. In the anglophone world, the most prominent social

science data archives may be the Inter-university Consortium for Political and Social 

Research (ICPSR) in Ann Arbor aa(http://www.icpsr.umich.edu/) and the UK Data 

Archive at the University of Essex (http://www.data-archive.ac.uk/). The Council of 

European Social Science Data Archives (http://www.nsd.uib.no/cessda/index.html) 

maintains a list of data archives in various countries both inside and outside Europe. 

With the increasing emphasis on image-based computing in the humanities and the 

creation of large electronic archives of images, there appears to be great potential utility 

in classification schemes for images. If the class formulas of an image classification 

scheme are written in conventional characters (as opposed, say, to being themselves 

thumbnail images), then collections of images can be made accessible to search and 

retrieval systems by indexing and searching the image classification formulas, and then 

providing access to the images themselves. Existing image classification schemes 

typically work with controlled natural-language vocabularies; some resources use 

detailed descriptions of the images in a rather formulaic English designed to improve the 

consistency of description and make for better retrieval. The Index of Christian Art at 

Princeton University (http://www.princeton.edu/~ica/) is an example. 

The difficulties of agreeing on and maintaining consistency in keyword-based 

classifications or descriptions of images, however, have meant that there is lively interest 

in automatic recognition of similarities among graphic images; there is a great deal of 

proprietary technology in this area. Insofar as it is used for search and retrieval, image 

recognition may be thought of as a specialized form of data-driven classification, 

analogous to automatic statistically based classification of texts. 


Anderson, James D. (1979). Contextual Indexing and Faceted Classification for 

Databases in the Humanities. In Roy D. Tally and Ronald R. Deultgen (eds.), 

Information Choices and Policies: Proceedings of the ASIS Annual Meeting, vol. 16 (pp. 

194–201). White Plains, NY: Knowledge Industry Publications. 

Balay, Robert, (ed.) (1996). Guide to Reference Books, 11th edn. Chicago, London: 

American Library Association. 

Biber, Douglas (1993). Representativeness in Corpus Design. Literary and Linguistic 

Computing 8, 4: 243–57. 

Borges, Jorge Luis (1981). The Analytical Language of John Wilkins, tr. Ruth L. C. 

Simms. In E. R. Monegal and A. Reid (eds.), Borges: A Reader (pp. 141–3). New York: 

Dutton. 

Bowker, Geoffrey C. and Susan Leigh Star (1999). Sorting Things Out: Classification 

and its Consequences. Cambridge, MA: MIT Press.

Deerwester, Scott et al. (1990). Indexing by Latent Semantic Analysis. Journal of the 

American Society for Information Science 41, 6: 391–407. 

Fellbaum, Christiane, (ed.) (1998). WordNet: An Electronic Lexical Database. 


Floud, Roderick (1979). An Introduction to Quantitative Methods for Historians. London 

and New York: Methuen. 

Foskett, A. C. (1996). The Subject Approach to Information, [n.p.]: Linnet Books and 

Clive Bingley, 1969. (4th edn. 1982. 5th edn. London: Library Association). 

Garfield, Eugene (1979). Citation Indexing: Its Theory and Application in Science, 

Technology, and Humanities. New York: Wiley. 

Garside, R. and N. Smith (1997). A Hybrid Grammatical Tagger: CLAWS4. In R. 

Garside, G. Leech, and A. McEnery (eds.). Corpus Annotation: Linguistic Information 

from Computer Text Corpora (pp. 102–21). London: Longman. 

Johansson, Stig, in collaboration with Eric Atwell, Roger Garside, and Geoffrey Leech 

(1986). The Tagged LOB Corpus. Bergen: Norwegian Computing Centre for the 

Humanities. 

Kuhn, Thomas (1977). Second Thoughts on Paradigms. In Frederick Suppe (ed.), The 

Structure of Scientific Theories, 2nd edn. (pp. 459–82). Urbana: University of Illinois 

Press. 

Library of Congress, Cataloging Policy and Support Office (1996). Library of Congress 

Subject Headings, 19th edn., 4 vols. Washington, DC: Library of Congress. 

Mills, Harlan (1983). The New York Times Thesaurus of Descriptors. In Harlan Mills, 

Software Productivity (pp. 31–55). Boston, Toronto: Little, Brown. 

Mills, Jack, and Vanda Broughton (1977–). Bliss Bibliographic Classification, 2nd edn. 

London: Butterworth. 

Ranganathan, S[hiyali] R[amamrita] (1967). Prolegomena to Library Classification, 3rd 

edn. Bombay: Asia Publishing House. 

Ranganathan, Shiyali Ramamrita (1989). Colon Classification, 7th edn. Basic and Depth 

version. Revised and edited by M. A. Gopinath. Vol. 1, Schedules for Classification. 

Bangalore: Sarada Ranganathan Endowment for Library Science. 

Svenonius, Elaine (2000). The Intellectual Foundation of Information Organization. 

Cambridge, MA: MIT Press.

Vossen, Piek (1998). Introduction to EuroWordNet. Computers and the Humanities 32: 

73–89. 

15. 

Databases 

Stephen Ramsay 

Introduction 

Databases are an ubiquitous feature of life in the modern age, and yet the most allencompassing 

definition of the term "database" – a system that allows for the efficient 

storage and retrieval of information – would seem to belie that modernity. The design of 

such systems has been a mainstay of humanistic endeavor for centuries; the seeds of the 

modern computerized database being fully evident in the many text-based taxonomies 

and indexing systems which have been developed since the Middle Ages. Whenever 

humanists have amassed enough information to make retrieval (or comprehensive 

understanding) cumbersome, technologists of whatever epoch have sought to put forth 

ideas about how to represent that information in some more tractable form. 

The computerized database, while a new development in this broad historical schema, 

nonetheless appeared more or less simultaneously with the early use of computers in 

academic and commercial environments. In such contexts, the essential problem of 

organization and efficient retrieval (usually understood as falling under the rubric of data 

structures and algorithms respectively) is complicated by the need for systems which 

facilitate interaction with multiple end users, provide platform-independent 

representations of data, and allow for dynamic insertion and deletion of information. The 

use of database technology among humanists has been invigorated by the realization – 

common, perhaps, to many other similar convergences – that a number of fascinating 

problems and intellectual opportunities lurk beneath these apparently practical matters. 

The inclusion of certain data (and the attendant exclusion of others), the mapping of 

relationships among entities, the often collaborative nature of dataset creation, and the 

eventual visualization of information patterns, all imply a hermeneutics and a set of 

possible methodologies that are themselves worthy objects for study and reflection. 

This chapter provides an introduction to these issues by working through the design and 

implementation of a simple relational database (our example stores basic information 

about books in print). The intent, of course, is to remove some of the complexities and 

idiosyncrasies of real-world data in order that the technical and conceptual details of 

database design might more readily emerge. The data to which humanist scholars are 

accustomed – literary works, historical events, textual recensions, linguistic phenomena – 

are, of course, rarely simple. We would do well, however, to bear in mind that what 

might be viewed as a fundamental inadequacy has often proved to be the primary 

attraction of relational database systems for the humanist scholar. Rather than exploiting

a natural congruity between relational ontologies and humanistic data, scholars have 

often sought insight in the many ways in which the relational structure enforces a certain 

estrangement from what is natural. The terms we use to describe books in a bookstore 

(authors, works, publishers) and the relationships among them (published by, created by, 

published in) possess an apparent stability for which the relational model is ideally suited. 

The most exciting database work in humanities computing necessarily launches upon less 

certain territory. Where the business professional might seek to capture airline ticket sales 

or employee data, the humanist scholar seeks to capture historical events, meetings 

between characters, examples of dialectical formations, or editions of novels; where the 

accountant might express relations in terms like "has insurance" or "is the supervisor of", 

the humanist interposes the suggestive uncertainties of "was influenced by", "is 

simultaneous with", "resembles", "is derived from." 

Such relationships as these hold out the possibility not merely of an increased ability to 

store and retrieve information, but of an increased critical and methodological selfawareness. 

If the database allows one to home in on a fact or relationship quickly, it 

likewise enables the serendipitous connection to come forth. Relational databases in 

humanistic study are, in this sense, not so much pre-interpretative mechanisms as parainterpretative 

formations. As with so many similar activities in digital humanities, the act 

of creation is often as vital to the experiential meaning of the scholarly endeavor as the 

use of the final product. 

Relational database management systems (RDBMS) represent the most popular way of 

creating searchable ontologies both among computing humanists and among 

professionals in other areas of research and industry, and so this chapter will be 

concerned primarily with the design and implementation of database systems using the 

relational model. Still, the modern database landscape continues to evolve. Some 

consideration of where databases may yet be going (and where humanists may be going 

with database technology) is therefore apposite as well. 

The Relational Model 

E. F. Codd first proposed the relational model in a 1970 article in Communications of the 

ACM entitled "A Relational Model of Data for Large Shared Databanks." Codd's 

proposal endeavored to overcome the limitations of previous systems, which had suffered 

from difficulties related both to inefficient (which is to say slow) access and unwieldy 

storage mechanisms – inefficiencies that often resulted from redundancies in the 

underlying data representation. Codd's model made great strides forward in both areas, 

and yet his achievement is perhaps more acutely evident in the mathematical presentation 

of his ideas. One researcher, who refers to the 1970 paper as "probably the most famous 

paper in the entire history of database management", notes: 

It was Codd's very great insight that a database could be thought of as a set of relations, 

that a relation in turn could be thought of as a set of propositions …, and hence that all of 

the apparatus of formal logic could be directly applied to the problem of database access 

and related problems.

(Date 2001) 

This fundamental idea has spawned a vast literature devoted to database theory, and 

while there have been several major additions to the relational model, the relational 

databases of today continue to operate on the basis of Codd's insights. 

Database Design 

The purpose of a database is to store information about a particular domain (sometimes 

called the universe of discourse) and to allow one to ask questions about the state of that 

domain. Let us suppose, for example, that we are creating a database that will contain 

information about current editions of American novels. Our goal will be to create a 

system that can store information about authors, works, and publishers, and allow us to 

ask questions like "What publications did Modern Library produce in 1992?" and "Which 

works by Herman Melville are currently in print?" The simplest database of all would 

simply list the data in tabular form (see table 15.1). 

This database might be expanded to include a vast collection of authors and works. With 

the addition of some mechanism with which to store and query the data, we can easily 

imagine a system capable of answering the questions we would like to pose. Yet the 

inefficiencies, which the relational model endeavors to overcome, are evident even in this 

simple example. A search for "Mark Twain" will require that the system continue 

iterating through the rows after it has found its first hit in order to ensure that the relevant 

matches have been found. This is because our data model allows – and indeed, demands 

– that the name of the author be entered into every row in which a new work is 

introduced. Similar redundancies occur with dates of publication, publisher names, and 

publisher addresses. Moreover, a change to an author's name (for example, the decision 

to enter "Samuel Clemens" in place of the author's pen name) will require that we update 

all fields in which the original name appears. Even if we devise some mechanism for 

ensuring a certain vigilance on the part of the machine, we are still left with a version of 

the same problem: having to go to "places instead of just one. In our example, the 

redundancy seems unproblematic – any machine can make quick work of a database with 

six items". In a system containing thousands or perhaps millions of items, the extra time 

and space required to perform search algorithms can become a severe liability. 

15.1 Table 

Last First YOB YOD Title 

Pub 

Year 

Publisher 

Pub 

Address 

Twain Mark 1835 1910 Huckleberry 1986 Penguin USA New York 

Twain Mark 1835 1910 Tom Sawyer 1987 Viking New York 

Cather Willa 1873 1947 My Antonia 1995 

Library of 

America 

New York

Hemingway Ernest 1899 1961 The Sun Also Rises 1995 Scribner New York 

Wolfe 

Look Homeward, 

Thomas 1900 1938 

Angel 

1995 Scribner New York 

Faulkner 

The Sound and the 

William 1897 1962 

Furry 

1990 Random House New York 

Relational modeling attempts to factor these redundancies out of the system. We can 

begin modifying our original design by isolating the individual entities in the domain at a 

more abstract level; that is, by locating the types of information that vary independently 

of one another. The preliminary outline shown in figure 15.1 might emerge as one 

possible representation of the domain. 

Each of the boxes in figure 15.1 illustrates a particular entity with a set of associated 

attributes. We have retained all the information from the original design, but have 

sequestered the various entities from one another according to certain logical groupings: 

authors (who have last names, first names, and dates of birth and death), works (which 

have titles and years of publication), and publishers (which have names and cities where 

they are headquartered). Often, the nouns we use to describe the domain and the 

recurrence of the word "have" helps to establish these entities and their attributes. To this 

basic outline we may also add a set of verb phrases describing the nature of the 

relationships among the entities. For example, authors create works, and works are, in 

turn, published by publishers. The addition of this information can then give rise to what 

is called an entity relationship (ER) diagram (see figure 15.2). 

This diagram captures the basic relationships we have isolated, but it remains to say how 

many instances of a single entity can be associated with other entities in the model. For 

example, one author may contract with several publishers, and a publisher may offer 

many different works by multiple authors. There are several ways to capture these 

features diagrammatically 1 . We will simply use the number "1" to indicate a single 

instance and an "M" to indicate multiple instances (see figure 15.3). We may then read 

the relationship line connecting authors and works to mean "One author has many 

works."

15.1 Figure 

Thus far, we have been pursuing the logical design of our database – a design entirely 

independent of both the eventual machine representation of the data and of the end user's 

view of the information contained within it. We would do well at this point to imagine 

how this logical structure might be populated with the particular instances from the first 

model. To do this, we need to make a subtle mental shift in the way we view the entity 

relationship diagram. We might be tempted to see the various boxes as storage areas that 

can hold instances of authors' last names, work titles, and so forth. However, we have 

been really modeling the generic form that the particular instances of data will take. The 

populated database is properly conceived of as a set of tables with rows and columns, in 

which each row corresponds to the entities and each column to the attributes in the ER 

diagram. These rows are usually referred to as records and the intersection of rows and 

columns as fields. Table 15.2, for example, is a mock-up of a populated database built 

according to the terms of the ER diagram. 

This more concrete view of our database captures the entities, but it makes no mention of 

the relationships. In order to represent the relationships between records, we need to 

introduce some variable that can hold these connections. 

Our ability to do this will be significantly enhanced if we can devise some way to refer to 

each individual instance of a particular entity as a unique datum. After all, the final 

database will not merely connect authors to works in some generic way, but will reflect 

the fact that, for example, the author Mark Twain created both Huckleberry Finn and 

Tom Sawyer. The usual method for establishing this uniqueness is to create a primary key 

for each record – a unique value associated with each individual record in a table 2 . This 

value is simply a new attribute which can be added to our ER diagram, and by extension, 

a new column in the final database for each record type. The author entity, for example, 

may be modified as shown in figure 15.4.

15.2 Figure 

15.3 Figure 

15.2 Table 

AUTHORS 

Last Name First Name Year of Birth Year of Death 

Twain Mark 1835 1910 

Cather Willa 1873 1947 

Hemingway Ernest 1899 1961 

Wolfe Thomas 1900 1935 

Faulkner William 1897 1962

WORKS 

Title PubYear 

The Adventures of Huckleberry Finn 1986 

Tom Sawyer 1987 

My Antonia 1995 

The Sun also Rises 1995 

Look Homeward, Angel 1995 

The Sound and the Fury 1990 

PUBLISHERS 

Name City 

Penguin USA New York 

Library of America New York 

Schribner New York 

Viking New York 

Random House New York 

15.4 Figure 

The resulting database table would then include a new column to hold this value (see 

table 15.3). With the other entities similarly modified, we now have a way of referring to 

each individual record in a table without ambiguity. 

The next step is really at the heart of the relational model. In order to capture the one-tomany 

(1:M) relationship between authors and works, we introduce a second key attribute 

to the entities on the "many" side of the relationship – one that can hold a reference (or 

pointer) back to the entity on the "one" side of the relationship. This reference is, like the 

primary key, simply another attribute called a foreign key. Table 15.4, for example, 

shows how the "Works" table would look with additional fields for primary and foreign 

keys. 

The foreign key field contains the primary key of the record with which it is associated. 

Thus the records for The Adventures of Huckleberry Finn and Tom Sawyer (which have 

been assigned primary keys 1 and 2, respectively) now contain foreign key references to

the record in the "Authors" table which bears primary key 1 (the record for "Mark 

Twain"). In this way, the database is able to retain one reference for the author "Mark 

Twain." The redundancy that hindered the original design has been eliminated. 

Unfortunately, the database still contains other instances of redundancy. For example, 

every publisher in our database is located in New York, which means this information is 

repeated for all six publisher records. Theoretically, this situation could have been 

avoided if we had held ourselves to a very strict interpretation of "types of information 

that vary independently of one another" in our initial ER diagram. In practice, such 

redundancies are often difficult to discern upon initial analysis of the domain. It may 

even be that some redundancies only appear after a considerable amount of data has 

already been entered into a prototype system. 

15.3 Table 

AUTHORS 

Author ID Last Name First Name Year of Birth Year of Death 

1 Twain Mark 1835 1910 

2 Cather Willa 1873 1947 

3 Hemingway Ernest 1899 1961 

4 Wolfe Thomas 1900 1938 

5 Faulkner William 1897 1962 

15.4 Table 

WORKS 

Work ID Title PubYear Author ID 

1 The Adventures of Huckleberry Finn 1986 1 

2 Tom Sawyer 1987 1 

3 My Antonia 1995 2 

4 The Sun Also Rises 1995 3 

5 Look Homeward, Angel 1995 4 

6 The Sound and the Fury 1990 5 

In any event, eliminating the redundancy in the "Publishers" table is simply a matter of 

breaking the "City" attribute off into its own table, assigning a primary key value to each 

record instance, and providing a new foreign key field in the "Publishers" table which can

hold a reference to the correct city. In other words, we need to take one of our attributes 

and elevate it to the status of entity (see table 15.5). 

Since primary key values in one table can be referenced from multiple tables as foreign 

keys, this restructuring may have a useful side effect if we ever decide to add "Place of 

Birth" to the Authors table. 

15.5 Table 

PUBLISHERS 

Pub ID Name City ID 

1 Penguin USA 1 

2 Library of America 1 

3 Scribner 1 

4 Viking 1 

5 Random House 1 

CITIES 

CityID City 

1 New York 

There is another kind of relationship in this domain which isn't represented; namely, the 

many-to-many (M:M) relationship. This situation might easily arise if several publishers 

release editions of the same work. We would naturally describe this relationship as being, 

like all the relationships in the current design, one-to-many, but in this case, there is 

already a one-to-many relationship going the other way (from "Publishers" to "Works"). 

One might be tempted simply to introduce a foreign key pointing to "Works" from 

"Publishers" to complement the foreign key pointing from "Publishers" to "Works." 

However, the more apt solution is to abstract the relationship into a new entity called an 

association (or junction table). An association is simply a new table which contains the 

two related foreign keys (see table 15.6). This association captures the fact that Penguin 

USA (Pub ID 1) publishes The Adventures of Huckleberry Finn (Work ID 1) and an 

edition of Tom Sawyer (Work ID 2). 

Each record in an association may be assigned a primary key, but in this case (and in the 

case of most associations) the combination of the two primary keys is understood to 

represent a unique combination. Most RDBMSs therefore allow one to declare the pair of 

values to be the primary key for that record (the creation of these compound keys will be 

discussed in the next section). 

We have now analyzed our domain with entity-relationship modeling and have begun to 

factor out the major redundancies in the model. Readers interested in the formal

explication of these methodologies will find abundant resources in the scholarly literature 

of the field, and while it is unnecessary to go into these matters here, at least one aspect 

of the more technical discussion deserves mention even in an introductory context. 

Database theorists (and serious designers) often speak of databases as being in one of five 

normal forms. The normal forms may be stated in set theoretic terms, but they may also 

be stated more simply as design criteria by which to judge the soundness of one's design. 

For example, one practically minded book paraphrases first normal form by stating that 

"at each row-and-column intersection, there must be one and only one value, and that 

value must be atomic: there can be no repeating groups in a table that satisfies first 

normal form" (Bowman et al. 1999). 

By the time a database is in fifth normal form, all redundancy has been removed; as 

Bowman puts it, "Tables normalized to this extent consist of little more than primary 

keys" (Bowman et al. 1999). This has the advantage of making it easier for the RDBMS 

to ensure the overall integrity of the data, but one may find that queries on those data 

become rather confusing to compose. As with most matters related to computer 

programming, one needs to balance the goals of correctness against the practical 

exigencies of the system and its users. 

15.6 Table 

PUBLISHER-WORKS TABLE 

Pub ID Work ID 

1 1 

1 2 

2 2 

3 3 

4 4 

Schema Design 

Up until now, our meditations on database design have been confined to what one might 

normally undertake at the whiteboard. The actual implementation of the design is much 

more akin to programming. Fortunately, there is little in the implementation stage that 

requires new concepts; for the most part, we simply need to translate our design into a 

representation intelligible to the machine. This representation is usually referred to as a 

database schema, and is created using Structured Query Language (SQL) 3 . 

There are a number of excellent SQL-compliant RDBMSs available to the humanist 

scholar. The majority of humanities computing projects that use databases employ free

(open source) systems, of which the most popular are MySQL, mSQL, and Post-greSQL. 

There are also a number of commercial systems in use (Oracle, Microsoft Access, IBM 

DB2). The systems differ somewhat in their feature sets and in the amount of 

documentation available, but all provide efficient, feature-rich implementations of the 

relational model with advanced security and management functions. For our database, we 

will use PostgreSQL – a free, well-supported RDBMS for Unix-like systems which can 

be downloaded over the Internet. 4 

The database schema is nothing more than a machine-readable representation of the ER 

diagram. We may begin by laying out the various entities and their attributes, but since 

we are now giving instructions to the machine, we need to be more specific about the 

precise nature of the data to be entered: 

Like most programming languages, SQL includes the notion of a datatype. Datatype 

declarations help the machine to use space more efficiently and also provide a layer of 

verification for when the actual data is entered (so that, for example, a user cannot enter 

character data into a date field). In this example, we have specified that the last_name, 

first_name, title, city, and name fields will contain character data of varying length (not 

to exceed 80 characters), and that the year_of_birth, year_of_cleath, and pub_year fields 

will contain integer data. Other possible datatypes include DATE (for day, month, and 

year data), TEXT (for large text blocks of undetermined length), and BOOLEAN (for 

true/ false values). Most of these can be further specified to account for varying date 

formats, number bases, and so forth. PostgreSQL, in particular, supports a wide range of 

datatypes, including types for geometric shapes, Internet addresses, and binary strings. 

Databases often differ in the way they represent (and ensure the integrity of) primary 

keys. In PostgreSQL, the usual method is to create a separate mechanism for generating 

and keeping track of unique numeric values, and to have the tables containing the entities

etrieve a value from that mechanism each time a new record is created. So, for example, 

to create a primary key for the authors table, we first create what is called a sequence 

table that will hold the set of unique values for that table: 

We can then add a new field to the author table which will have as its default value the 

next value called from the appropriate sequence: 

This new line thus amounts to the following declaration about the author_icl field: the 

author_icl will have as its default value an integer that corresponds to the next value 

provided by the sequence named author_seq. 5 

We also want to declare this value specifically as the primary key for the table. This is 

accomplished with the following addition: 

A foreign key is simply another data field, which, like the primary key field, has been 

specifically designated as a key. In order to capture the one-to-many relationship between 

works and authors, for example, we would modify the works table as follows:

The PRIMARY KEY () specifier also makes it easy to declare the publisher and work ids 

in our publisher/work association as a compound key: 

The designation of primary and foreign keys is one of the most important aspects of 

schema design, because it helps the system to achieve what is called referential integrity. 

Referential integrity is compromised when we delete a record that contains a reference to 

another record. So, for example, if we were to delete an author from the authors table, we 

might leave a reference to that author in the works table without a referent. A good 

RDBMS will use the primary and foreign key references either to prevent this from 

occurring or to warn the database administrator that the operation will result in a dangling 

reference (sometimes called a null pointer). 

The method for creating an empty database and getting this schema into the RDBMS 

varies from system to system. Most RDBMSs provide command-line tools for setting up 

databases and for executing commands through an interactive command interpreter. The 

documentation for the particular RDBMS will discuss such matters in detail 6 . 

Importing Data 

Importing data into this schema requires the use of another set of SQL commands. The 

most useful of these is the INSERT keyword, which adds a record to one of the tables 

specified in the schema: 

The structure of this command is such that the target fields are declared in the first set of 

parentheses and the actual values in the second (the structure of the DELETE command,

for removing data from a database, uses the same syntax). In most systems, character 

strings must be enclosed in quotation marks. Notice also that the INSERT statement does 

not include a primary key because our database schema has already instructed the system 

to use the next value in the sequence table as the default. A work record may be added in 

much the same way: 

In this case, however, we need to add a foreign key value that contains a primary key 

from the authors table. There are three ways to accomplish this. The first is simply to 

look up the appropriate record in the authors table before executing the INSERT 

statement for the work, note its primary key, and add this to the statement. The second 

way is to perform an UPDATE statement after the record has been created, which adds 

the primary key for the author to the foreign key field author_icl of the work record. The 

third, and perhaps the most efficient way, is to embed the statement that looks up the 

appropriate primary key into the INSERT statement for adding a work record. All three 

of these methods require that we have an understanding of how to query the database for 

information, so let us defer this discussion for a moment while we explore the query 

commands of SQL. 

Database Queries 

The database administrator can query the database using the SELECT command. To 

retrieve all the last names currently in the authors table, for example, one would execute 

the following command: 

Most RDBMSs will produce output that looks something like this: 

In most circumstances, however, we want to create a more complex query that will let us 

home in on a particular record. Here, for example, is a SELECT statement that only 

selects authors born after 1890:

This statement begins by isolating two fields which should be returned to the user, but 

then adds a WHERE clause which qualifies the search according to particular criteria – in 

this case, to records in which the integer value contained in the year_of_birth field is 

greater than (or equal to) 1890. The result is a list of authors: 

Assuming we have already created records for Mark Twain and for The Adventures of 

Huckleberry Finn, we can now use the query syntax to discover the primary key for Mark 

Twain and use it to fill in the foreign key field for his works. The first is accomplished 

with a SELECT statement: 

The system returns the value "1." We can now use an UPDATE statement to modify the 

appropriate work record: 

The more efficient method alluded to above – loading a record into the works table with 

the appropriate foreign key in one statement – uses what is called a subselect: 

This statement follows the usual syntax of the INSERT statement, except that the last 

value in the second set of parentheses (the one pertaining to author_id) is itself a 

SELECT statement designed to return a single value: the primary key for the author 

record with the word "Twain" in the last_name field. 

Database queries can reach a significant level of complexity; the WHERE clause can 

accept Boolean operators (e.g.''WHERE year_of_birth > 1890 AND year_of_-birth < 

1900''), and most SQL implementations offer keywords for changing the ordering of the 

output (e.g. ORDER BY year_of_birth). Taking full advantage of the relational model, 

moreover, requires that we be able to gather information from several tables and present 

it in one result list. This operation is called a join. 

Let us suppose that we want to return a single result set that lists the authors' names, 

titles, and publishers of all works in the database. Doing so will require that we gather

information from the author, publisher, and work tables, and also from the association 

that links publishers with works. The syntax for constructing this query follows the 

general template for SELECT … FROM … WHERE … but with a twist: 

Any database of non-trivial complexity will have equivalent column names spread across 

several tables. Such equivalence will naturally occur with columns representing common 

concepts (such as "date"), but it will occur inevitably with the keys (since the primary key 

label in one table will often occur as the foreign key label in several others). The key to 

understanding this join query lies in the FROM clause, where each table participating in 

the join is aliased to a short variable. In this query, the variable a will stand in for the 

authors table, w for works, and so on. Thus we may read the first part of the query as 

saying "Select the last_name field from the authors table, the first_name field from the 

authors table, the name field from the publishers table, and the title field from the works 

table." The WHERE clause then tries to match up the appropriate key columns: the 

publisher_id in the publishers and pub_works tables must match, the work_id in the 

works and pub_works tables must match, and the author_ids in the authors and works 

tables must match. The RDBMS will then return all records that match all of these 

criteria in the order in which they were requested in the first part of the query: 

Constraining this query to a single author is simply a matter of adding another constraint 

to the WHERE clause: au . last_name = d 'Twain'. 

The necessity for join operations (and subselects, which can often be used to accomplish 

the same thing) increases as the databases approach full normalization. Database 

administrators wishing to take full advantage of a good relational design will want to 

study the particular features of their SQL implementation closely. 

Database Management

A good RDBMS will include all the facilities necessary for implementing a schema, 

importing data into the system, and performing queries. However, such facilities 

represent only part of the overall set of functions and capabilities necessary for creating a 

production system. 

Prudence would suggest that only a few privileged users should possess the ability to 

create and destroy databases, and that a broader (but not unlimited) set of users be able to 

add data to existing databases. There may also be administrative data stored in the 

database that are not intended for ordinary users of the system. All of the major RDBMSs 

provide facilities – ranging from simple permissions files to elaborate security systems – 

for limiting user access to the system. Most simply use a separate database (integral to the 

RDBMS itself) that permits or denies the ability to run specific commands (often using 

the GRANT and REVOKE keywords from SQL). Since RDBMSs usually run as network 

processes (in order to allow for remote connections), some mechanism should also be in 

place for limiting connections to particular network domains. Unlike simple user 

applications (which usually come with a reasonable set of security defaults), advanced 

multi-user applications often assume that the administrator will take the time to study the 

security model carefully and implement the appropriate procedures. The assumption that 

users will do this is perhaps naive, but the assumption that the developers have already 

done everything to make the particular installation secure can be reckless indeed. 

Transaction management represents another related set of concerns. Suppose that we had 

created a large file full of INSERT statements for the initial load of our data, but had left 

off the semicolon at the end of one of the statements. In all likelihood, the RDBMS 

(interpreting each INSERT statement as a discrete SQL operation) would execute the first 

n number of statements, inserting the data for each one, only to break in the middle of the 

list. We could perhaps delete the data already inserted, fix the error in the file, and reload 

the entire file, or perhaps we could erase the INSERT statements from the file that we 

have already executed and start anew. We would do better, however, if we had some way 

to declare all the INSERT statements in the file as an "all-or-nothing" block; the database 

has to perform all of these operations successfully or else it must exit with a warning, 

having performed none of them. Most systems provide some method for declaring a set 

of statements as a transaction block of this kind. In PostgreSQL, for example, the 

keywords are BEGIN (for signaling the beginning of a transaction), COMMIT (for 

executing the statements in the event that the proposed operations would generate no 

errors), and ROLLBACK (for undoing a set of operations). 7 

While the relational model lends itself to efficient storage and retrieval mechanisms, the 

sheer number of insertions, deletions, and updates on a production database system will 

often result in a certain fragmentation of the data (much in the way a hard drive can 

become fragmented after many frequent read-write operations). Much research has been 

devoted to finding new ways to defragment and otherwise optimize storage and retrieval 

in heavily used RDBMSs. While some of these operations occur behind the scenes, 

others are designed to be customized by the administrator for local circumstances. Such 

optimizations become more and more pertinent as a database (or a userbase) grows in 

size, and administrators will want to avail themselves of these facilities when appropriate.

Databases and Software Development 

Database administrators (particularly those working in a Unix environment) will often 

find that the humble command-line interface represents the most efficient way to create 

database schema, load large files full of SQL statements, check the validity of queries, 

and perform the various tasks associated with user accounts and security. However, many 

database applications in humanities computing are intended for use outside the group of 

scholars and developers implementing the system. In such cases, low-level access to the 

RDBMS is rarely desirable. Indeed, it is often useful to design a system that shields users 

from SQL entirely. 

The construction of middleware systems intended to provide such abstraction is among 

the more common software development tasks in humanities computing. In the case of 

both standalone and web-based applications, the usual goal is to place the execution of 

SQL queries and commands "in between" the user interface and the underlying RDBMS. 

Such a design (often called an n-tiered design) has the additional virtue of allowing the 

interface, database, and intervening programming logic to vary independently of one 

another; in a well-designed system, a change to the user interface (for example) would 

ideally imply only minor changes to the other tiers. Fortunately, nearly every 

programming language in common use among computing humanists provides elaborate 

mechanisms for executing SQL commands against a database from within an application. 

The DBI (or DataBase independent) module is an extension to the Perl programming 

language which provides a set of functions and variables for communicating with an 

RDBMS. In addition to allowing one to pass SQL statements to a database, retrieve 

results, and restructure data for delivery (to, for example, a web browser), DBI also 

provides an extremely powerful plug-in architecture that allows one to use the same 

program with several database implementations. So, for example, a program using the 

DBI interface could retrieve values from a web form, embed those values in a series of 

SQL statements, and pass them off to the RDBMS. Instead of going directly to the 

RDBMS, however, the DBI system would look for a driver (or DBD module) 

corresponding to the particular RDBMS being used, and effectively translate the DBI 

SQL syntax into the dialect of the particular database. Switching from PostgreSQL to 

MySQL (or perhaps running the two simultaneously) is therefore simply a matter of 

adding a new driver. DBI is also fairly mature; there are DBD modules corresponding to 

nearly every major database implementation, and all are freely available. 8 

Java programmers can find the same concept implemented in the JDBC (or Java. 

DataBase Connectivity) library. JDBC is one of the more comprehensive 

implementations of SQL, and drivers are also available for all the major RDBMSs. Java 

also has the advantage of avoiding the overhead of CGI (Common Gateway interface) 

upon which Perl and other similar scripting languages depend. CGI usually requires that a 

separate copy of the language interpreter be loaded into memory each time the program is 

invoked. The Java servlet architecture (intended for creating server-side applications) 

avoids this overhead by having a copy of the Java Virtual Machine (JVM) running 

constantly in memory with various caching facilities for the individual programs running

on it 9 . This method is generally superior to CGI and can leverage the considerable power 

and flexibility of the Java programming language, but one sacrifices the simplicity of the 

CGI in the process. 

Another technology that deserves mention is PHP (PHP Hypertext Processor). PHP is a 

programming language that can be embedded in ordinary HTML pages and interpreted 

by a program embedded into the web server itself (thus aligning itself with Java servlets 

in its avoidance of CGI). PHP does not employ the concept of a single interface with 

multiple drivers, but instead provides built-in function sets for all the major RDBMSs. Of 

the three middleware technologies here discussed, PHP is perhaps the easiest to use, but it 

is unfortunately the least "middle" of the three. Embedding code into an HTML page 

implies mingling the logic of the user interface with the programming logic – a 

convergence that can lead to systems which are difficult to maintain. Often, the size (and 

predicted mutability) of the system will determine whether the simplicity of PHP 

outweighs the potential for confusion later on. 10 

Alternative Models 

The relational database model has been an extremely successful one. Previous models – 

hierarchical databases and network databases – are seldom used today, and it seems clear 

that the relational model will continue to be the dominant one for many years to come 11 . 

However, there are a number of competing models which have been the subject of active 

research in computer science and information technology circles over the last two 

decades. While none are as widely employed as the relational model, the usual proclivity 

for exploration and early adoption among computing humanists may well serve to bring 

these models into prominence in humanities research. 

The most active contender for the prominence of the relational model is the objectoriented 

(OO) database model. The impetus for its creation lies in the widespread use of 

the object-oriented programming paradigm among software engineers. The details of this 

paradigm are beyond the scope of this discussion, but some sense of the model may be 

gleaned from the observation that relational schemas rely upon an older conception of 

programming in which the data and the procedures for manipulating those data are kept 

separate from one another. In our database, for example, the data pertaining to authors are 

entirely separate from the various operations (queries) we would like to perform on those 

data. The object-oriented model proposes that the data and the procedures be refactored 

into discrete objects. The data for the author table and the elements of the operations that 

can be performed on them would therefore belong to the same basic structure. Partisans 

of OO argue that this model facilitates maintenance by creating fewer dependencies 

between data items, allows for reusable data modules that can be easily moved from one 

database context to another, and creates a certain semantic richness (through the creation 

of inheritance hierarchies) in the data unattainable with more conventional methods. 

While the more mainstream database developers have been reluctant to embrace this 

model fully, the relational database landscape has begun to see implementations which 

bill themselves as object-relational database management systems (ORDBMS). Such

implementations typically fall short of the fully object-oriented databases envisioned by 

researchers, but borrow the notions of inheritance (having one table "inherit" properties 

from another without duplication) and complex object creation commonly associated 

with object-oriented systems. It seems clear that the trend toward such hybrid systems 

will continue. 

Databases and the Humanist 

Whether it is the historian attempting to locate the causes of a military conflict, the 

literary critic teasing out the implications of a metaphor, or the art historian tracing the 

development of an artist's style, humanistic inquiry reveals itself as an activity 

fundamentally dependent upon the location of pattern. Dealing with patterns necessarily 

implies the cultivation of certain habits of seeing; as one critic has averred: "Recognizing 

a pattern implies remaining open to gatherings, groupings, clusters, repetitions, and 

responding to the internal and external relations they set up" (Hunter 1990). Of all the 

technologies in use among computing humanists, databases are perhaps the best suited to 

facilitating and exploiting such openness. To build a database one must be willing to 

move from the forest to the trees and back again; to use a database is to reap the benefits 

of the enhanced vision which the system affords. 

Humanists have used relational databases as the engines behind complex visualization 

systems, text archives, and multimedia works. In most cases the intent has been merely to 

leverage the efficiencies of the relational model as a means for storing and retrieving the 

information needed to populate a map, load a list of hits, or assemble a website. 

However, even in these relatively simple applications it becomes clear that the underlying 

ontology has considerable intellectual value. A well-designed database that contains 

information about people, buildings, and events in New York City contains not static 

information, but an entire set of ontological relations capable of generating statements 

about a domain. A truly relational database, in other words, contains not merely "Central 

Park", "Frederick Law Olmstead", and "1857", but a far more suggestive string of logical 

relationships (e.g., "Frederick Law Olmstead submitted his design for Central Park in 

New York during 1857"). 

One possible path for the future may seek to exploit further the implications of 

collaborative database creation. A database, as we have seen, can be set up in such a way 

as to allow multiple users access to the insert mechanisms of the system. The challenges 

proposed by this possibility are substantial, since the robustness of a system largely 

depends upon its consistency. Enforceable rules mechanisms (far above the level of mere 

transaction management) would need to be devised to ensure such consistency. The 

successful employment of such systems in humanistic contexts, however, would expand 

the possibilities of knowledge representation considerably. Since the data would enter 

into the system from a number of different sources, the logical statements that would 

flow from that ontology would necessarily exceed the knowledge of any one individual. 

The power of relational databases to enable the serendipitous apprehension of 

relationships would be that much more increased.

There is, of course, ample precedent for using complex, collaboratively managed data 

structures in humanistic inquiry. The earliest concordances were nominally produced to 

assist scholars in locating passages in the Bible, and one of the earliest uses of computers 

in humanistic study was a concordance of Thomas Aquinas. In both cases, the ultimate 

goal was not efficient retrieval, but interpretative insight. It only seems appropriate that 

after we have designed and implemented relational systems, and reaped the benefits of 

the efficiencies they grant, we consider the role they may play in the varied pursuits 

which have descended from what was once called – appropriately – the higher criticism. 

Note 

1 Entity Relationship Modeling (developed by Peter Chen in the mid-1970s) is by far the 

most popular diagramming notation; even people unacquainted with formal 

methodologies will often adopt something like it when working through an initial design. 

Unfortunately, there is no standard outlining the precise notation (and thus enabling 

designers to communicate in a common format). Other, perhaps more sophisticated 

notations include Object-Role Modeling (ORM) and (for object- oriented database 

design) Unified Modeling Language (UML). See Halpin (2001). 

2 Some database systems can generate a unique value for each record in the entire 

database. While this ensures an additional level of integrity useful (and perhaps even 

requisite) for other aspects of database management, it is not strictly necessary for the 

relational model as such. 

3 SQL is specified in a standards document first issued by the American National 

Standards Institute in 1983, later revised in 1992, and again in 1999- While this standard 

has gone a long way toward creating a common tongue among database programmers, 

companies and developer groups that create RDBMSs continue to introduce subtle 

dialectical differences between implementations. Fortunately, these differences are 

relatively minor; the programmer searching for a particular semantic construct in one 

implementation will usually be able to find its counterpart in another with relative ease. 

4 PostgreSQL (available at http://www.postgresql.org) is an open source application that 

can be modified and distributed freely (in source or binary form) under the terms of the 

BSD License. While it is intended for Unix-like systems (Linux, Solaris, AIX,* etc.), 

PostgreSQL can be run under Microsoft Windows using the Cygwin tools (available at 

http://www.cygwin.com). See Geschwinde (2001) and Stinson (2001). 

5 The creation of unique values for use as primary keys is one of the areas in which 

RDBMSs differ the most, and most databases provide facilities for ensuring uniqueness 

that amount to extensions to the SQL-92 standard. Such is the case with the PostgreSQL 

sequence construct and with, for example, MySQL's use of the (somewhat less flexible) 

AUTO_INCREMENT keyword. 

6 In PostgreSQL, the command for creating a database is simply createdb [database 

name]. The interactive command interpreter can be launched with pgsql [data base name]

. While it is possible to build up a schema by entering commands one line at a time into 

the interpreter, the more common method is to use the interpreter's built-in command for 

importing a file containing SQL commands. 

7 All the major RDBMSs provide some level of built-in transaction management to 

prevent concur rent users from executing incompatible commands. 

8 DBI-style modules exist for all the major scripting languages, including Python, Ruby, 

and Tel. 

9 Most of the major scripting languages have modules (usually intended to be used with 

the Apache web server) that allow the target interpreter to be used in the same manner. 

10 PHP does provide facilities for moving the bulk of the programming logic into 

separate libraries on the server – a facility that can at least help to minimize the effect of 

embedding code in web pages. 

11 The most famous hierarchical database system was undoubtedly IMS (Information 

Management System). This highly successful product, developed jointly by IBM and 

North American Rockwell in the late 1960s, was the dominant DBMS for commercial 

accounting and inventory for many years (Elmasri and Navanthe 1994). Its design – and 

even more so, the design of its query language, DL/1 – had a substantial influence on the 

development of later systems. 


Bowman, J. S., et al. (1999). The Practical SQL Handbook. Reading, MA: Addison- 

Wesley. 

Codd, E. F. (1970). A Relational Model of Data for Large Shared Data Banks. 

Communications of the Association for Computing Machinery 13: 377–87. 

Date, C. J. (2001). The Database Relational Model: A Retrospective Review and 

Analysis. Reading: Addison-Wesley. 

Elmasri, R. and S. Navanthe (1994). Fundamentals of Database Systems. Redwood City: 

Benjamin/Cummings. 

Geschwinde, E. (2001). PostgreSQL Developer's Handbook. Indianapolis: SAMS. 

Halpin, T. (2001). Information Modeling and Relational Databases: From Conceptual 

Analysis to Logical Design. San Francisco: Morgan-Kaufmann. 

Hunter, L. (1990). Fact - Information - Data - Knowledge: Databases as a Way of 

Organizing Knowledge. Literary and Linguistic Computing 5: 49–57.

Postgresql. Vers. 7.2.3. Accessed April 22, 2004. At http://www.postgresql.org. 

Stinson, B. (2001). PostgreSQL Essential Reference. Indianapolis: New Riders. 

16. 

Marking Texts of Many Dimensions 

Jerome McGann 

Bring out number weight & measure in a year of death 

William Blake 

A sign is something by knowing which we know something more. 

C. S. Peirce 

Introduction: What is Text? 

Although "text" has been a "Keyword" in clerical and even popular discourse for more 

than fifty years, it did not find a place in Raymond Williams's important (1976) book 

Keywords. This strange omission may perhaps be explained by the word's cultural 

ubiquity and power. In that lexicon of modernity Williams called the "Vocabulary of 

Culture and Society", "text" has been the "one word to rule them all." Indeed, the word 

"text" became so shape-shifting and meaning-malleable that we should probably label it 

with Tolkien's full rubrication: "text" has been, and still is, the "one word to rule them all 

and in the darkness bind them." 

We want to keep in mind that general context when we address the issues of digitized 

texts, text markup, and electronic editing, which are our specialized concerns here. As we 

lay foundations for translating our inherited archive of cultural materials, including vast 

corpora of paper-based materials, into digital depositories and forms, we are called to a 

clarity of thought about textuality that most people, even most scholars, rarely undertake. 

Consider the phrase "marked text", for instance. How many recognize it as a redundancy? 

All text is marked text, as you may see by reflecting on the very text you are now 

reading. As you follow this conceptual exposition, watch the physical embodiments that 

shape the ideas and the process of thought. Do you see the typeface, do you recognize it? 

Does it mean anything to you, and if not, why not? Now scan away (as you keep reading) 

and take a quick measure of the general page layout: the font sizes, the characters per 

line, the lines per page, the leading, the headers, footers, margins. And there is so much 

more to be seen, registered, understood simply at the documentary level of your reading: 

paper, ink, book design, or the markup that controls not the documentary status of the text 

but its linguistic status. What would you be seeing and reading if I were addressing you

in Chinese, Arabic, Hebrew – even Spanish or German? What would you be seeing and 

reading if this text had been printed, like Shakespeare's sonnets, in 1609? 

We all know the ideal reader of these kinds of traditional documents. She is an actual 

person, like the texts this person reads and studies. He writes about her readings and 

studies under different names, including Randall McLeod, Randy Clod, Random Cloud, 

etc. She is the Dupin of the textual mysteries of our exquisite and sophisticated 

bibliographical age. 

Most important to realize, for this book's present purposes, is that digital markup schemes 

do not easily – perhaps do not even naturally - map to the markup that pervades paperbased 

texts. Certainly this is the case for every kind of electronic markup currently in use: 

from simple ASCII, to any inline SGML derivative, to the recent approaches of standoff 

markup (see Berrie website; Thompson and McKelvie 1997). The symptoms of this 

discrepancy are exemplified in the AI (Artificial Intelligence) community's struggles to 

simulate the complex processes of natural language and communicative exchange. 

Stymied of success in achieving that goal, these efforts have nonetheless been singularly 

fruitful for giving us a clearer view of the richness and flexibility of traditional textual 

machineries. 

How, then, are traditional texts marked? If we could give an exhaustive answer to that 

question we would be able to simulate them in digital forms. We cannot complete an 

answer for two related reasons: first, the answer would have to be framed from within the 

discourse field of textuality itself; and second, that framework is dynamic, a continually 

emerging function of its own operations, including its explicitly self-reflexive operations. 

This is not to say that markup and theories of markup must be "subjective." (It is also not 

to say – see below – that they must not be subjective.) It is to say that they are and must 

be social, historical, and dialectical, and that some forms have greater range and power 

than others, and that some are useful exactly because they seek to limit and restrict their 

range for certain special purposes. 

Autopoietic Systems and Co-dependency 

Describing the problems of electronic texts in her book Electronic Texts in the 

Humanities, Susan Hockey laconically observes that "There is no obvious unit of 

language" (2000: 20). Hockey is reflecting critically on the ordinary assumption that this 

unit is the word. Language scholars know better. Words can be usefully broken down into 

more primitive parts and therefore understood as constructs of a second or even higher 

order. The view is not unlike the one continually encountered by physicists who search 

out basic units of matter. Our analytic tradition inclines us to understand that forms of all 

kinds are "built up" from "smaller" and more primitive units, and hence to take the selfidentity 

and integrity of these parts, and the whole that they comprise, for objective 

reality. 

Hockey glances at this problem of the text-unit in order to clarify the difficulties of 

creating electronic texts. To achieve that, we instruct the computer to identify (the) basic

elements of natural language text and we try to ensure that the identification has no 

ambiguities. In natural language, however, the basic unit – indeed, all divisioning of any 

kind – is only procedurally determinate. The units are arbitrary. More, the arbitrary units 

themselves can have no absolute self-identity. Natural language is rife with redundancy 

and ambiguity at every unit and level and throughout its operating relations. A long 

history of analytic procedures has evolved certain sets of best practices in the study of 

language and communicative action, but even in a short run, terms and relations of 

analysis have changed. 

Print and manuscript technology represent efforts to mark natural language so that it can 

be preserved and transmitted. It is a technology that constrains the shapeshiftings of 

language, which is itself a special-purpose system for coding human communication. 

Exactly the same can be said of electronic encoding systems. In each case constraints are 

installed in order to facilitate operations that would otherwise be difficult or impossible. 

In the case of a system like the Text Encoding Initiative (TEI), the system is designed to 

"disambiguate" entirely the materials to be encoded. 

The output of TEI's markup constraints differs radically from the output generated by the 

constraints of manuscript and print technology. Whereas redundancy and ambiguity are 

expelled from TEI, they are preserved – are marked - in manuscript and print. While print 

and manuscript markups don't "copy" the redundancies of natural language, they do 

construct systems that are sufficiently robust to develop and generate equivalent types of 

redundancy. This capacity is what makes manuscript and print encoding systems so much 

more resourceful than any electronic encoding systems currently in use. ("Natural 

language" is the most complex and powerful reflexive coding system that we know of.) 1 

Like biological forms and all living systems, not least of all language itself, print and 

manuscript encoding systems are organized under a horizon of co-dependent relations. 

That is to say, print technology – I will henceforth use that term as shorthand for both 

print and manuscript technologies – is a system that codes (or simulates) what are known 

as autopoietic systems. These are classically described in the following terms: 

If one says that there is a machine M in which there is a feedback loop through the 

environment so that the effects of its output affect its input, one is in fact talking about a 

larger machine M 1 which includes the environment and the feedback loop in its defining 

organization. 

(Maturana and Varela 1980: 78) 

Such a system constitutes a closed topological space that "continuously generates and 

specifies its own organization through its operation as a system of production of its own 

components, and does this in an endless turnover of components" (Maturana and Varela 

1980: 79). Autopoietic systems are thus distinguished from allopoietic systems, which are 

Cartesian and which "have as the product of their functioning something different from 

themselves" (1980: 80).

In this context, all coding systems appear to occupy a peculiar position. Because "coding 

… represents the interactions of [an] observer" with a given system, the mapping stands 

apart from "the observed domain" (Maturana and Varela 1980: 135). Coding is a function 

of "the space of human design" operations, or what is classically called "hetero-poietic" 

space. Positioned thus, coding and markup appear allopoietic. 

As machines of simulation, however, coding and markup (print or electronic) are not like 

most allopoietic systems (cars, flashlights, a road network, economics). Coding functions 

emerge as code only within an autopoietic system that has evolved those functions as 

essential to the maintenance of its life (its dynamic operations). Language and print 

technology (and electronic technology) are second- and third-order autopoietic systems – 

what McLuhan famously, expressively, if also somewhat mislead-ingly, called 

"extensions of man." Coding mechanisms – proteins, print technology – are generative 

components of the topological space they serve to maintain. They are folded within the 

autopoietic system like membranes in living organisms, where distinct components 

realize and execute their extensions of themselves. 

This general frame of reference is what makes Maturana and Varela equate the "origin" 

of such systems with their "constitution" (1980: 95). This equation means that codependency 

pervades an autopoietic structure of relations. All components of the system 

arise (so to speak) simultaneously and they perform integrated functions. The system's 

life is a morphogenetic passage characterized by various dynamic mutations and 

transformations of the local system components. The purpose or goal of these processes 

is autopoietic – self-maintenance through self-transformation – and their basic element is 

not a system component but the relation (co-dependence) that holds the mutating 

components in changing states of dynamic stability. The states generate measurable codependency 

functions both in their periods (or basins) of stability and in their unique 

moments of catastrophic change. 

Marking the Text: A Necessary Distinction 

At the 2002 Extreme Markup Conference, Michael Sperberg-McQueen offered these 

observations on the problem of overlapping structures for SGML-based markup systems. 

It is an interesting problem because it is the biggest problem remaining in the residue. If 

we have a set of quantitative observations, and we try to fit a line to them, it is good 

practice to look systematically at the difference between the values predicted by our 

equation (our theory) and the values actually observed; the set of these differences is the 

residue …. In the context of SGML and XML, overlap is a residual problem. 2 

But in any context other than SGML and XML, this formulation is a play of wit, a kind 

of joke – as if one were now to say that the statistical deviations produced by Newtonian 

mathematical calculations left a "residue" of "interesting" matters to be cleared up by 

further, deeper calculations. But those matters are not residual, they are the hem of a 

quantum garment.

My own comparison is itself a kind of joke, of course, for an SGML model of the world 

of textualities pales in comprehensiveness before the Newtonian model of the physical 

world. But the outrageousness of the comparison in each case helps to clarify the 

situation. No autopoietic process or form can be simulated under the horizon of a 

structural model like SGML, not even topic maps. We see this very clearly when we 

observe the inability of a derivative model like TEI to render the forms and functions of 

traditional textual documents. The latter, which deploy markup codes themselves, supply 

us with simulations of language as well as of many other kinds of semeiotic processes, as 

Peirce called them. Textualized documents restrict and modify, for various kinds of 

reflexive purposes, the larger semeiotic field in which they participate. Nonetheless, the 

procedural constraints that traditional textualities lay upon the larger semeiotic field that 

they model and simulate are far more pragmatic, in a full Peircean sense, than the 

electronic models that we are currently deploying. 

Understanding how traditional textual devices function is especially important now when 

we are trying to imagine how to optimize our new digital tools. Manuscript and print 

technologies – graphical design in general – provide arresting models for information 

technology tools, especially in the context of traditional humanities research and 

education needs. To that end we may usefully begin by making an elementary distinction 

between the archiving and the simulating functions of textual (and, in general, semeiotic) 

systems. Like gene codes, traditional textualities possess the following as one of their 

essential characteristics: that as part of their simulation and generative processes, they 

make (of) themselves a record of those processes. Simulating and record keeping, which 

are co-dependent features of any autopoietic or semeiotic system, can be distinguished 

for various reasons and purposes. A library processes traditional texts by treating them 

strictly as records. It saves things and makes them accessible. A poem, by contrast, 

processes textual records as a field of dynamic simulations. The one is a machine of 

memory and information, the other a machine of creation and reflection. Each may be 

taken as an index of a polarity that characterizes all semeoitic or autopoietic systems. 

Most texts – for instance, this chapter you are reading now – are fields that draw upon the 

influence of both of those polarities. 

The power of traditional textualities lies exactly in their ability to integrate those different 

functions within the same set of coding elements and procedures. 

SGML and its derivatives are largely, if not strictly, coding systems for storing and 

accessing records. They possess as well certain analytic functions that are based in the 

premise that text is an "ordered hierarchy of context objects." This conception of 

textuality is plainly non-comprehensive. Indeed, its specialized understanding of "text" 

reflects the pragmatic goal of such a markup code: to store objects (in the case of TEI, 

textual objects) so that they can be quickly accessed and searched for their informational 

content – or more strictly, for certain parts of that informational content (the parts that fall 

into a hierarchical order modeled on a linguistic analysis of the structure of a book). 

These limitations of electronic markup codes are not to be lamented, but for humanist 

scholars they are to be clearly understood. A markup code like TEI creates a record of a

traditional text in a certain form. Especially important to see is that, unlike the textual 

fields it was designed to mark up, TEI is an allopoietic system. Its elements are 

unambiguously delimited and identified a priori, its structure of relations is precisely 

fixed, it is non-dynamical, and it is focused on objects that stand apart from itself. Indeed, 

it defines what it marks not only as objective, but as objective in exactly the 

unambiguous terms of the system's a priori categories. This kind of machinery will 

therefore serve only certain, very specific, purposes. The autopoietic operations of textual 

fields – operations especially pertinent to the texts that interest humanities scholars – lie 

completely outside the range of an order like the TEI. 

For certain archival purposes, then, structured markup will serve. It does not unduly 

interfere with, or forbid implementing, some of the searching and linking capacities that 

make digital technology so useful for different types of comparative analysis. Its strict 

formality is abstract enough to permit implementation within higher-order formalizations. 

In these respects it has greater flexibility than a stand-off approach to text markup, which 

is more difficult to integrate into a dispersed online network of different kinds of 

materials. All that having been recognized and said, however, these allopoietic textprocess 

ing systems cannot access or display the autopoietic character of textual fields. 

Digital tools have yet to develop models for displaying and replicating the self-reflexive 

operations of bibliographical tools, which alone are operations for thinking and 

communicating – which is to say, for transforming data into knowledge. 

We have to design and build digital environments for those purposes. A measure of their 

capacity and realization will be whether they can integrate data-function mechanisms like 

TEI into their higher-order operations. To achieve that will entail, I believe, the 

deployment of dynamic, topological models for mapping the space of digital operations. 

But these models will have to be reconceived, as one can see by reflecting on a remark 

about textual interpretation that Stanley Fish liked to make years ago. He would point out 

that he was able to treat even the simplest text – road signage, for example – as a poem 

and thus develop from his own "response" and commentary its autopoietic potential. The 

remark underscores a basic and almost entirely neglected (undertheorized) feature of 

discourse fields: that to "read" them – to read "in" them at any point – one must regard 

what we call "the text" and "the reader" as co-dependent agents in the field. You can't 

have one without the other. 

Fish's observation, therefore, while true, signals a widespread theoretical and 

methodological weakness in our conceptions of textuality, traditional or otherwise. This 

approach figures "text" as a heuristic abstraction drawn from the larger field of discourse. 

The word "text" is used in various ways by different people – Barthes's understanding is 

not the same as a TEI understanding – but in any case the term frames attention on the 

linguistic dimension of a discourse field. Books and literary works, however, organize 

themselves along multiple dimensions of which the linguistic is only one. 

Modeling digital simulations of a discourse field requires that a formal set of dimensions 

be specified for the field. This is what TEI provides a priori, though the provision, as we 

know, is minimal. Our received scholarly traditions have in fact passed down to us an

understanding of such fields that is both far more complex and reasonably stable. 

Discourse fields, our textual condition, regularly get mapped along six dimensions (see 

below, and Appendix B). Most important of all in the present context, however, are the 

implications of cognizing a discourse field as autopoietic. In that case the field 

measurements will be taken by "observers" positioned within the field itself. That 

intramural location of the field interpreter is in truth a logical consequence of the codependent 

character of the field and its components. "Interpretation" is not undertaken 

from a position outside the field; it is an essential part of a field's emergence and of any 

state that its emergence might assume. 

This matter is crucial to understand when we are reaching for an adequate formalizing 

process for textual events like poetry or other types of orderly but discontinuous 

phenomena. Rene Thorn explains very clearly why topological models are preferable to 

linear ones in dynamic systems: 

it must not be thought that a linear structure is necessary for storing or transmitting 

information (or, more precisely, significance); it is possible that a language, a semantic 

model, consisting of topological forms could have considerable advantages from the 

point of view of deduction, over the linear language that we use, although this idea is 

unfamiliar to us. Topological forms lend themselves to a much richer range of 

combinations…than the mere juxtaposition of two linear sequences. 

(Thorn 1975: 145) 

These comments distinctly recall Peirce's exploration of existential graphs as sites of 

logical thinking. But Thorn's presentation of topological models does not conceive field 

spaces that are autopoietic, which seems to have been Peirce's view. Although Thorn's 

approach generally eschews practical considerations in favor of theoretical clarity, his 

models assume that they will operate on data carried into the system from some external 

source. If Thorn's "data" come into his studies in a theoretical form, then, they have been 

theorized in traditional empirical terms. The topological model of a storm may therefore 

be taken either as the description of the storm and/or a prediction of its future behavior. 

But when a model's data are taken to arise co-dependently with all the other components 

of its system, a very different "result" ensues. Imagined as applied to textual autopoiesis, 

a topological approach carries itself past an analytic description or prediction over to a 

form of demonstration or enactment. 

The view taken here is that no textual field can exist as such without "including" in itself 

the reading or measurement of the field, which specifies the field's dataset from within. 

The composition of a poem is the work's first reading, which in that event makes a call 

upon others. An extrinsic analysis designed to specify or locate a poetic field's selfreflexiveness 

commonly begins from the vantage of the rhetorical or the social dimension 

of the text, where the field's human agencies (efficient causes) are most apparent. The 

past century's fascination with structuralist approaches to cultural phenomena produced, 

as we know, a host of analytic procedures that chose to begin from a consideration of

formal causation, and hence from either a linguistic or a semiotic vantage. Both 

procedures are analytic conventions based in empirical models. 

Traditional textuality provides us with autopoietic models that have been engineered as 

effective analytic tools. The codex is the greatest and most famous of these. Our problem 

is imagining ways to recede them for digital space. To do that we have to conceive 

formal models for autopoietic processes that can be written as computer software 

programs. 

Field Autopoiesis: from IVANHOE to 'Patacriticism 

Let's recapitulate the differences between book markup and TEI markup. TEI defines 

itself as a two-dimensional generative space mapped as (1) a set of defined "content 

objects" (2) organized within a nested tree structure. The formality is clearly derived 

from an elementary structuralist model of language (a vocabulary + a syntax, or a 

semantic + a syntagmatic dimension). In the SGML/TEI extrusion, both dimensions are 

fixed and their relation to each other is defined as arbitrary rather than co-dependent. The 

output of such a system is thus necessarily symmetrical with the input (cf. Curie's 

principle of causes and effects). Input and output in a field of traditional textuality works 

differently. Even in quite restricted views, as we know, the operations of natural language 

and communicative exchange generate incommensurable effects. The operations exhibit 

behavior that topolo-gists track as bifurcation or even generalized catastrophe, whereby 

an initial set of structural stabilities produces morphogenetic behaviors and conditions 

that are unpredictable. 3 This essential feature of "natural language" – which is to say, of 

the discourse fields of communicative exchange – is what makes it so powerful, on one 

hand, and so difficult to model and formalize, on the other. 

In these circumstances, models like TEI commend themselves to us because they can be 

classically quantified for empirical – numerable – results. But as Thorn observed long 

ago, there is no such thing as "a quantitative theory of catastrophes of a dynamical 

system" like natural language. To achieve such a theory, he went on to say, "it would be 

necessary to have a good theory of integration on function spaces" (Thorn 1975: 321), 

something that Thorn could not conceive. 

That limitation of qualitative mathematical models did not prevent Thorn from 

vigorously recommending their study and exploration. He particularly criticized the 

widespread scientific habit of "tak[ing] the main divisions of science, the[ir] taxonomy… 

as given a priori" rather than trying to re-theorize taxonomies as such (1975: 322). In this 

frame of reference we can see (1) that textualization in print technology is a qualitative 

(rather than a taxonomic) function of natural language, and (2) that textualization 

integrates function spaces through demonstrations and enactments rather than 

descriptions. This crucial understanding – that print textuality is not language but an 

operational (praxis-based) theory of language – has stared us in the face for a long time, 

but seeing we have not seen. It has taken the emergence of electronic textualities, and in 

particular operational theories of natural language like TEI, to expose the deeper truth 

about print and manuscript texts. SGML and its derivatives freeze (rather than integrate)

the function spaces of discourse fields by reducing the field components to abstract forms 

– what Coleridge called "fixities and definites." This approach will serve when the object 

is to mark textual fields for storage and access. 

Integration of dynamic functions will not emerge through such abstract reductions, 

however. To develop an effective model of an autopoietic system requires an analysis 

that is built and executed "in the same spirit that the author writ." That formulation by 

Alexander Pope expresses, in an older dialect, what we have called in this century "the 

uncertainty principle", or the co-dependent relation between measurements and 

phenomena. An agent defines and interprets a system from within the system itself – at 

what Dante Gabriel Rossetti called "an inner standing point." What we call "scientific 

objectivity" is in one sense a mathematical function; in another, it is a useful method for 

controlling variables. We use it when we study texts as if they were objective things 

rather than dynamic autopoietic fields. 

Traditional textual conditions facilitate textual study at an inner standing point because 

all the activities can be carried out – can be represented – in the same field space, 

typically, in a bibliographical field. Subject and object meet and interact in the same 

dimensional space – a situation that gets reified for us when we read books or write about 

them. Digital operations, however, introduce a new and more abstract space of relations 

into the study-field of textuality. This abstract space brings the possibility of new and in 

certain respects greater analytic power to the study of traditional texts. On the downside, 

however, digitization – at least to date, and typically – situates the critical agent outside 

the field to be mapped and re-displayed. Or – to put this crucial point more precisely 

(since no measurement has anything more than a relative condition of objectivity) – 

digitization situates the critical agent within levels of the textual field's dimensionalities 

that are difficult to formalize bibliographically. 

To exploit the power of those new formalizations, a digital environment has to expose its 

subjective status and operation. (Like all scientific formalities, digital procedures are 

"objective" only in relative terms.) In the present case – the digital marking of textual 

fields – this means that we will want to build tools that foreground the subjectivity of any 

measurements that are taken and displayed. Only in this way will the autopoietic 

character of the textual field be accurately realized. The great gain that comes with such a 

tool is the ability to specify – to measure, display, and eventually to compute and 

transform – an autopoietic structure at what would be, in effect, quantum levels. 

A series of related projects to develop such tools is under way at University of Virginia's 

Speculative Computing Laboratory (Speclab). The first of these, IVANHOE, is an online 

gamespace being built for the imaginative reconstruction of traditional texts and 

discourse fields. Players enter these works through a digital display space that encourages 

players to alter and transform the textual field. The game rules require that 

transformations be made as part of a discourse field that emerges dynamically through 

the changes made to a specified initial set of materials. 4

As the IVANHOE project was going forward, a second, related project called Time 

Modelling was being taken up by Bethany Nowviskie and Johanna Drucker. The project 

was begun "to bring visualization and interface design into the early content modeling 

phase" of projects like IVANHOE, which pursue interpretation through transformational 

and even deformative interactions with the primary data. IVANHOE's computer is 

designed to store the game players' performative interpretational moves and then produce 

algorithmically generated analyses of the moves after the fact. The chief critical function 

thus emerges after-the-fact, in a set of human reflections on the differential patterns that 

the computerized analyses expose. In the Time Modelling device, however, the 

performative and the critical actions are much more closely integrated because the human 

is actively involved in a deliberated set of digital transformations. The Time Modelling 

device gives users a set of design functions for reconstructing a given lineated timeline of 

events in terms that are subjective and hypothetical. The specified field of event-related 

data is brought forward for transformation through editing and display mechanisms that 

emphasize the malleability of the initial set of field relations. The project stands, 

conceptually, somewhere between design programs (with their sets of tools for making 

things) and complex websites like The Rossetti Archive (with their hypertextual datasets 

organized for on-the-fly search and analysis). It is a set of editing and display tools that 

allows users to design their own hypothetical (re)formulations of a given dataset. 

The frankly experimental character of Time Modelling's data (re)constructions has led to 

an important reimagining of the original IVANHOE project. From the outset of that 

project we intended to situate the "interpreter" within the discourse field that was the 

subject of interpretive transformation. Our initial conception was toward what we called 

"Ultimate IVANHOE", that is, toward a playspace that would be controlled by emergent 

consciousness software. With the computer an active agent in an IVANHOE session, 

players could measure and compare their own understandings of their actions against a 

set of computer generated views. This prospect for IVANHOE's development remains, 

but the example of Time Modelling exposed another way to situate the human interpreter 

at an inner standing point of an autopoietic system. 

If'Pataphysics is, in the words of its originator, "the science of exceptions", the project 

here is to reconceive IVANHOE under the rubric of'Patacriticism, or the theory of 

subjective interpretation. The theory is implemented through what is here called the 

dementianal method, which is a procedure for marking the autopoietic features of textual 

fields. The method works on the assumption that such features characterize what topologists 

call a field of general catastrophe. The dementianal method marks the dynamic 

changes in autopoietic fields much as Thorn's topological models allow one to map forms 

of catastrophic behavior. The'Patacritical model differs from Thorn's models because the 

measurements of the autopoietic field's behaviors are generated from within the field 

itself, which only emerges as a field through the action of the person interpreting – that is 

to say, marking and displaying – the field's elements and sets of relations. The field arises 

co-dependently with the acts that mark and measure it. In this respect we wish to 

characterize its structure as dementianal rather than dimensional.

As the device is presently conceived, readers engage autopoietic fields along three 

behavior dementians: transaction, connection, resonance. A common transaction of a 

page space moves diagonally down the page, with regular deviations for horizontal line 

transactions left to right margin, from the top or upper left to the bottom at lower right. 

Readers regularly violate that pattern in indefinite numbers of ways, often being called to 

deviance by how the field appears marked by earlier agencies. Connections assume, in 

the same way, multiple forms. Indeed, the primal act of autopoietic connection is the 

identification or location of a textual element to be "read." In this sense, the transaction 

of an autopoietic field is a function of the marking of connections of various kinds, on 

one hand, and of resonances on the other. Resonances are signals that call attention to a 

textual element as having a field value – a potential for connectivity – that appears and 

appears unrealized. 

Note that each of these behavior dementians exhibit co-dependent relations. The field is 

transacted as connections and resonances are marked; the connections and resonances are 

continually emergent functions of each other; and the marking of dementians 

immediately reorders the space of the field, which itself keeps re-emerging under the sign 

of the marked alteration of the dynamic fieldspace and its various elements. 

These behavioral dementians locate an autopoietic syntax, which is based in an 

elementary act or agenting event: G. Spencer Brown's "law of calling", which declares 

that a distinction can be made. From that law comes the possibility that elements of 

identities can be defined. They emerge with the co-dependent emergence of the textual 

field's control dimensions, which are the field's autopoietic semantics. (For further 

discussion of these matters see below, Appendix A and Appendix B.) 

Writing and Reading in Autopoietic Fields 

This'Patacritical approach to textual dementians is a meta-theory of textual fields, a 

pragmatistic conception of how to expose discontinuous textual behaviors ("natural 

language" so called, or what Habermas (1984) has better called "communicative action"). 

Integration of the dynamic functions begins not by abstracting the theory away from a 

target object – that is, the method of a taxonomic methodology – but by integrating the 

meta-theoretical functions within the discourse space itself. 

Informational discourse fields function well precisely by working to limit redundancy 

and concurrent textual relations. Because poetry – or imaginative textuality broadly 

conceived – postulates much greater freedom of expressive exchange, it exhibits a special 

attraction for anyone wishing to study the dynamics of textuality. Aristotle's studies of 

semiotic systems preserve their foundational character because they direct their attention 

to autopoietic rather than allopoietic discourse fields. His studies pursue a taxonomy for 

the dynamic process of making and exchanging (remaking) simulations. 

Plato's dialogues, by contrast, situate – or, more precisely, generate – their critical 

reflections at a standing point inside the textualities they are themselves unfolding. In this 

respect they have much in common with Wittgenstein's critical colloquies in the

Philosophical Investigations or with Montaigne's Essais. But the dynamic play of even 

these textual fields remain, from the point of view of their readers, exemplary exercises. 

This situation prevails in all modes of critical reflection which assume to preserve the 

integrity and self-identity of the textual fields they study. Two forms of critical reflection 

regularly violate the sanctity of such self-contained textual spaces: translation and 

editing. The idea that an object of criticism like a textual field is an object can be 

maintained either as an heuristic procedure or as an ontological illusion. Consequently, 

acts of translation and editing are especially useful forms of critical reflection because 

they so clearly invade and change their subjects in material ways. To undertake either, 

you can scarcely not realize the performative – even the deformative - character of your 

critical agency. 

At this point let me exemplify the general markup model for autopoietic textualities. This 

comes as the following hypothetical passage through an early poem by Robert Creeley, 

"The Innocence." Because imaginative textuality is, in this view, an exemplary kind of 

autopoietic process, any poetical work would do for a narrative demonstration. I choose 

"The Innocence" because it illustrates what Creeley and others called "field poetics." As 

such, it is especially apt for clarifying the conception of the autopoietic model of 

textuality being offered here. "Composition by field" poetics has been much discussed, 

but for present purposes it suffices to say that it conceives poetry as a self-unfolding 

discourse. "The poem" is the "field" of action and energy generated in the poetic 

transaction of the field that the poem itself exhibits. "Composition by field", whose 

theoretical foundations may be usefully studied through Charles Olson's engagements 

with contemporary philosophy and science, comprised both a method for understanding 

(rethinking) the entire inheritance of poetry, and a program for contemporary and future 

poetic discourse (its writing and its reading). 

The text chosen is taken from Donald Allen's famous anthology (first published in 1960) 

The New American Poetry in its 1999 University of California Press reprinting. 

The Innocence 

Looking to the sea, it is a line 

of unbroken mountains. 

It is the sky. 

It is the ground. There 

we live, on it. 

It is a mist 

now tangent to another 

quiet. Here the leaves 

come, there 

is the rock in evidence

or evidence. 

What I come to do 

is partial, partially kept 

Before tracing a model for this poetic field we want to bear two matters in mind. First, 

the field we are transacting is localized in relation to this documentary instance of "the 

text." One of the most persistent and misleading procedures in traditional hermeneutics is 

to take the object of study as something not only abstract and disembodied, but as 

something lying outside the field space – itself specific and material – of the act of 

critical representation. Second, the sequence of readings (below) consciously assumes a 

set of previous readings whereby certain elementary forms of order – by no means 

insignificant forms – have been integrated into the respective textual dementians. All 

such forms are extrusions from the elementary semiotic move, which is Spencer Brown's 

basic law of form: that a distinction can be drawn (as a dementian, or within and between 

dementians). Thus the readings below assume that each dementian is oriented to a set of 

established formal objects which get called and then crossed (transformed) in the 

transaction of the field. 

That said, let me transact the poetic field through the initial textual model supplied above. 

A First Reading: 

I mark the following elements in the first line group (and in that act I mark as well the 

presence of (a) lines and (b) line groups): "Looking" as a dangling participle; "it" (line 1) 

as ambiguously pronominal; "line" as a word play referencing (first) this line of verse I 

am transacting, and (second) a landscape of "unbroken mountains" (to be marked as such 

only with the marking of the final line in the group). All of these are defined (connected 

to the fieldspace) as textual elements with marked resonances (anticipations and clear if 

inchoate recollections) as well as several manifest, second-order connections (e.g., "sea", 

"line", and "mountains" as objects in a landscape). 

Line group 2 emerges to connect a network of "it" words as well as to settle the 

dominance of a linguistic gravity field centered in the initially marked "landscape" (a 

linguistic dementian subdomain). As line group 3 continues to elaborate the "landscape 

field", several distinctly new elements emerge and get marked. They center in the words 

"tangent", "quiet", "evidence", the notable enjambment at the end of the line group, and 

the deictics "Here and "there." The first four resonate by the differences they make with 

the previous elements I had defined in my transaction of the field. The deictics connect 

back to the second linguistic dementian subdomain (the self-reflexive set of textual 

elements marked in line 1 as the dangling participle and the final word "line"). The fourth 

and last line group is itself marked as strongly resonant in itself because of the emergence 

within it of the unique "I" and the startling repetitions ("evidence", "partial"/ "partially"). 

So the field transaction is marked geometrically as a complete and continuous passage 

from upper left to lower right and proceeding line by line left to right. That passage of the 

textspace marks out two control dementians, linguistic and graphical, as well as several

distinct basins of order within them. In the graphical dementian we see an array of 

marked words, lines, and line groups. In the linguistic dementian I have marked two 

distinct subdomains, one referential (the set of "landscape" semantics), one a subdomain 

of pure signifiers (proliferating from line 1 through the deictic markers "Here" and 

"there." 

A Second Reading: 

I mark the title as strongly resonant and I find myself scanning the poem rather than 

reading it linearly, and marking elements unnoticed in the first reading. I re-mark the 

array of "it" words and connect all of them to the title, marking thereby another linguistic 

subdomain. I mark as resonant the striking idea of "a mist / now tangent to another / 

quiet", and I mark a distinction in the linguistic subdomain (of "landscape") between 

different sensory aspects of a "landscape." I mark as resonant the equally striking final 

sentence and the phrase "the rock in evidence / or evidence." 

A Third Reading: 

This is a sequential transaction through the poem as in the first reading. It is largely 

devoted to marking connections between the various elements already marked with 

resonance values. The wordplay in "line" is marked as a strongly resonant locus of 

fieldspace connections across the several linguistic subdomains. This connective 

fieldspace is especially resonant as the connection between the words "line" and 

"tangent." I mark all of the previously marked textual elements as connected to each other 

in a broadly dispersed semiotic dementian because I am seeing that elements in different 

fieldspace dementians and domains (e.g., "mist" and "quiet") are connected to each other. 

A Fourth Reading: 

A sequential reading leads to marking the final sentence as a dramatic locus of a 

rhetorical dementian in the fieldspace. The construction of the textspace is "What I come 

to do." The emergence of this idea allows me to mark the poem as a deliberated 

sequential organization that exposes itself in certain telling (marked) moments and 

textual elements: "Looking", "line", "tangent", the deictic words, the previously 

unmarked "we" (line 5), the enjambment between the third and fourth line groups. In all 

these I mark a rhetorical organization tied most centrally to the phrase "What I come to 

do." I mark that these marks unfold as a relation that must be seen as sequenced: "I" in 

the present tense here is always the present tense in the linguistic dementian of this work. 

Marking the verb tense in that way immediately produces the first, remarkable emergence 

in this reading process of the work's social dementian. "I" comes to write this poem, 

which is marked thereby as an event in the world and as objective as any material thing 

(these material things, the "landscape" things, first marked in the linguistic dementian). In 

that rhetorical dementian I mark as well a key element of this work's social dementian 

first marked in the linguistic dementian: the relation between the "we" and the "I." The 

phrase "is partial, partially kept" is marked now as an element in the social dementian of 

the textspace – as if one were to say, interpretatively, that the "doing" of the poem is only

one event in a larger field that the poem is part of and points toward. My acts of marking 

the poem fall into both the local fieldspace and the larger discourse field marked by this 

local poetical field. And I further mark the social space by connecting the textspace to the 

book in which the text is printed – for that book (the polemic it made) marks this specific 

text in the strongest way. At this point the sixth dementian of the fieldspace begins to get 

marked, the material dementian. I mark three documentary features in particular: the 

placement of the text in the book, the organ of publication, the date of publication. I mark 

as well the fact that these material features of the work are, like the word "line", doublemeaninged 

(or double dementianed), having as well a clear placement in the work's social 

dementian as well. 

A Fifth Reading: 

I mark new elements in the six marked dementians that emerge in a widespread process 

of subdividing and proliferating. Elements defined in one dementian or subdomain get 

marked in another (for instance, "I" began in the rhetorical, reappeared in the social, and 

now gets marked in all the other dementians as well); unmarked textual features, like the 

letter "t", get marked as resonant; the shape of the textspace from word to line to word 

group is marked as a linked set of spare elements. These additional markings lead to 

other, previously unseen and unmarked relations and elements. The spare graphical 

dementian gets linked to the linguistic dementian ("The Innocence") and to the social and 

rhetorical dementians (the graphical spareness is only markable in relation to the 

absent/present discourse field in which this poetical work stands and declares its 

comparative allegiance. 

A Sixth Reading: 

This is a reading that poses significant theoretical and practical issues. Time-stamped two 

weeks after the previous readings, this reading was negotiated in my mind as I recalled 

the history of my readings of the poem. It is thus a reading to be digitally marked afterthe-fact. 

Focused on the final line group, it also marks the entirety of the autopoietic field. 

The reading marks the "I" as a figure in the social dementian, the poet (Creeley) who 

composed the poem. In that linking, however, I as reader become linked to the linguistic 

"I" that is also a social "I." This linkage gets enforced by marking a set of "partial" agents 

who "come to do" part of the continuous marking of the autopoietic field. (Creeley does 

what he does, I do what I do, and we both inhabit a space resonant with other, as yet 

unspecified, agents.) 

Conclusion 

What we theorize here and propose for a digital practice is a science of exceptions, a 

science of imaginary (subjective) solutions. The markup technology of the codex has 

evolved an exceedingly successful instrument for that purpose. Digital technology ought 

to be similarly developed. Organizing our received humanities materials as if they were 

simply information depositories, computer markup as currently imagined handicaps or 

even baffles altogether our moves to engage with the well-known dynamic functions of

textual works. An alternative approach to these matters through a formal reconception of 

textspace as topological offers distinct advantages. Because this space is autopoietic, 

however, it does not have what mathematicians would normally call dimensionality. As 

autopoietic, the model we propose establishes and measures its own dimensions autotelically, 

as part of its self-generative processes. Furthermore, space defined by pervasive 

co-dependencies means that any dimension specified for the system might be formally 

related to any other. This metamorphic capacity is what translates the concept of a 

dimension into the concept of a dementian. 

This model of text-processing is open-ended, discontinuous, and non-hierarchical. It 

takes place in a fieldspace that is exposed when it is mapped by a process of "reading." A 

digital processing program is to be imagined and built that allows one to mark and store 

these maps of the textual fields and then to study the ways they develop and unfold and 

how they compare with other textual mappings and transactions. Constructing textualities 

as field spaces of these kinds short-circuits a number of critical predilections that inhibit 

our received, commonsense wisdom about our textual condition. First of all, it escapes 

crippling interpretative dichotomies like text and reader, or textual "subjectivity" and 

"objectivity." Reader-response criticism, so-called, intervened in that space of problems 

but only succeeded in reifying even further the primary distinctions. In this view of the 

matter, however, one sees that the distinctions are purely heuristic. The "text" we "read" 

is, in this view, an autopoietic event with which we interact and to which we make our 

own contributions. Every textual event is an emergence imbedded in and comprising a set 

of complex histories, some of which we each partially realize when we participate in 

those textual histories. Interestingly, these histories, in this view, have to be grasped as 

fields of action rather than as linear unfoldings. The fields are topological, with various 

emergent and dynamic basins of order, some of them linear and hierarchical, others not. 

Appendix A: The 'Pataphysics of Text and Field 

Markup 

Texts and their field spaces are autopoietic scenes of co-dependent emergence. As such, 

their primal state is dynamic and has been best characterized by G. Spencer Brown's 

Laws of Form (1969), where "the form of distinction" – the act of making indications by 

drawing a distinction – is taken as "given" and primal (1). This means that the elementary 

law is not the law of identity but the law of non-identity (so that we must say that "a 

equals a if and only if a does not equal a)." Identities emerge as distinctions are drawn 

and redrawn, and the acts of drawing out distinctions emerge as co-dependent responses 

to the field identities that the form of distinction calls to attention. 

Spencer Brown supplies a formal demonstration of what Alfred Jarry called'pataphysics 

and that he and his OULIPian inheritors demonstrated in forms of traditional textual 

practice (i.e., in forms of "literature").'Pataphysics is a general theory of autopoietic 

systems (i.e., a general theory of what we traditionally call "imaginative literature"), and 

Laws of Form is a specifically 'patapbysical event because it clearly gives logical priority 

to the unique act and practice of its own theoretical thought. The fifth "Chant" of Lautrea-

mont's Les chants de Maldoror, Jarry's Gestes et opinions du docteur 

Faustroll,'patapbysicien, and all the descendants of those self-conscious works – Laura 

Riding's stories are the earliest English-language examples – are the "literary" equivalents 

of Spencer Brown's Laws of Form. 

In this view of any systematics, the taxonomy of a system is a derivation of what Peirce 

called an initial abduction. The abduction is an hypothesis of the total semeiotic integrity 

of the system. The hypothesis is tested and transformed (internally as well as externally) 

in a dialectical process – ultimately endless – of representation and reflection. 

Appendix B: Control Dementians for a 'Patacriticism of 

Textualities 

The transaction of textual fields proceeds by a series of moves (field behaviors) that 

proliferate from an elementary modal distinction between what have been specified 

(above) as connections and resonances, which are the elementary behavioral forms of the 

textual transaction. These modes correspond to what traditional grammarians define as 

an indicative and a subjunctive verbal mood. (In this view, interrogative and interjective 

moods are derivatives of these two primary categories.) Emerging co-dependently with 

these behavioral dementians is an elementary taxonomy of control dementians that are 

called into form and then internally elaborated. 

The history of textual studies has evolved a standard set of field formalities that may be 

usefully analyzed in six distinct parts. These correspond to an elemental set of 

dimensions for textual fields (or, in fields conceived as autopoietic systems, an elemental 

set of six dementians). These control dementians locate what grammarians designate as 

the semantics of a language. 

Let it be said here that these behavioral and control dementians, like their allopoietic 

dimensions, comprise a set of categories that recommend themselves through an evolved 

history of previous use. Other dimensions (and dementians) might be proposed or 

imagined. However, since the proposals being advanced here are all conceived within a 

pragmatistic frame of reference, the categories bring with them the strong authority of a 

habitual usefulness. 

The Linguistic Dimension/Dementian. This aspect of the textual condition has been the 

principal focus of attention in the West. It represents a high-order framework of 

conceptual markers or distinctions that unfold and multiply from an initial pair of 

categories, the semantic and the grammatical. The former is an elemental category, the 

latter is a relational one, and the two together epitomize the structure of co-dependency 

that pervades and in a sense defines all textual processes at every dimension. That is to 

say, neither marker or category has conceptual priority over the other, they generate 

meaning together in a co-dependent and dialectical process. However, to specify their codependence 

requires that one adopt a pragmatistic or performative approach such as we 

see in Maturana, Spencer Brown, and Peirce.

The Graphical/Auditional Dimension/Dementian. Some kind of graphical and/or auditional 

state of affairs is a prerequisite for any appearance or functional operation of a 

Linguistic Dimension, and that state must be formally constrained. In Western attempts to 

clarify language and textuality, these forms are defined in the systematic descriptors of 

morphology and phonology, which are co-dependent subcategories of the Linguistic 

Dimension. 

This Graphical/Auditional Dimension comprises the set of a text's codes of materiality 

(as opposed to the specific material state of a particular document). In print and 

manuscript states, the dimension includes various subsets of bibliographical codes and 

paratexts: typography, layout, book design, and the vehicular components of those forms. 

(If we are considering oral texts, the material assumes auditional forms, which can have 

visual components as well.) 

Documentary Dimension/Dementian. This comprises the physical incarnation – the "real 

presence", so to speak – of all the formal possibilities of the textual process. We 

recognize it as a bibliographical or palaeographical description of some specific object, or 

as a library or archival record of an object's historical passage (transmission history). 

Note that this dimension does not simply constitute some brute chemical or physical 

thing – what Coleridge referred to when he spoke as the "object as object", which he 

called "fixed and dead." Coleridge's "object as object" is a negative abstraction – that's to 

say, a certain formal conception of the documentary dimension that sets it apart (a priori) 

from any place in a study or interpretation of textuality. A document can and – in any 

comprehensive approach to textuality – should be maintained as an integral function of 

the textual process. 

A document is a particular object that incarnates and constrains a specific textual process. 

In terms of print and manuscript texts, it is a specific actualized state of the 

Graphical/Auditional Dimension. 

Semiotic Dimension/Dementian. This dimension defines the limit state of any text's 

formal possibilities. It postulates the idea of the complete integration of all the elements 

and dynamic relations in a field of discourse. In this dimension we thus cognize a textual 

process in holistic terms. It is a purely formal perspective, however, and as such stands as 

the mirrored antithesis of the document per se, whose integrity is realized as a 

phenomenal event. The document is the image of the hypothesis of total form; it appears 

at (or as) a closure of the dynamic process set in perpetual motion by the hypothesis at 

the outset. 

We register the semiotic dimension as a pervasiveness of patterned relations throughout 

the textual system – both within each part of the system and among the parts. The 

relations emerge in distinct types or modes: elements begin and end; they can be 

accumulated, partitioned, and replicated; they can be anchored somewhere, linked to 

other elements, and relayed through the system

The first of those late systems of analysis called by Herbert Simon "Sciences of the 

Artificial", the science of semiotics, labels itself as an heuristic mechanism. The 

pervasive order of a textual process's semiotic dimension thus emerges as a function of 

the formal categories, both system elements and system processes, that are consciously 

specified by the system's agents. Order is constructed from the systemic demand for 

order. As a result, the forms of order can be of any type – hierarchical or nonhierarchical, 

continuous or discontinuous. 

Rhetorical Dimension/Dementian. The dominant form of this dimension is genre, which 

is a second-order set of textual forms. Genre calls into play poems, mathematical proofs, 

novels, essays, speeches, dramas, and so forth. The function of this dimension is to 

establish forms of readerly attention – to select and arrange textual materials of every 

kind in order to focus the interest of the reader (audience, user, listener) and establish a 

ground for response. 

Readers and writers (speakers and listeners) are rhetorical functions. (Writers' first 

readers are themselves in their act of composition.) Bakhtin's celebrated studies of textual 

polyvalence and heteroglossia exemplify the operative presence of this textual dimension. 

Social Dimension/Dementian. This is the dimension of a textual production and of 

reception histories. It is the dimension of the object as subject: that is to say, of a 

determinate set of textual elements arrayed under names like "writer", "printer", 

"publisher", "reader", "audience", "user." It is the dimension that exposes the temporality 

function which is an inalienable feature of all the dimensions of the textual condition. 

The social dimension of textuality unfolds a schedule of the uses to which its works are 

put beyond what New Critics liked to call "the poem itself." It is the dimension in which 

the dynamic and non-self-identical character of textual works is most plainly disclosed. 

In most traditional theories of textuality, the social dimension is not considered an 

intrinsic textual feature or function. Framed under the sign "context", it is seen as the 

environment in which texts and documents stand. Until the recent emergence of more 

holistic views of environments – notably in the work of Donald McKenzie – this way of 

seeing textuality's social dimension forced severe restrictions on our ability to 

comprehend and study the dynamic character of textual processes. 

Acknowledgment 

"The Innocence" by Robert Creeley is reprinted from The Collected Poems of Robert 

Creeley, 1945–1975 with kind permission of the University of California Press and 

Robert Creeley. 

Note 

1 See Maturana and Varela (1992).

2 "What Matters?" (at: http://www.w3.org/People/cmsmcq/2002/whatmatters.html). 

3 As the terms in this sentence indicate, I am working in terms laid down thirty years ago 

by Rene Thorn in his classic study Structural Stability and Morphogenesis (1975). 

4 For essays describing IVANHOE see the Special issue of TEXT Technology devoted to 

it (vol. 12, no. 2, 2003). 


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Sontag, Susan, (ed.) (1982). A Barthes Reader. New York: Noonday Press. 

Sperberg-McQueen, C. M. (2002). What Matters? At 

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Sperberg-McQueen, C. M., Claus Huitfeldt, and Allen Renear (2000). Meaning and 

Interpretation of Markup. Markup Languages 2, 3: 215–34. 

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17. 

Text Encoding 

Allen H. Renear

Before they can be studied with the aid of machines, texts must be encoded in a machinereadable 

form. Methods for this transcription are called, generically, "text encoding 

schemes"; such schemes must provide mechanisms for representing the characters of the 

text and its logical and physical structure … ancillary information achieved by analysis or 

interpretation [may be also added] … 

Michael Sperberg-McQueen, Text Encoding and Enrichment. In The Humanities 

Computing Yearbook 1989–90, ed. Ian Lancashire (Oxford: Oxford University Press, 

1991) 

Introduction 

Text encoding holds a special place in humanities computing. It is not only of 

considerable practical importance and commonly used, but it has proven to be an exciting 

and theoretically productive area of analysis and research. Text encoding in the 

humanities has also produced a considerable amount of interesting debate – which can be 

taken as an index of both its practical importance and its theoretical significance. 

This chapter will provide a general orientation to some of the historical and theoretical 

context needed for understanding both contemporary text encoding practices and the 

various ongoing debates that surround those practices. We will be focusing for the most 

part, although not exclusively, on "markup", as markup-related techniques and systems 

not only dominate practical encoding activity, but are also at the center of most of the 

theoretical debates about text encoding. This chapter provides neither a survey of markup 

languages nor a tutorial introduction to the practice of markup. The reader new to 

SGML/XML text encoding should read this chapter of the Companion concurrently with 

the short (21-page) second chapter of the TEI Guidelines, "A Gentle Introduction to 

XML", online at http://www.tei-c.org/P4X/SG.html. The justly renowned "Gentle 

Introduction" remains the best brief presentation of SGML/XML text encoding and it 

provides a necessary complement of specific description to the background and theory 

being presented here. For a good general introduction to text encoding in the humanities, 

see Electronic Texts in the Humanities: Theory and Practice, by Susan Hockey (Hockey 

2001). 

In accordance with the general approach of this Companion, we understand text encoding 

in the "digital humanities" in a wide sense. Traditional humanities computing 

(particularly when related to literature and language) typically emphasized either 

analytical procedures on encoded textual material – such as, for instance, stylometric 

analysis to support authorship or seriation studies – or the publishing of important 

traditional genres of scholarship such as critical and variorum editions, indexes, 

concordances, catalogues, and dictionaries. But text encoding is no less important to 

digital humanities broadly conceived, in the sense which includes the creation and study 

of new cultural products, genres, and capabilities, such as those involving hypertext, 

multimedia, interactivity, and networking – cultural products that are often called "new 

media." In order to be presented using computers, such material must be encoded in 

machine-readable form. Although the presentation below does not take up hypertext or

"new media" topics directly, we believe the background presented nevertheless provides 

a useful background for text encoding in general, new media applications as well as 

traditional humanities computing and publishing. For more specific treatment of 

encoding issues, hypertext, multimedia, and other new media topics, see chapters 10, 28, 

29, and 30. For discussions of traditional humanities computing applications exploiting 

text encoding, see chapters 20, 21, 22, and 35. 

What follows then is intended as a background for understanding the text encoding as a 

representation system for textually based cultural objects of all kinds. 

Markup 

Introduction 

Markup, in the sense in which we are using the term here, may be characterized, at least 

provisionally, as information formally distinct from the character sequence of the digital 

transcription of a text, which serves to identify logical or physical features or to control 

later processing. In a typical unformatted view of a digital representation of a text such 

markup is visibly evident as the more or less unfamiliar expressions or codes that are 

intermixed with the familiar words of the natural-language writing system. The term 

markup comes, of course, from traditional publishing, where an editor marks up a 

manuscript by adding annotations or symbols on a paper copy of text indicating either 

directly (e.g., "center") or indirectly ("heading") how something is to look in print 

(Spring 1989); Chicago 1993. 

Many markup theorists have found it instructive to conceptualize markup as a very 

general phenomenon of human communication. Extending the notion of markup in 

straightforward and natural ways, one can easily use this concept to illuminate aspects of 

the general nature and history of writing systems and printing, particularly in the areas 

such as page layout, typography, and punctuation (Coombs et al. 1987). 

In addition, other fields and disciplines related to communication, such as rhetoric, 

bibliography, textual criticism, linguistics, discourse and conversation analysis, logic, and 

semiotics also seem to be rich sources of related findings and concepts that generalize our 

understanding of markup practices and make important connections between markup 

practices narrowly understood and other bodies of knowledge and technique. 

However, although such a broad perspective can be illuminating, the significance of 

markup for humanities computing is best approached initially by considering markup's 

origin and development in computer-based typesetting and early text processing. 

Although markup might arguably be considered part of any communication system, and 

of fundamental theoretical significance, it is with straightforward applications in digital 

text processing and typesetting in the 1960s, 1970s, and 1980s that the use of markup, in 

our sense, first becomes explicit and begins to undergo deliberate and self-conscious 

development (Goldfarb 1981; Spring 1989; SGML Users' Group 1990).

Emergence of descriptive markup 

The use of computers to compose text for typesetting and printing was common by the 

mid-1960s and the general process was more or less the same regardless of the specific 

technology. Typically, the first step was to create and store in a computer file a 

representation of the text to be printed. This representation consisted of both codes for the 

individual characters of the textual content and codes for formatting commands, the 

commands and the text being distinguished from each other by special characters or 

sequences of characters serving as "delimiters." The file would then be processed by a 

software application that acted on the formatting instructions to create data which could 

in turn be directly read and further processed by the phototypesetter or computer printer – 

creating formatted text as final output (Seybold 1977; Furuta et al. 1982). 

The 1960s, 1970s, and 1980s saw extensive development of these document markup 

systems as software designers worked to improve the efficiency and functionality of 

digital typesetting and text processing software (IBM 1967; Ossanna 1976; Lesk 1978; 

Goldfarb 1978; Reid 1978; Knuth 1979; Lamport 1985). 

One natural improvement on the approach just described was to replace the long strings 

of complex formatting codes with simpler abbreviations that could be automatically 

expanded into the formatting commands being abbreviated. The compositor would enter 

just the abbreviation instead of the entire string of commands. In many typesetting 

systems in the 1960s and 1970s these abbreviations were called "macros", a term drawn 

from assembly language programming where it referred to higher-level symbolic 

instructions which would be expanded, before program execution, into sequences of 

lower-level primitive instructions. 

In typesetting and text processing these macros had the obvious immediate advantage of 

easier and more reliable data entry. However, during their early use it wasn't always 

entirely clear whether a macro got its primary identity from the text component (e.g., a 

caption, extract, or heading) whose formatting it was controlling, or whether it was 

simply a short name for the combination of the specific formatting codes it was 

abbreviating, with no other meaning or identity. The distinction is subtle but important. If 

a macro is truly just an abbreviation of a string of formatting codes then it can be 

appropriately used wherever those formatting codes would be used. So if, for instance, 

figure captions and third-level headings happen to have the same design specifications, 

then the same macro could reasonably and appropriately be used for both. In such a case 

the macro, as a mere abbreviation, gets its entire meaning and identity from the 

formatting commands it abbreviates and it would be natural for the macro name to then 

simply indicate the appearance of the formatted text (e.g., ":SmallCenteredBold;"), or be 

an arbitrary expression (e.g., ":format!7;") rather than have a name that suggested an 

intrinsic relationship with the text component being formatted (such as, ":figurecaption") 

(Goldfarb 1997). 

Although macros used as described above obviously provided some advantages over 

entering strings of formatting codes, it was a natural next step to see that many more

advantages could be achieved by understanding the presence of the macro name in the 

file to be identifying the occurrence of a particular text component – a third-level 

heading, caption, stanza, extract, title, etc. – rather than just being an abbreviation for a 

string of formatting commands. On this new approach, figure captions, for instance, 

would be identified with one code (say, ":FigureCaption;"), and third-level headings 

would be identified with another (say, ":Heading3;" even if, according to the page design 

specification currently being applied, these codes were mapped to the same set of 

formatting commands. 

The first advantage to this new approach is that it is now possible to globally alter the 

formatting of figure captions (by simply updating the formatting commands associated 

with ":FigureCaption;") without necessarily changing the formatting of the third-level 

headings (identified by the macro name ":Heading3;"). In addition, authoring and even 

composition can now take place without the author or compositor needing to know how 

the different components are to be formatted. As this approach to typesetting and text 

processing began to be systematically applied it became quickly apparent that there were 

a great many other advantages. So many advantages, in fact, and such a diversity of 

advantages, that the descriptive markup approach began to appear to be somehow the 

fundamentally correct approach to organizing and processing text (Goldfarb 1981; Reid 

1981; Coombs et al. 1987). 

The promotion of descriptive markup as the fundamentally correct systematic approach 

in digital publishing and text processing is usually traced to three events: (i) a 

presentation made by William Tunnicliffe, chairman of the Graphic Communications 

Association's Composition Committee, at the Canadian Government Printing Office in 

September 1967; (ii) book designer Stanley Rice's project, also in the late 1960s, of 

developing a universal catalogue of "editorial structure" tags that would simplify book 

design and production; and (iii) early work on the text processing macro language 

"GML", led by Charles Goldfarb, at IBM in 1969 (SGML Users' Group 1990; Goldfarb 

1997). In the late 1970s these events would lead to an effort to develop SGML, a standard 

for machine-readable definitions of descriptive markup languages. Other examples of 

early use of descriptive markup in digital text processing include the Brown University 

PRESS hypertext system (Carmody et al. 1969; DeRose and van Dam 1999), and, later in 

the 1970s, Brian Reid's SCRIBE (Reid 1978, 1981). In addition, the seminal work on text 

processing by Douglas Engelbart in the 1960s should probably also be seen as exhibiting 

some of the rudiments of this approach (Engelbart et al. 1973). 

Nature and advantages of descriptive markup (adapted from DeRose 

et al. 1990) 

Early experience with descriptive markup encouraged some text processing researchers to 

attempt to develop a general theoretical framework for markup and to use that framework 

to support the development of high-function text processing systems. Some of the 

research and analysis on markup systems was published in the scientific literature (Goldfarb 

1981; Reid 1981; Coombs et al. 1987), but most was recorded only in the working

documents and products of various standards bodies, and in the manuals and technical 

documentation of experimental systems. 

At the heart of this effort to understand markup systems was the distinction between 

"descriptive" and "procedural" markup originally put forward by Goldfarb. Descriptive 

markup was typically said to "identify" or "describe" the "parts" of a document, whereas 

procedural markup was a "command" or "instruction" invoking a formatting procedure. It 

was also often said that descriptive markup identified the "logical" or "editorial" parts or 

"components" of a document, or a text's "content objects" or its "meaningful structure" – 

emphasizing the distinction between the intrinsic ("logical") structure of the document 

itself, and the varying visual, graphic features of a particular presentation of that 

document. (For recent arguments that descriptive markup can be further divided into the 

genuinely descriptive and the "performative", see Renear 2000.) 

Several particular advantages of descriptive markup, such as simplified composition and 

systematic control over formatting, have already been alluded to, but in order to 

appreciate how the descriptive markup motivated a new theory of the nature of text it is 

useful to rehearse the number, diversity, and value of the advantages of descriptive 

markup in overview; so we present a categorized summary below. 

Advantages for authoring, composition, and transcription 

• Composition is simplified. With descriptive markup, intended formatting considerations 

make no claim on the attention of the author, compositor, or transcriber, whereas with 

procedural markup one must remember both (i) the style conventions that are intended, 

and (ii) the specific commands required by the formatting software to get those effects. 

With descriptive markup one simply identifies each text component for what it is and the 

appropriate formatting takes place automatically. (Particularly striking is how descriptive 

markup allows the author to work at an appropriate "level of abstraction" – identifying 

something as a quotation, paragraph, or caption is a natural authorial task, while knowing 

whether to, and how to, format that text a certain way is not.) 

• Structure-oriented editing is supported. Descriptive markup supports "structureoriented 

editors" who "know" about what patterns of components can be found in a 

particular genre of document and who use this knowledge to assist the author or 

compositor. For instance, if a date component must always follow a title component then 

the software, upon detecting that a title component has just been entered by an author, 

can automatically add the required date markup and prompt the author to enter the actual 

date. If either date or status is allowed after a title then the author will be presented with a 

choice. During editing the cursor location can be used to identify and present to the 

author the list of components that may be added or deleted at that point. For complicated 

document genres this means that there is much less for the author to remember and fewer 

possibilities for error. 

• More natural editing tools are supported. Moves and deletes, for example, can take as 

their targets and scope the natural meaningful parts of the text (words, sentences, para

graphs, sections, extracts, equations, etc.) rather than relying on the mediation of 

accidental features (such as current lineation) or arbitrarily marked regions. 

• Alternative document views are facilitated. An outline view of a text, for instance, can 

be done automatically, by taking advantage of the descriptive markup for chapters, 

sections, and headings. Or a more sophisticated and specialized display of portions of 

documents can be effected using identified discipline-specific components: such as 

equations, examples, cautions, lines spoken by a particular character in a play script, and 

so on. 

Advantages for publishing 

• Formatting can be generically specified and modified. When procedural markup is 

being used, the appearance of paragraphs can only be modified by editing the formatting 

commands preceding each actual occurrence of a paragraph in the source file, whereas 

with descriptive markup only the rule associating formatting commands with the 

descriptive markup for paragraph needs to be updated – and if these rules are stored 

separately there may be no need to even alter the files containing text in order to make 

formatting alterations. Obviously, controlling formatting with descriptive markup is 

easier, less error-prone, and ensures consistency. 

• Apparatus can be automated. Descriptive markup supports the creation of indexes, 

appendices, and such. For instance, if stanzas and verse lines are explicitly identified, the 

creation of an index of first lines of stanzas (or second lines or last lines) is a matter of 

simple programming, and does not require that a human editor again laboriously identify 

verses and lines. Similarly, generating tables for equations, plates, figures, examples, is 

also easy, as are indexes of place names, personal names, characters, medium, authors, 

periods, and so on. 

• Output device support is enhanced. When coding is based on logical role rather than 

appearance, output-device specific support for printers, typesetters, video display 

terminals and other output devices can be maintained separately, logically and physically, 

from the data with the convenient result that the data files themselves are output-device 

independent while their processing is efficiently output-device sensitive. 

• Portability and interoperability are maximized. Files that use descriptive markup, rather 

than complicated lists of application-specific formatting instructions, to identify 

components are much easier to transfer to other text processing systems. In some cases 

little more than a few simple systematic changes to alter the delimiter conventions, 

substitute one mnemonic name for another, and a translation of format ting rules into 

those for the new system, are all that is necessary. 

Advantages for archiving, retrieval, and analysis 

• Information retrieval is supported. Descriptive markup allows documents to be treated 

as a database of fielded content that can be systematically accessed. One can request all

equations, or all headings, or all verse extracts; or one can request all titles that contain a 

particular personal name, place name, chemical, disease, drug, or therapy. This can 

facilitate not only personal information retrieval functions, such as the generation of 

alternative views, but also a variety of finding aids, navigation, and data retrieval 

functions. It may seem that in some cases, say where equations are uniquely formatted, it 

is not necessary to identify them, as the computer could always be programmed to exploit 

the formatting codes. But in practice it is unlikely that equations will always be 

consistently formatted, and even more unlikely that they will be uniquely formatted. 

Similarly, it might be thought that a string search might retrieve all references to the city 

Chicago, but without markup one cannot distinguish the city, the rock band, and the 

artist. 

• Analytical procedures are supported. Computer-based analysis (stylometrics, content 

analysis, statistical studies, etc.) can be carried out more easily and with better results if 

features such as sentences, paragraphs, stanzas, dialogue lines, stage directions, and so on 

have been explicitly identified so that the computer can automatically distin guish them. 

Consider some examples: If the style of spoken language in a play is being analyzed, the 

text that is not speech (stage directions, notes, etc.) must not be conflated with the 

dialogue. If the speech of a particular character is being studied it must be distinguished 

from the speech of other characters. If proximity is being studied, the word ending one 

paragraph should perhaps not be counted as collocated with the word beginning the next, 

and so on. Descriptive markup identifies these important components and their 

boundaries, supporting easier, more consistent, and more precise automatic processing. 

The OHCO view of "what text really is" 

That the descriptive markup approach had so many advantages, and so many different 

kinds of advantages, seemed to some people to suggest that it was not simply a handy 

way of working with text, but that it was rather in some sense deeply, profoundly, 

correct, that "descriptive markup is not just the best approach … it is the best imaginable 

approach" (Coombs et al. 1987). 

How could this be? One answer is that the descriptive markup approach, and only the 

descriptive markup approach, reflects a correct view of "what text really is" (DeRose et 

al. 1990). On this account, the concepts of descriptive markup entail a model of text, and 

that model is more or less right. The model in question postulates that text consists of 

objects of a certain sort, structured in a certain way. The nature of the objects is best 

suggested by example and contrast. They are chapters, sections, paragraphs, titles, 

extracts, equations, examples, acts, scenes, stage directions, stanzas, (verse) lines, and so 

on. But they are not things like pages, columns, (typographical) lines, font shifts, vertical 

spacing, horizontal spacing, and so on. The objects indicated by descriptive markup have 

an intrinsic direct connection with the intellectual content of the text; they are the 

underlying "logical" objects, components that get their identity directly from their role in 

carrying out and organizing communicative intention. The structural arrangement of 

these "content objects" seems to be hierarchical – they nest in one another without 

overlap. Finally, they obviously also have a linear order as well: if a section contains

three paragraphs, the first paragraph precedes the second, which in turn precedes the 

third. 

On this account then text is an "Ordered Hierarchy of Content Objects" (OHCO), and 

descriptive markup works as well as it does because it identifies that hierarchy and makes 

it explicit and available for systematic processing. This account is consistent with the 

traditional well-understood advantages of "indirection" and "data abstraction" in 

information science. 

A number of things seem to fall into place from this perspective. For one thing, different 

kinds of text have different kinds of content objects (compare the content objects in 

dramatic texts with those in legal contracts), and typically the patterns in which content 

objects can occur is at least partially constrained: the parts of a letter occur in a certain 

order, the lines of a poem occur within, not outside of a stanza, and so on. Presentational 

features make it easier for the reader to recognize the content objects of the text. 

There are alternative models of text that could be compared with the OHCO model. For 

instance, one could model text as a sequence of graphic characters, as in the "plain vanilla 

ASCII" approach of Project Gutenberg; as a combination of procedural coding and 

graphic characters, as in a word processing file; as a complex of geometric shapes, as in 

"vector graphics" format of an image of a page on which the text is written; as a pure 

image, as in a raster image format (JPEG, GIF, etc.); or in a number of other ways. 

However, implementations based on these alternative models are all clearly inferior in 

functionality to implementations based on an OHCO model, and in any case can be easily 

and automatically generated from an OHCO format (DeRose et al. 1990; Renear et al. 

1996). 

The OHCO account of "what text really is" has not gone uncriticized, but many have 

found it a compelling view of text, one that does explain the effectiveness of descriptive 

markup and provides a general context for the systematization of descriptive markup 

languages with formal metalanguages such as SGML. 

SGML and XML 

As we indicated above, this chapter is not designed to introduce the reader to text 

encoding, but to provide in overview some useful historical and theoretical background 

that should help support a deeper understanding. In this section we present some of the 

principal themes in SGML and XML, and take up several topics that we believe will be 

useful, but for a complete presentation the reader is again directed to the "Gentle 

Introduction" and the other readings indicated in the references below. 

History of SGML and XML: Part I 

Norman Scharpf, director of the Graphics Communication Association, is generally 

credited with recognizing the significance of the work, mentioned above, of Tunnicliffe, 

Rice, and Goldfarb, and initiating, in the late 1960s, the GCA "GenCode" project, which

had the goal of developing a standard descriptive markup language for publishing 

(SGML Users' Group 1990; Goldfarb 1997). Soon much of this activity shifted to the 

American National Standards Institute (ANSI), which selected Charles Goldfarb to lead 

an effort for a text description language standard based on GML and produced the first 

working draft of SGML in 1980. These activities were reorganized as a joint project of 

ANSI and the International Organization for Standardization (ISO) and in 1986 ISO 

published ISO 8879: Information Processing – Text and Office Systems – Standard 

Generalized Markup Language (SGML) (ISO 1986; Goldfarb 1991). Later events in the 

history of SGML, including the development of XML and the profusion of SGML/XML 

standards that began in the mid-1990s, are discussed below. We will later describe more 

precisely the relationship between SGML and XML; at this point in the chapter the reader 

only needs to know that XML is, roughly, just a simpler version of SGML, and as a 

consequence almost everything we say about SGML applies to XML as well. 

The basic idea: a metalanguage for defining descriptive markup 

languages 

Although for a variety of reasons the SGML standard itself can be difficult to read and 

understand, the basic idea is quite simple. SGML is a language for creating machinereadable 

definitions of descriptive markup languages. As such it is called a 

"metalanguage", a language for defining a language. The SGML standard provides a way 

to say things like this: these are the characters I use for markup tag delimiters; these are 

the markup tags I am using; these are the acceptable arrangements that the components 

identified by my markup tags may take in a document; these are some characteristics I 

may be asserting of those components, these are some abbreviations and shortcuts I'll be 

using, and so on. An alternative characterization of SGML is as a "metagrammar", a 

grammar for defining other grammars; here "grammar" is used in a technical sense 

common in linguistics and computer science. (Although its origin is as a metalanguage 

for defining document markup languages, SGML can be, and is, used to define markup 

languages for things other than documents, such as languages for data interchange or 

interprocess communication, or specific data management applications.) 

An almost inevitable first misconception is that SGML is a document markup language 

with a specific set of document markup tags for features such as paragraphs, chapters, 

abstracts, and so on. Despite its name ("Standard Generalized Markup Language"), 

SGML is not a markup language in this sense and includes no document markup tags for 

describing document content objects. SGML is a metalanguage, a language for defining 

document markup languages. The confusion is supported by the fact that both markup 

metalanguages like SGML and XML and also SGML-based markup languages like 

HTML and XHTML (technically applications of SGML) are "markup languages" in 

name. In each case the "ML" stands for "markup language", but SGML and HTML are 

markup languages in quite different senses. The characterization of SGML as a document 

markup language is not entirely unreasonable of course, as it is in a sense a language for 

marking up documents, just one without a predefined set of markup for content objects. 

Whether or not SGML should be called a markup language is historically moot, but to 

emphasize the distinction between markup languages that are actual sets of markup tags

for document content objects (such as HTML, XHTML, TEI, DocBook, etc.) and 

metalanguages for defining such markup languages (such as SGML and XML) we will, 

in what follows, reserve the term markup language for languages like HTML and TEI, 

excluding the metalanguages used to define them. 

Standardizing a metalanguage for markup languages rather than a markup language was a 

key strategic decision. It was an important insight of the GCA GenCode Committee that 

any attempt to define a common markup vocabulary for the entire publishing industry, or 

even common vocabularies for portions of the industry, would be extremely difficult, at 

best. However, a major step towards improving the interoperability of computer 

applications and data could be achieved by standardizing a metalanguage for defining 

markup languages. Doing this would obviously be an easier task: (i) it would not require 

the development of a markup language vocabulary with industry-wide acceptance and (ii) 

it would not assume that the industry would continue to conform, even as circumstances 

changed and opportunities for innovations arose, to the particular markup language it had 

agreed on. 

At the same time this approach still ensured that every markup language defined using 

SGML, even ones not yet invented, could be recognized by any computer software that 

understood the SGML metalanguage. The idea was that an SGML software application 

would first process the relevant SGML markup language definition, and then could go 

on, as a result of having processed that definition, to process the marked-up document 

itself – now being able to distinguish tags from content, expand abbreviations, and verify 

that the markup was correctly formed and used and that the document had a structure that 

was anticipated by the markup language definition. Because the SGML markup language 

definition does not include processing information such as formatting instructions, 

software applications would also require instructions for formatting that content; shortly 

after the SGML project began, work started on a standard for assigning general 

instructions for formatting and other processing to arbitrary SGML markup. This was the 

Document Style and Semantics Specification Language (DSSSL). The World Wide Web 

Consortium (W3C)'s Extensible Stylesheet Language (XSL) is based on DSSSL, and the 

W3C Cascading Style Sheet specification performs a similar, although more limited 

function. 

This strategy supports technological innovation by allowing the independent 

development of better or more specialized markup, without the impediment of securing 

antecedent agreement from software developers that they will manufacture software that 

can process the new markup – all that is required is that the software understand SGML, 

and not a specific SGML markup language. 

Elements and element types 

Up until now we have been using the phrase content object for the logical parts of a 

document or text. SGML introduces a technical term, element, that roughly corresponds 

to this notion, developing it more precisely. The first bit of additional precision is that 

where we have used "content object" ambiguously, sometimes meaning the specific titles,

paragraphs, extracts and the like that occur in actual documents, and sometimes meaning 

the general kind of object (title, paragraph, extract) of which those specific occurrences 

are instances, SGML has distinct terminology for these two senses of "content object": an 

actual component of a document is an "element", and the type of an element (title, 

paragraph, extract, etc.) is an "element type." However, apart from the standard itself, and 

careful commentary on the standard, it is common, and usually not a problem, for 

"element" to be used in the sense of "element type" as well. This kind of ambiguity is 

common enough in natural language and almost always easily and unconsciously 

resolved by context. 

The second bit of additional precision is that where we used "content object" to refer to 

the parts of texts understood in the usual way as abstract cultural objects, independent of, 

and prior to, any notion of markup languages, the most exact use of "element" in the 

technical SGML sense is to refer to the combination of SGML markup tags and enclosed 

content that is being used to represent (for instance, in a computer file) these familiar 

abstract textual objects. Again the distinction is subtle and "element" is routinely used in 

both senses (arguably even in the standard itself). Typically, no damage is done, but 

eventually encoding cruxes, design quandaries, or theoretical disputes may require appeal 

to the full range of distinctions that can be made when necessary. 

Document types and document instances 

Fundamental to SGML is the notion of the document type, a class of documents with a 

particular set of content objects that can occur in some combinations but not in others. 

Some examples of document types might be: novel, poem, play, article, essay, letter, 

contract, proposal, receipt, catalogue, syllabus, and so on. Some examples of combination 

rules: in a poem, lines of verse occur within stanzas, not vice versa; in a play, stage 

directions may occur either within or between speeches but not within other stage 

directions (and not, normally, in the table of contents); in a catalogue, each product 

description must contain exactly one part number and one or more prices for various 

kinds of customers. There are no predefined document types in SGML of course; the 

identification of a document type at an appropriate level of specificity, (e.g., poem, 

sonnet, petrarchan sonnet, sonnet-by-Shakespeare) is up to the SGML markup language 

designer. 

A document type definition (DTD) defines an SGML markup language for a particular 

document type. Part of a document type definition is given in a document type 

declaration that consists of a series of SGML markup declarations that formally define 

the vocabulary and syntax of the markup language. These markup declarations specify 

such things as what element types there are, what combinations elements can occur in, 

what characteristics may be attributed to elements, abbreviations for data, and so on. 

The SGML standard is clear that there is more to a document type definition than the 

markup declarations of the document type declaration: "Parts of a document type 

definition can be specified by a SGML document type declaration, other parts, such as 

the semantics of elements and attributes or any application conventions … cannot be

expressed formally in SGML" (ISO 1986: 4.105). That is, the document type 

declarations can tell us that a poem consists of exactly one title followed by one or more 

lines and that lines can't contain titles, but it does not have any resources for telling us 

what a title is. The document type definition encompasses more. However, all that the 

SGML standard itself says about how to provide the required additional information 

about "semantics … or any application conventions" is: "Comments may be used … to 

express them informally" (ISO 1986: 4.105). In addition, it is generally thought that 

properly written prose documentation for a markup language is an account of the 

"semantics … or any application conventions" of that markup language. 

Obviously these two expressions "document type definition" and "document type 

declaration", which are closely related, sound similar, and have the same initials, can 

easily be confused. But the difference is important, if irregularly observed. We reiterate 

that: it is the document type definition that defines a markup language; part of that 

definition is given in the markup declarations of the document type declaration; and an 

additional portion, which includes an account of the meaning of the elements' attributes, 

is presented informally in comments or in the prose documentation of the markup 

language. We note that according to the SGML standard the acronym "DTD" stands for 

"document type definition" which accords poorly with the common practice of referring 

to the markup declarations of the document type declaration as, collectively, the "DTD", 

and using the extension "DTD" for files containing the markup declarations – these being 

only part of the DTD. 

A document instance is "the data and markup for a hierarchy of elements that conforms to 

a document type definition" (ISO 1986: 4.100, 4.160). Typically, we think of the file of 

text and markup, although in fact the definition, like SGML in general, is indifferent to 

the physical organization of information: an SGML document instance may be organized 

as a "file", or it may be organized some other way. 

Strictly speaking (i.e., according to ISO 1986, definitions 4.100 and 4.160), a document 

instance by definition actually conforms to the document type definition that it is 

putatively an instance of. This means that (i) the requirements for element and attribute 

use expressed in the markup declarations are met; and (ii) any other expressed semantic 

constraints or applications are followed. However, actual usage of "document instance" 

deviates from this definition in three ways: (i) it is common to speak of text and markup 

as a "document instance" even if it in fact fails to conform to the markup declarations of 

the intended DTD (as in "The document instance was invalid"); (ii) text plus markup is 

referred to as a document instance even if it is not clear what if any DTD it conforms to; 

and (iii) even a terminological purist wouldn't withhold the designation "document 

instance" because of a failure to conform to the "semantics and application conventions" 

of the document type definition – partly, no doubt, because there is no established way to 

automatically test for semantic conformance.

History of SGML and XML: Part II 

Although SGML was soon used extensively in technical documentation and other largescale 

publishing throughout the world, it did not have the broad penetration into 

consumer publishing and text processing that some of its proponents had expected. Part 

of the problem was that it was during this same period that "WYSIWIG" word processing 

and desktop publishing emerged, and had rapid and extensive adoption. Such software 

gave users a sense that they were finally (or once again?) working with "the text itself", 

without intermediaries, and that they had no need for markup systems; they could, again, 

return to familiar typewriter-based practices, perhaps enhanced with "cut-and-paste." This 

was especially frustrating for SGML enthusiasts because there is no necessary real 

opposition between WYSIWYG systems and descriptive markup systems and no reason, 

other than current market forces and perhaps technical limitations, why a WYSIWYG 

system couldn't be based on the OHCO model of text, and store its files in SGML. Most 

of what users valued in WYSIWYG systems – immediate formatting, menus, interactive 

support, etc. – was consistent with the SGML/OHCO approach. Users would of course 

need to forgo making formatting changes "directly", but on the other hand they would get 

all the advantages of descriptive markup, including simpler composition, context-oriented 

support for selecting document components, global formatting, and so on. There would 

never, needless to say, be any need to remember, type, or even see, markup tags. The 

"WYSIWYG" classification was misleading in any case, since what was at issue was 

really as much the combination of editing conveniences such as interactive formatting, 

pull-down menus, and graphical displays, as it was a facsimile of how the document 

would look when printed – which would typically vary with the circumstances, and often 

was not up to the author in any case. 

But very few SGML text processing systems were developed, and fewer still for general 

users. Several explanations have been put forward for this. One was that OHCO/SGML - 

based text processing was simply too unfamiliar, even if more efficient and more 

powerful, and the unfamiliarity, and prospect of spending even an hour or two learning 

new practices posed a marketing problem for salesmen trying to make a sale "in three 

minutes on the floor of Radio Shack." Another was that OHCO/SGML software was just 

too hard to develop: the SGML standard had so many optional features, as well as several 

characteristics that were hard to program, that these were barriers to development of 

software. Several OHCO/SGML systems were created in the 1980s, but these were not 

widely used. 

The modest use of SGML outside of selected industries and large organizations changed 

radically with the emergence of HTML, the HyperText Markup Language, on the World 

Wide Web. HTML was designed as an SGML language (and with tags specifically 

modeled on the "starter tag set" of IBM DCF/GML). However, as successful as HTML 

was it became apparent that it had many problems, and that the remedies for these 

problems could not be easily developed without changes in SGML. 

From the start HTML's relationship with SGML was flawed. For one thing HTML began 

to be used even before there was a DTD that defined the HTML language. More

important, though, was the fact that HTML indiscriminately included element types not 

only for descriptive markup, but also procedural markup as well ("font", "center", and so 

on). In addition there was no general stylesheet provision for attaching formatting or 

other processing HTML. Finally none of the popular web browsers ever validated the 

HTML they processed. Web page authors had only the vaguest idea what the syntax of 

HTML actually was and had little motivation to learn, as browsers were forgiving and 

always seemed to more or less "do the right thing." DTDs seemed irrelevant and 

validation unnecessary. HTML files were in fact almost never valid HTML. 

But perhaps the most serious problem was that the HTML element set was impoverished. 

If Web publishing were going to achieve its promise it would have to accommodate more 

sophisticated and specialized markup languages. That meant that it would need to be 

easier to write the software that processed DTDs, so that arbitrary markup vocabularies 

could be used without prior coordination. And it was also obvious that some provision 

needed to be made for allowing more reliable processing of document instances without 

DTDs. 

This was the background for the development of XML 1.0, the "Extensible Markup 

Language." XML was developed within the World Wide Web Consortium (W3C) with 

the first draft being released in 1996, the "Proposed Recommendation" in 1997, and the 

"Recommendation" in 1998. The committee was chaired by Jon Bosak of Sun 

Microsystems, and technical discussions were conducted in a working group of 100 to 

150 people, with final decisions on the design made by an editorial review board with 

eleven members. The editors, leading a core working group of eleven people, supported 

by a larger group of 150 or so experts in the interest group, were Michael Sperberg- 

McQueen (University of Illinois at Chicago and the Text Encoding Initiative), Tim Bray 

(Textuality and Netscape), and Jean Paoli (Microsoft). 

XML 

The principal overall goal of XML was to ensure that new and specialized markup 

languages could be effectively used on the Web, without prior coordination between 

software developers and content developers. One of the intermediate objectives towards 

this end was to create a simpler, more constrained version of SGML, so that with fewer 

options to support and other simplifications, it would be easier for programmers to 

develop SGML/XML software, and then more SGML software would be developed and 

used, and would support individualized markup languages. This objectively was 

achieved: the XML specification is about 25 pages long, compared to SGML's 155 (664 

including the commentary in Charles Goldfarb's SGML Handbook), and while the 

Working Group did not achieve their stated goal that a graduate student in computer 

science should be able to develop an XML parser in a week, the first graduate student to 

try it reported success in a little under two weeks. 

Another objective was to allow for processing of new markup languages even without a 

DTD. This requires several additional constraints on the document instance, one of which 

is illustrative and will be described here. SGML allowed markup tags to be omitted when

they were implied by their markup declarations. For instance if the element type 

declaration for paragraph did not allow a paragraph inside of a paragraph, then the starttag 

of a new paragraph would imply the closing of the preceding paragraph even without 

an end-tag, and so the end-tag could be omitted by the content developer as it could be 

inferred by the software. But without a DTD it is not possible to know what can be nested 

and what can't – and so in order to allow "DTD-less" processing XML does not allow 

tags to be omitted. It is the requirement that elements have explicit end-tags that is 

perhaps the best-known difference between documents in an SGML language like HTML 

and an XML language like XHTML. 

Whereas SGML has just one characterization of conformance for document instances, 

XML has two: (i) all XML documents must be well-formed; (ii) an XML document may 

or may not be valid (vis-a-vis a DTD). To be well-formed, a document instance need not 

conform to a DTD, but it must meet other requirements, prominent and illustrative among 

them: no start-tags or end-tags may be omitted, elements must not "overlap", attribute 

values must be in quotation marks, and case must be consistent. These requirements 

ensure that software processing the document instance will be able to unambiguously 

determine a hierarchy (or tree) of elements and their attribute assignments, even without a 

DTD♭. 

The Text Encoding Initiative 

Background 

The practice of creating machine-readable texts to support humanities research began 

early and grew rapidly. Literary text encoding is usually dated from 1949, when Father 

Roberto Busa began using IBM punched-card equipment for the Index Thomisticus - in 

other words, literary text encoding is almost coeval with digital computing itself. By the 

mid-1960s there were at least three academic journals focusing on humanities computing, 

and a list of "Literary Works in Machine-Readable Form" published in 1966 already 

required 25 pages (and included some projects, listed modestly as single items, that were 

encoding an entire authorial oeuvre) (Carlson 1967). It is tempting to speculate that 

efforts to standardize encoding practices must have begun as soon as there was more than 

one encoding project. Anxiety about the diversity of encoding systems appears early – 

one finds that at a 1965 conference on computers and literature for instance, an 

impromptu meeting was convened to discuss "the establishment of a standard format for 

the encoding of text … a matter of great importance." It obviously wasn't the first time 

such a concern had been expressed, and it wouldn't be the last (Kay 1965). 

At first, of course, the principal problem was consistent identification and encoding of the 

characters needed for the transcription of literary and historical texts. But soon it included 

encoding of structural and analytic features as well. A standard approach to literary text 

encoding would have a number of obvious advantages; it would make it easier for 

projects and researchers to share texts, possible to use the same software across textual 

corpora (and therefore more economical to produce such software), and it would simplify 

the training of encoders – note that these advantages are similar to those, described

above, which motivated the effort to develop standards in commercial electronic 

publishing. And one might hope that without the disruptive competitive dynamic of 

commercial publishing where formats are sometimes aspects of competitive strategy, it 

would be easier to standardize. But there were still obstacles. For one thing, given how 

specialized text encoding schemes sometimes were, and how closely tied to the specific 

interests and views – even disciplines and theories – of their designers, how could a 

single common scheme be decided on? 

Origins 

The TEI had its origins in early November, 1987, at a meeting at Vassar College, 

convened by the Association for Computers in the Humanities and funded by the 

National Endowment for the Humanities. It was attended by thirty-two specialists, from 

many different disciplines and representing professional societies, libraries, archives, and 

projects in a number of countries in Europe, North America, and Asia. There was a sense 

of urgency, as it was felt that the proliferation of needlessly diverse and often poorly 

designed encoding systems threatened to block the development of the full potential of 

computers to support humanities research. 

The resulting "Poughkeepsie Principles" defined the project of developing a set of text 

encoding guidelines. This work was then undertaken by three sponsoring organizations: 

The Association for Computers in the Humanities, the Association for Literary and 

Linguistic Computing, and the Association for Computational Linguistics. A Steering 

Committee was organized and an Advisory Board of delegates from various professional 

societies was formed. Two editors were chosen: Michael Sperberg-McQueen of the 

University of Illinois at Chicago, and Lou Burnard of Oxford University. Four working 

committees were formed and populated. By the end of 1989 well over fifty scholars were 

already directly involved and the size of the effort was growing rapidly. 

The first draft ("PI") of the TEI Guidelines was released in June, 1990. Another period of 

development followed (the effort now expanded and reorganized into 15 working groups) 

releasing revisions and extensions throughout 1990–93. Early on in this process a number 

of leading humanities textbase projects began to use the draft Guidelines, providing 

valuable feedback and ideas for improvement. At the same time workshops and seminars 

were conducted to introduce the Guidelines as widely as possible and ensure a steady 

source of experience and ideas. During this period, comments, corrections, and requests 

for additions arrived from around the world. After another round of revisions and 

extensions the first official version of the Guidelines ("P3") was released in May, 1994. 

The TEI Guidelines have been an enormous success and today nearly every humanities 

textbase project anywhere in the world uses TEI. In 2002 the newly formed TEI 

Consortium released "P4", a revision that re-expressed the TEI Guidelines in XML, and 

today the Consortium actively continues to evolve the Guidelines and provide training, 

documentation, and other support. After HTML, the TEI is probably the most extensively 

used SGML/XML text encoding system in academic applications.

The history of the development of the TEI Guidelines, briefly told here, makes evident an 

important point. The Guidelines were, and continue to be, the product of a very large 

international collaboration of scholars from many disciplines and nations, with many 

different interests and viewpoints, and with the active participation of other specialists 

from many different professions and institutions. The work was carried out over several 

years within a disciplined framework of development, evaluation, and coordination, and 

with extensive testing in many diverse projects. All of this is reflected in a text encoding 

system of extraordinary power and subtlety. 

General nature and structure 

The TEI Guidelines take the form of several related SGML document type definitions, 

specified both in formal markup declarations and in English prose. The main DTD, for 

the encoding of conventional textual material such as novels, poetry, plays, dictionaries, 

or term-bank data, is accompanied by a number of auxiliary DTDs for specialized data: 

tag-set documentation, feature-system declarations used in structured linguistic or other 

annotation, writing-system declarations, and free-standing bibliographic descriptions of 

electronic texts. 

The main DTD itself is partitioned into eighteen discrete tag sets or modules, which can 

be combined in several thousand ways to create different views of the main DTD. Some 

tag sets are always included in a view (unless special steps are taken to exclude them), 

some (the "base" tag sets) are mutually exclusive, and some (the "additional" tag sets) 

may be combined with any other tag sets. 

In addition to the flexibility of choosing among the various tag sets, the main TEI DTD 

takes a number of steps to provide flexibility for the encoder and avoid a Procrustean 

rigidity that might interfere with the scholarly judgment of the encoder. Individual 

element types may be included or excluded from the DTD, additional element types may 

be introduced (as long as they are properly documented), and the names of element types 

and attributes may be translated into other languages for the convenience of the encoder 

or user. 

It cannot be repeated enough that the apparent complexity of the TEI is, in a sense, only 

apparent. For the most part only as much of the TEI as is needed will be used in any 

particular encoding effort; the TEI vocabulary used will be exactly as complex, but no 

more complex, than the text being encoded. Not surprisingly, a very small TEI 

vocabulary, known as TEI Lite (http://www.tei.org), is widely used for simple texts. 

Larger significance of the TEI 

The original motivation of TEI was to develop interchange guidelines that would allow 

projects to share textual data (and theories about that data) and promote the development 

of common tools. Developing such a language for the full range of human written culture, 

the full range of disciplinary perspectives on those objects, and the full range of 

competing theories was a daunting task.

It is easy to talk about accommodating diversity, about interdisciplinarity, about multiculturalism, 

about communication across various intellectual gaps and divides. But few 

efforts along these lines are more than superficial. The experience of the TEI makes it 

evident why this is so. Not only do different disciplines have quite different interests and 

perspectives, but, it seems, different conceptual schemes: fundamentally different ways 

of dividing up the world. What is an object of critical contest and debate for one 

discipline, is theory-neutral data for another, and then completely invisible to a third. 

What is structured and composite for one field is atomic for another and an irrelevant 

conglomeration to a third. Sometimes variation occurred within a discipline, sometimes 

across historical periods of interest, sometimes across national or professional 

communities. Practices that would seem to have much in common could vary radically – 

and yet have enough in common for differences to be a problem! And even where 

agreement in substance was obtained, disagreements over nuances, of terminology for 

instance, could derail a tenuous agreement. 

(Mylonas and Renear 1999) 

The TEI made several important decisions that gave the enterprise at least the chance of 

success. For one thing the TEI determined that it would not attempt to establish in detail 

the "recognition criteria" for TEI markup; it would be the business of the encoderscholar, 

not that of the TEI, to specify precisely what criteria suffice to identify a 

paragraph, or a section, or a technical term, in the text being transcribed. Examples of 

features commonly taken to indicate the presence of an object were given for illustration, 

but were not defined as normative. In addition the TEI, with just a few trivial exceptions, 

does not specify what is to be identified and encoded. Again, that is the business of the 

scholar or encoder. What the TEI does is provide a language to be used when the encoder 

(i) recognizes a particular object, and (ii) wishes to identify that object. In this sense, the 

TEI doesn't require antecedent agreement about what features of a text are important and 

how to tell whether they are present; instead, it makes it possible, when the scholar 

wishes, to communicate a particular theory of what the text is. One might say that the TEI 

is an agreement about how to express disagreement. 

The principal goal of the TEI, developing an interchange language that would allow 

scholars to exchange information, was ambitious enough. But the TEI succeeded not only 

in this, but at a far more difficult project, the development of a new data description 

language that substantially improves our ability to describe textual features, not just our 

ability to exchange descriptions based on current practice. The TEI Guidelines represent 

an elucidation of current practices, methods, and concepts, [that] opens the way to new 

methods of analysis, new understandings, and new possibilities for representation and 

communication. Evidence that this is indeed a language of new expressive capabilities 

can be found in the experience of pioneering textbase projects which draw on the 

heuristic nature of the TEI Guidelines to illuminate textual issues and suggest new 

analyses and new techniques. 

(Mylonas and Renear 1999)

Finally, we note that the TEI is now itself a research community, 

connecting many professions, disciplines, and institutions in many countries and defining 

itself with its shared interests, concepts, tools, and techniques. Its subject matter is textual 

communication, with the principal goal of improving our general theoretical 

understanding of textual representation, and the auxiliary practical goal of using that 

improved understanding to develop methods, tools, and techniques that will be valuable 

to other fields and will support practical applications in publishing, archives, and 

libraries. It has connections to knowledge representation systems (formal semantics and 

ontology, objection orientation methodologies, etc.), new theorizing (non-hierarchical 

views of text, antirealism, etc.), and new applications and tools. ♭ providing new insights 

into the nature of text, and new techniques for exploiting the emerging information 

technologies. 

(Mylonas and Renear 1999) 

Concluding Remarks 

Text encoding has proven an unexpectedly exciting and illuminating area of activity, and 

we have only been able to touch on a few aspects here. Ongoing work is of many kinds 

and taking place in many venues, from experimental hypermedia systems, to improved 

techniques for literary analysis, to philosophical debates about textuality. The references 

below should help the reader explore further. 

For Further Information 

SGML, XML, and related technologies 

The best introduction to XML is the second chapter of the TEI Guidelines, "A Gentle 

Introduction to XML", online at . 

An excellent single-volume presentation of a number of XML-related standards, with 

useful practical information, is Harold and Means (2001). See also Bradley (2001). 

The SGML standard is available as Goldfarb (1991), a detailed commentary that includes 

a cross-reference annotated version of the specification. A treatment of a number of 

subtleties is DeRose (1997). 

A good detailed presentation of the XML specification is Graham and Quin (1999). 

There are two extremely important resources on the Web for anyone interested in any 

aspect of contemporary SGML/XML text encoding. One is the extraordinary "Cover 

Pages", http://xml.coverpages.org, edited by Robin Cover and hosted by OASIS, an 

industry consortium. These provide an unparalleled wealth of information and news 

about XML and related standards. The other is the W3C site itself, http://www.w3c.org.

These will provide an entry to a vast number of other sites, mailing lists, and other 

resources. Two other websites are also valuable: Cafe con Leche 

http://www.ibiblio.org/xml/, and XML.com http://www.xml.com. The most advanced 

work in SGML/ XML markup research is presented at IDEAlliances' annual Extreme 

Markup Languages conference; the proceedings are available online, at 

http://www.idealliance.org/papers/extreme03/. 

Document analysis, data modeling, DTD design, project design 

Unfortunately there is a lack of intermediate and advanced material on the general issues 

involved in analyzing document types and developing sound DTD designs. There is just 

one good book-length treatment: Maler and Andaloussi (1996), although there are 

introductory discussions from various perspectives in various places (e.g., Bradley 2001). 

Other relevant discussions can be found in various places on the Web (see, for example, 

the debate over elements vs. attributes at 

http://xml.coverpages.org/elementsAndAttrs.html) and in various discussion lists and the 

project documentation and reports of encoding projects. (There are however very good 

resources for learning the basic techniques of TEI-based humanities text encoding; see 

below.) 

Humanities text encoding and the TEI 

For an excellent introduction to all aspects of contemporary humanities text encoding see 

Electronic Texts in the Humanities: Theory and Practice, by Susan Hockey (Hockey 

2001). 

The TEI Consortium, http://www.tei-c.org, periodically sponsors or endorses workshops 

or seminars in text encoding using the Guidelines; these are typically of high quality and 

a good way to rapidly get up to speed in humanities text encoding. There are also selfpaced 

tutorials and, perhaps most importantly, a collection of project descriptions and 

encoding documentation from various TEI projects. See: http://www.tei-c.org/Talks/ and 

http://www.tei-c.org/Tutorials.index.html. Anyone involved in TEI text encoding will 

want to join the TEI-L discussion list. 

Theoretical issues 

Text encoding has spawned an enormous number and wide variety of theoretical 

discussions and debates, ranging from whether the OHCO approach neglects the 

"materiality" of text, to whether TEI markup is excessively "interpretative"; to whether 

texts are really "hierarchical", to the political implications of markup, to whether 

SGML/XML is flawed by the lack of an algebraic "data model." From within the 

humanities computing community some of the influential researchers on these topics 

include Dino Buzzetti, Paul Caton, Glaus Huitfeldt, Julia Flanders, Jerome McGann, 

Michael Sperberg-McQueen, Alois Pichler, and Wendell Piez, among others. A 

compendium of these topics, with references to the original discussions, may be found at 

http://www.isrl.uiuc.edu/eprg/markuptheoryreview.html.

One theoretical topic promises to soon have a broad impact beyond as well as within the 

digital humanities and deserves special mention. There has recently been a renewed 

concern that SGML/XML markup itself is not a "data model" or a "conceptual model", or 

does not have a "formal semantics." This is a criticism that on this view SGML/XML 

markup serializes a data structure, but it does not express the meaning of that data 

structure (the specification of the conceptual document) in a sufficiently formal way. For 

an introduction to this topic see Renear et al. (2002) and Robin Cover (1998, 2003). For a 

related discussion from the perspective of literary theory see Buzzetti (2002). Obviously 

these issues are closely related to those currently being taken up in the W3C Semantic 

Web Activity (http://www.w3.org/2001/sw/Activity). 


The author is deeply indebted to Michael Sperberg-McQueen for comments and 

corrections. Remaining errors and omissions are the author's alone. 

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18. 

Electronic Texts: Audiences and Purposes 

Perry Willett 

History 

… today, even the most reluctant scholar has at least an indefinite notion of the 

computer's ability to store, manipulate and analyse natural language texts, whilst his less 

wary colleagues are likely to meet with a sympathetic reception if they approach their 

local computing centre with proposals for literary or linguistic research – some 

universities, indeed, have set up institutes and departments whose special task it is to 

facilitate work of this kind. 

Wisbey, The Computer in Literary and Linguistic Research: Papers from a Cambridge 

Symposium, vii 

Claims of the ubiquity of electronic text, such as this one published in 1971, may seem 

exaggerated for an era before personal computers, before standards for character or text 

encoding, before the World Wide Web, yet they have come more or less true. What is 

remarkable about this and other writing on electronic texts in the humanities published 

before 1995 is how well they predicted the uses of electronic text, before many people 

were even aware that text could be electronic. It is important to remember the 

introduction of the World Wide Web in the mid-1990s in evaluating the statements of 

computing humanists, for prior to the Web's arrival, while a great deal was written on the 

uses of and audiences for electronic text, almost no one foresaw such a powerful tool for 

the wide distribution of electronic texts, or that wide distribution for a general reading 

public would become the most successful use made of electronic texts.

It is well documented that the history of electronic text is almost as long as the history of 

electronic computing itself. Vannevar Bush famously imagined vast libraries available 

via the new technology in 1945. Father Roberto Busa began his pioneering effort to 

create the Index Thomisticus, the monumental index to the works of St Thomas Aquinas, 

in 1946 (Busa 1950, 1992). Other pioneers adopted computers and advocated their use in 

literary and linguistic research, with electronic texts as the necessary first ingredient. The 

same early study quoted above divides the world of humanities computing into several 

categories, as shown in its table of contents: 

1 Lexicographical, textual archives, and concordance making 

2 Textual editing and attribution studies 

3 Vocabulary studies and language learning 

4 Stylistic analysis and automated poetry generation 

This list encompasses all aspects of humanities scholarship, from literary analysis and 

author attribution studies, to scholarly editing, to rhetoric and language studies, to the 

creation of archives of electronic text. While automated poetry generation has yet to find 

its audience, the other categories seem remarkably prescient. Humanities computing now 

includes media in other formats such as digital images, audio and video, yet early studies 

viewed electronic text as the starting point. Electronic text has indeed become common in 

humanities research and publishing. Many other early studies (Lusignan and North 1977; 

Hockey 1980; Bailey 1982) enthusiastically describe the potential that computers and 

electronic text hold for literary studies, and describe similar audiences. 

These scholars developed a vision of the importance of electronic texts for the 

humanities, and developed the standards by which they are created. Humanists, precisely 

because of their sophisticated understanding of text, have a central role in the 

development of standards driving the World Wide Web, with far-reaching implications 

for what can be done today on the Internet. 

Still, the kind of environment described by Wisbey in 1971, with computing centers 

sympathetic to humanists' concerns, existed at only a handful of universities and research 

centers at that time. One was more likely to find scientists and social scientists at such 

computer centers than literary scholars or historians. Universities might have had at most 

one or two humanities professors interested in electronic texts, leaving computing 

humanists largely isolated, with annual conferences and specialist journals the only 

opportunities to discuss issues, ideas, and developments with like-minded colleagues. 

Skepticism about the use of electronic texts in humanities research has a long history 

also, and not just among traditionalists. Despite the best efforts of computing humanists, 

electronic text remained the domain of a few specialists into the early 1990s. In 1993, 

Mark Olsen wrote:

[c]omputer processing of textual data in literary and historical research has expanded 

considerably since the 1960s. In spite of the growth of such applications, however, it 

would seem that computerized textual research has not had a significant influence on 

research in humanistic disciplines and that literature research has not been subject to the 

same shift in perspective that accompanied computer-assisted research in the social 

science oriented disciplines, such as history. … In spite of the investment of significant 

amounts of money and time in many projects, the role of electronic text in literary 

research remains surprisingly limited. 

(Olsen 1993–4: 309) 

He goes on to list several reasons to support this assertion. Most importantly, Olsen notes 

a distrust of computing methodology among humanists at large, and believes that most 

research involving computing is too narrowly focused to one or two authors. Olsen spoke 

not as a traditionalist, for he was (and remains) the director of the pioneering collection of 

electronic text, the American and French Research on the Treasury of the French 

Language (ARTFL). He describes the potential of harnessing computers to large 

collections of literature, and the ability to trace concepts and important keywords over 

time both within and among various authors' works, along with more sophisticated kinds 

of analysis. He notes that in at least some disciplines, in the era prior to the World Wide 

Web, a critical mass of primary texts was available, pointing to the Thesaurus Linguae 

Graecae (TLG) for classics and ARTFL for modern French. Yet, he still concludes that 

the potential goes largely ignored. Alison Finch in 1995 displayed even more skepticism 

in her analysis of computing humanists, as she explored the heroic narratives invoked by 

literary researchers who use computers, while she claimed that these studies lacked 

importance to the larger field of literary studies. 

Other commentators, less technologically savvy, see darker implications. The best known 

of these critics, Sven Birkerts in The Gutenberg Elegies (1994) and Nicholson Baker in 

Double Fold (2001), mourn the changes wrought by the development of electronic media, 

and fear that books, once decoupled from their physical presence, will lose their meaning 

and historical importance. Birkerts, also writing pre-World Wide Web, in particular fears 

that the digitization of books may lead to the "erosion of language" and a "flattening of 

historical perspectives" (Birkerts 1994: 128–9). He does not consider other possible 

outcomes, such as one in which general readers and scholars alike have a better sense of 

the concerns and ideas of peoples and historical periods with increased access to works 

otherwise available in only a few libraries. The development of digital collections does 

not require the destruction of books; instead, it may provoke more interest in their 

existence and provide different opportunities for their study through keyword and 

structured searching. 

More nuanced critiques recognize the disadvantages of digital technology, while 

exploring its uses and potentials. As Bornstein and Tinkle noted: 

We agree with the proposition that the shift from print to digital culture has analogies in 

scale and importance to the shift from manuscript to print culture beginning in the

Renaissance. Further, the change enables us to consider more strikingly than ever before 

the diverse characteristics of manuscript, print, and electronic cultures. This is 

particularly important now that new electronic technologies are making possible the 

production, display, and transmission of texts in multiple forms that far exceed the more 

fixed capacity of traditional codex books. 

(Bornstein and Tinkle 1998: 2) 

Most of these studies were written prior to widespread popularization of the World Wide 

Web. As a tool, the Web does not solve all of the problems surrounding the creation, 

storage, delivery and display of electronic texts, but certainly simplifies many of them, 

particularly in comparison with the older technologies used for electronic text storage and 

delivery such as ftp, listserv, and Gopher. The early adopters, enthusiasts, and critics 

viewed electronic texts as the domain of scholars and researchers, and used electronic 

texts to assist in the traditional work of humanists as outlined in the table of contents 

reproduced above. They did not foresee the World Wide Web's capacity to reach a wider 

reading public. 

Has the world of electronic texts changed with the ubiquity of the World Wide Web, or 

are these criticisms still accurate? Put another way, who is the audience for electronic 

texts today? A recent book on electronic text (Hockey 2000) provides a current view on 

their audience. Hockey lists these categories in her table of contents: 

1 Concordance and Text Retrieval Programs 

2 Literary Analysis 

3 Linguistic Analysis 

4 Stylometry and Attribution Studies 

5 Textual Critical and Electronic Editions 

6 Dictionaries and Lexical Databases 

On the whole, this list is not significantly different from the categories of uses and users 

in the book edited by Wisbey almost 30 years earlier. Hockey believes that "most of the 

present interest in electronic texts is focused on access" (Hockey 2000: 3), that is, the use 

of computers to store and deliver entire texts. In her view, access is the least interesting 

aspect of electronic texts, for it leaves largely unexploited their real power: the ability for 

texts to be searched and manipulated by computer programs. 

What is Electronic Text? 

Answering this simple question could involve textual and literary theory and their 

intersection with digital technology, as discussed elsewhere in this volume. I wish to

focus instead on practical considerations: what forms do electronic texts in the humanities 

take? 

The first type to consider is an electronic transcription of a literary text, in which 

characters, punctuation, and words are faithfully represented in a computer file, allowing 

for keyword or contextual searching. Just what is meant by a "faithful representation" of a 

printed book is at the crux of a long debate, again explored elsewhere in this volume (see 

chapters 16, 17, and 22, for discussions of theoretical issues, and chapter 32 for more 

practical matters). A transcription in the form of a computer text file is the most compact 

form for electronic text, an important consideration given the severe constraints on 

computer storage and bandwidth that still exist for some people. In addition, this form 

provides greater ease of manipulation, for searching, for editing, and has significant 

advantages for people with visual impairments. Another basic form is a digital image of a 

physical page, allowing readers to see a representation of the original appearance, of 

great importance for textual scholars. This once seemed impractical given the amount of 

storage necessary for the hundreds of image files needed for even one book. Now, with 

ever greater storage available even on desktop computers, this is much less of an issue 

(while bandwidth remains an issue). Some collections, such as those available through 

digital libraries at the Library of Congress, the University of Virginia, and the University 

of Michigan, combine the two forms. 

Another category is encoded text. For a multitude of reasons, many scholars and editors 

believe that encoding schemes, such as that developed by the Text Encoding Initiative 

(TEI), provide the best and fullest representation of text in all its complexity, allowing the 

creator or editor an opportunity to encode the hierarchical structures and multitude of 

features found in text. In addition to the belief that explicit structural markup along with 

robust metadata will allow for greater longevity of electronic texts, those who choose the 

TEI use its many elements and features for sophisticated analysis and repurposing of 

electronic texts for electronic and print publishing. The people developing the TEI have 

used open standards such as SGML and XML, as well as providing extensive 

documentation to assist in the use of the standard, as a way of encouraging and promoting 

the preservation and interchange of e-texts. 

Others, notably people associated with Project Gutenberg, distrust all encoding schemes, 

believing that the lack of encoding will better allow e-texts to survive changes in 

hardware, operating systems, and application software. No special software and little 

training are required for contributors to Project Gutenberg, and a volunteer ethos prevails. 

They will have over 10,000 titles at the end of 2003, but little can be said for the accuracy 

of the transcriptions, for there is no central editorial control. The lack of encoding means 

it would be impossible, for example, to separate notes from text, or to determine quickly 

where chapters begin and end, or to indicate highlighting and font shifts within the text. 

Still, Project Gutenberg's founder Michael Hart has tapped into a volunteer spirit that 

drives most open source projects. 

Encoding and editorial standards remain expensive, and the more encoding to be added 

and editing performed, the higher the level of expertise about the original document and/

or the technology for searching and display is required. Projects such as the Women 

Writers Online project at Brown University and the Model Editions Partnership provide 

extensive documentation about editorial standards and practices. Other projects listed on 

the TEI Consortium website provide examples of equally detailed documentation and 

exacting editorial standards. 

Some projects have avoided this additional cost by converting page images to text using 

optical character recognition (OCR) software, and allowing readers to perform keyword 

searches against the unedited and unencoded text files. The results are displayed as digital 

images of the original page, with the uncorrected text hidden. While imperfections 

impede precise and complete results from keyword searches, proponents believe that with 

adequate OCR accuracy, the results will still prove useful, and at a significantly lower 

overall cost to produce than highly accurate transcriptions. Some extremely large e-text 

collections with tens of thousand of volumes and millions of pages, such as the Making of 

America and American Memory collections, are based upon this idea, pioneered by John 

P. Wilkin, called "rough OCR" or "dirty OCR." 

Several people have pursued the idea of creating an archive of works containing 

manuscripts and variant editions, meant for both sophisticated researchers and general 

readers. In this type of publication, researchers would have access to all manuscript 

versions, allowing them to trace the development of a work through its variants, while a 

general audience could use a preferred reading created by the editors and derived directly 

from the sources. This kind of archive, proposed for authors such as Yeats, Hardy, and 

others, would be a boon to both audiences, but has proven to be very difficult. Outside of 

a few notable exceptions such as the Canterbury Tales Project, this idea has rarely been 

realized, indicating the enormous commitment and difficult work required. 

A very different category is hypertext. Works of this type are generally original and not 

representations of previously published works. Hypertext would seem to have great 

potential for expression, allowing for a multiplicity of narrative choices and making the 

reader an active participant in the reading experience. Hypertextual content is generally 

bound inextricably with hypertext software, making productions in this format even more 

ephemeral than other kinds of electronic text. Some hypertext authors, such as Michael 

Joyce and Stuart Mouthrop, recognize the impermanence inherent in the genre and 

incorporate it into their hyperfictions, but this fundamental aspect of its nature will make 

it difficult or impossible for libraries to collect hyperfictions for the long term. 

Eventually, even if the medium on which the hypertext is stored remains viable, the 

software on which it relies will no longer run. While early critics such as Robert Coover 

believed (with some hope) that hypertext would lead to "the end of books", others, such 

as Tim Parks, dismiss the genre as "hype." 

Creating Electronic Text 

Scholars, students, librarians, computing professionals, and general enthusiasts of all 

kinds create and publish electronic texts. Commercial publishers, notably those 

previously known for publishing microfilm collections, are digitizing and licensing

access to ambitiously large collections of tens of thousands of volumes. A few projects, 

such as the University of Virginia's collaboration with Chadwyck-Healey to publish 

Early American Fiction, and the Text Creation Partnerships formed for Early English 

Books Online and the Evans Early American Imprints, are examples of partnerships 

between libraries and publishers. 

How do humanities scholars and students approach the creation of electronic texts? One 

axis stretches from a traditional humanities approach, taking time and great pains to 

create well-considered, well-documented, well-edited electronic works, such as the 

Women Writers Online project at Brown University, the archive of Henrik Ibsen's 

writings, sponsored by the National Library of Norway and hosted by the universities of 

Oslo and Bergen, and the archive of Sir Isaac Newton's manuscripts at Imperial College, 

London, to less rigorous efforts, relying on volunteers and enthusiasts, with the bestknown 

examples being Project Gutenberg and Distributed Proofreaders. This difference 

in approaches mirrors various editorial approaches in the past century, with the same 

difference occurring between scholarly editions and reading editions, each with different 

purposes and audiences. For some scholars, the latter type suffices for classroom use or 

quick consultation, while for others, the extensive documentation and editorial standards 

of the former are of paramount importance. 

Interestingly, skepticism may play a central role in either attitude. On the one hand, 

Project Gutenberg enthusiasts, most notably project leader Michael Hart, express 

skepticism about the added software and expertise needed to create and read encoded 

electronic texts. His concerns, forged by painful hardware and software changes 

beginning in the mainframe era, are founded in the belief that the lowest common 

denominator of encoding will ensure the largest readership and longevity of the texts. 

Others, equally concerned about longevity and the demands created by changing 

hardware and software, believe that encoding systems based on international standards 

such as SGML, XML, and Unicode allow for the best representation of complex textual 

structures and writing systems, and these formats will prove most useful for scholars and 

general readers, as well as being the most promising means for the long-term archiving of 

these works. It should be noted that even some Project Gutenberg texts are available in 

HTML, PDF, or Microsoft Ebook reader formats, presumably for the added functionality 

or characters available beyond plain ASCII. Still, both sides view the other with 

suspicion. 

High-cost equipment is not required for creating electronic text – it can be achieved with 

the simplest of computers and word processing software. Scanning equipment and OCR 

software continue to drop in price. However, our expectations of accuracy in printed texts 

are high – we are generally disappointed to find any errors, and certainly disappointed if 

we discover an error every 10 to 20 pages. An accurate transcription requires skill and 

concentration, either in keying it in or in proofreading, yet the standard acceptable 

accuracy rate used by many large e-text projects is 99.995 percent, or 1 error in every 

20,000 characters. Given that an average page has 1,000–2,000 characters, this works out 

to 1 error every 10 to 20 pages. Spellcheckers, while useful in catching some 

misspellings, cannot catch typographic errors or inaccurate OCR that result in correctly

spelled words, and they are also little help with dialect or creative misspellings that 

authors employ. Spellcheckers exist for only a limited number of languages as well, 

making them generally not useful for the work of correcting electronic texts. Instead, it is 

common for large projects to outsource the creation of electronic text to vendors. Many 

of these vendors use a technique whereby two or three typists work on the same text. The 

typists' work is collated, with any discrepancies between the versions being used to find 

errors. This method produces a high accuracy rate, because the chance of two or three 

typists making the same error in the same spot in a text is statistically low. 

Using Electronic Text 

The use of online collections, even collections of relatively obscure writers, remains 

surprisingly high. As noted above, one group overlooked by most early computing 

humanists is the general reader, someone willing and interested to read something online, 

remarkable considering the relatively primitive displays, unsophisticated interfaces, and 

slow connections available. These general readers may have many different reasons for 

using electronic texts – they may lack access to research libraries; they may prefer to find 

and use books on their computers, in their homes or offices; they may live in countries 

without access to good collections in foreign languages; the books themselves may be 

fairly rare and physically available at only a handful of libraries. Whatever the motivation 

may be, these readers are finding and using electronic texts via the World Wide Web. 

Jerome McGann, noted scholar, editor, theorist, and creator of the Dante Gabriel Rossetti 

Archives, places much more value on this aspect of access than does Hockey. A great 

change has swept through literary studies in the past twenty years, as scholars re-evaluate 

writers and works overlooked by previous generations of literary critics. The immediate 

challenge for anyone interested in re-evaluating a little-known text will be to find a copy, 

for it may be available in only a few libraries. For McGann, collections of electronic texts 

may be most valuable for holding works by those writers who are less well known, or 

who are considered minor. In analyzing the Chadwyck-Healey English Poetry Database, 

McGann explains it this way: 

For research purposes the database grows less and less useful for those authors who 

would be regarded, by traditional measures, as the more or the most important writers. It's 

most useful for so-called minor writers. This paradox comes about for two reasons. On 

one hand, the poetical works of "minor" writers are often hard to obtain since they exist 

only in early editions, which are typically rare and can be quite expensive. By providing 

electronic texts of those hard-to-acquire books, "The English Poetry Database" supplies 

scholars with important primary materials. On the other hand, the policy of the Database 

is – wherever possible – to print from collected editions of the poets as such editions 

exist. The better-known the poet, the more likely there will be collected edition(s) …. 

The Database would have done much better to have printed first editions of most of its 

authors, or at any rate to have made its determinations about editions on scholastic rather 

than economic grounds. But it did not do this.

[…] Speaking for myself, I now use the Database in only two kinds of operation: as a 

vast concordance, and as an initial source for texts that we don't have in our library. In the 

latter case I still have to find a proper text of the work I am dealing with. 

(McGann 1995: 382–3) 

The initial hindrances to reading works by lesser-known writers, perhaps insurmountable 

in the past, can be much more easily overcome in this new medium. It should be noted 

that even such a digitally adept scholar as McGann prefers printed sources over electronic 

versions (or at least, still did in 1996). He used the online collection for discovery and 

research, but turned to the printed copy for verification and citation. 

Many problems face a scholar wishing to use electronic texts for research. The immediate 

problem is in discovering just what is available. Traditionally, a researcher would check 

the library catalogue to discover whether a particular title or edition is available in the 

stacks. Given the growing number of online collections, it is impossible to be aware of all 

relevant sources for any given research topic, and even the specialized portals such as 

Voice of the Shuttle have fallen behind. As it stands now for most online projects, 

researchers must remember that a particular website has works by Charles Dickens or 

Margaret Oliphant to find electronic editions by these authors, for they may be found 

neither in online catalogues nor by using search tools such as Google. Libraries could 

have records for electronic texts in their online catalogues also and link directly to the 

electronic editions. However, few libraries have included records for all titles available 

in, for instance, the English Poetry Database from Chadwyck-Healey even if they have 

acquired the full-text database for their communities. This situation is worse for those 

texts that are freely available, for the job of discovering and evaluating electronic texts, 

and then creating and maintaining links to them, is overwhelming. 

There is a great deal of interest in improving this situation. Libraries are beginning to 

include records for electronic texts in their online catalogues. Other developments, such 

as the Open Archives Initiative, could allow the discovery of the existence of electronic 

texts much more readily than at present. Methods are under development for searching 

across collections stored and maintained at different institutions, meaning that someone 

interested in nineteenth-century American history could perform one search that would 

be broadcast to the many sites with collections from this period, with results collected 

and presented in a single interface. 

Another problem is the artificial divide that exists between those collections available 

from commercial publishers and those that are locally created. Generally, these two 

categories of materials use different interfaces and search systems. This distinction is 

completely irrelevant to researchers and students, but systems and interfaces that allow 

for searching both categories of materials simultaneously and seamlessly, while available, 

are still rare, due to the complexities of authentication and authorization. 

Another type of divide exists as well. With the growing body of collections available 

from commercial publishers, the divide between the haves and have-nots in this area is

growing. At a recent conference on nineteenth-century American literature, it was notable 

that graduate students at research universities had access to a wide range of commercially 

published electronic text collections, while many of their colleagues, recently graduated 

with first jobs at smaller institutions, did not. These untenured scholars may not need to 

travel to institutions to see original documents any more, but they will continue to need 

support to travel to institutions that have access to licensed collections of electronic texts. 

There is hope for these scholars, however, as libraries and museums digitize wider 

portions of their collections and make them publicly available. 

A much more complex problem is the limited range of electronic texts that are available. 

A crazy patchwork quilt awaits any researcher or reader willing to use electronic texts, 

and as McGann points out, the selection of proper editions may not be given much 

thought. The situation resembles a land rush, as publishers, libraries, and individuals seek 

to publish significant collections. The number of freely available texts, from projects such 

as Making of America, to the Wright American Fiction project, to the University of 

Virginia E-Text Center, to the Library of Congress's American Memory, is growing at a 

phenomenal pace. 

Commercial publishers are now digitizing large microfilm collections such as Pollard and 

Redgrave, and Wing (Early English Books, published by Bell and Howell/UMI), and 

Evans (Early American Imprints, by Readex), and Primary Source Media has recently 

announced its intention to digitize the massive Eighteenth Century collection. The 

English Poetry Database, first published in 1992 and one of the earliest efforts, used the 

New Cambridge Bibliography of English Literature, first published in 1966 based on an 

earlier edition from the 1940s, as the basis for inclusion. The collections listed above are 

based upon bibliographies begun in the 1930s. Those collections of early literature such 

as the Early English Books Online can claim comprehensive coverage of every known 

publication from Great Britain before 1700, but as these online collections include 

publications closer to the present time, the less inclusive they can be, given the 

exponential growth of publishing. Thus, large collections such as the English Poetry 

Database have to employ selection criteria. Critics have debated these traditional 

collections, known informally as the "canon", and argued for broader inclusion of women 

or authors in popular genres. These electronic collections, while very large and inclusive, 

reflect the values and selection criteria of fifty years ago or more. 

Some libraries are developing their own digital collections, and McGann suggests it is no 

coincidence. He sees in literary studies two movements: a return to a "bibliographical 

center" and a "return to history": 

The imperatives driving libraries and museums toward greater computerization are not 

the same as those that have brought the now well-known "return to history" in literary 

and humanities scholarship. Nevertheless, a convergence of the twain has come about, 

and now the two movements – the computerization of the archives, and the rehistoricization 

of scholarship – are continually stimulating each other toward new 

ventures.

(McGann 1995: 380) 

Even with this phenomenal growth, one realizes immediately the inadequacy of using 

these collections for comprehensive research. Scholars still cannot assume that their 

fields have an adequate collection of electronic texts available, nor can they assume that 

those collections that do exist will reflect current thinking about inclusion. McGann 

stated in 1996, something still true, "the Net has not accumulated those bodies of content 

that we need if we are to do our work" (p. 382). The collection size of a research library 

numbers in millions of volumes. The size of the collective electronic text collections 

available through both commercial publishers and freely available websites probably 

exceeds 200,000, but not by much. Scholars, students, and librarians are learning that the 

collections will have to grow considerably in order to reliably meet the needs of a broad 

range of humanists. 

As McGann states, these collections and editions were chosen in large part because they 

are in the public domain and free of copyright restrictions. The commercial publishers 

listed above started as microfilm publishers, and their microfilm collections were formed 

by the same principle. Copyright is the hidden force behind most electronic text 

collections. Very few electronic text collections, even those from commercial publishers, 

contain publications under copyright. This has two main effects on electronic collections. 

First and foremost, it means that most digital collections consist of authors who lived and 

published up to the twentieth century; the works of writers after that may still be under 

copyright, and therefore more difficult and perhaps expensive to copy and republish. 

Second, new editions of these pre-twentieth-century writers are generally excluded, with 

projects and publishers selecting those in the public domain. Finally, contemporary works 

of literary criticism, biography, and theory, that could provide needed context and 

interpretation to the primary literature, also remain largely excluded. The possibilities 

inherent in the medium, for providing a rich context for the study of primary literary texts 

and historical documents, have not yet been realized. 

The effect of copyright means that researchers and students interested in twentiethcentury 

and contemporary writing are largely prevented from using electronic text. A 

quick check of the online MLA Bibliography shows the number of articles published after 

1962 dealing with twentieth-century authors is nearly double that of all other centuries 

combined, covering 1200–1900 ce. With the majority of researchers and students 

interested in writers and their work after 1900, it is no wonder that they may consider 

electronic text largely irrelevant to their studies. 

In addition, many of the markers traditionally used by scholars to determine the merit of 

any given electronic text are missing. There may be no recognizable publisher, no editor, 

no preface or statement of editorial principles, which may cause scholars to shy away. 

They are left to their own devices to judge the value of many resources. On the other 

hand, in this unfamiliar terrain, there may be a tendency to trust the technology in the 

absence of such markers. Electronic text collections do not receive reviews as frequently 

as those published commercially, perhaps for some of these same reasons, although they 

may receive more use. Even in those reviews, one notes an uncritical trust on the part of

eviewers. For instance, I assert on the website of the Victorian Women Writers Project 

(VWWP) that it is "devoted to highly accurate transcriptions" of the texts. I am 

alternately amused and alarmed to see that phrase quoted verbatim in reviews and 

websites that link to the VWWP (e.g., Burrows 1999: 155; Hanson 1998; McDermott 

2001) without any test of its accuracy. Scholars who use these collections are generally 

appreciative of the effort required to create these online resources and reluctant to 

criticize, but one senses that these resources will not achieve wider acceptance until they 

are more rigorously and systematically reviewed. 

It is difficult to find scholarly articles that cite electronic text collections as sources, or 

discuss the methodology of creating or using e-texts, outside of journals for computing 

humanists. Humanists have been slow to accept electronic texts for serious research, for 

the reasons explained above, particularly their scattershot availability, and the inadequate 

documentation of sources and editorial practices used in creating them. In an article taken 

from a major scholarly journal almost at random (Knowles 2001), the author investigates 

the use of the word "patriot" throughout the seventeenth century. He cites familiar 

authors such as Milton, Dryden, Bacon, Jonson, Sir Winston Churchill, as well as less 

familiar authors. Electronic text collections such as Chadwyck-Healey's English Poetry 

Database or ProQuest's Early English Books Online would be perfectly suited for this 

purpose – McGann's "vast concordance", with the ability to search across thousands of 

works, leading to both familiar and unfamiliar works containing the term. However, 

Knowles does not mention these, although it is possible that he used them behind the 

scenes. At any rate, it is rare to find them cited in scholarly articles or books, and thus 

their use and importance goes unnoticed and untested. Scholars need to hear more from 

their peers about the use of these resources in their research. 

Electronic texts have an important place in classroom instruction as well. Julia Flanders 

points to a survey conducted by Women Writers Online in which a majority of professors 

responded that they "were more likely to use electronic tools in their teaching than in 

their research" (Flanders 2001: 57). The same problems described above would apply, 

but she posits that for students, unfamiliarity is an opportunity rather than an obstacle, 

giving them the chance to interact directly with the texts independent of contextual 

information and prior interpretation. The form of interaction offered by e-texts, with the 

opportunity to quickly explore words and terms across a large body of works, is vastly 

different than that offered by print texts. This approach to literary studies, closely related 

to traditional philology developed in the nineteenth century, is more common in classical 

studies, and may account for the successful adoption of computers and digital resources 

among classicists and medievalists in comparison to those who study and teach later 

periods. 

One assumption underlying much of the criticism of Olsen and Finch noted above is that 

the computer will in some way aid in literary criticism. Beginning with Father Busa, up 

to the creation of the World Wide Web, computer-aided analysis was the entire purpose 

of electronic texts, as there was no simple way for people to share their works. More 

recent books (Sutherland 1997; Hockey 2000) have shifted focus to the methods needed 

to create and publish electronic texts, leaving aside any discussion of their eventual use.

Still, Hockey and others point to the inadequacies of the tools available as another 

limitation to wider scholarly acceptance of electronic texts. Early electronic texts, such as 

the WordCruncher collection of texts, were published on CD-ROM along with tools for 

their use. While there are a multitude of problems associated with this kind of bundling, it 

certainly made entrance to use very easy. Today, many collections of electronic texts 

contain tools for simple navigation and keyword searching, but little else. As Hockey 

notes (2000: 167), "[c]omplex tools are needed, but these tools must also be easy for the 

beginner to use", which explains in part why few exist. Another researcher, Jon K. 

Adams, writes: 

Like many researchers, I have found the computer both a fascinating and frustrating tool. 

This fascination and frustration, at least in my case, seems to stem from a common 

source: we should be able to do much more with the computer in our research on literary 

texts than we actually do. 

(Adams 2000: 171) 

In some sense, while the World Wide Web has sparked development and distribution of 

electronic texts in numbers unthinkable before it, the tools available for using these texts 

are of lesser functionality than those available through the previous means of publishing, 

such as CD-ROMs. This is perhaps due to the lack of consensus about the uses of 

electronic texts, as well as the difficulty of creating generalized text analysis tools for use 

across a wide range of collections. The acceptance of more sophisticated automated 

analysis will remain limited until more sophisticated tools become more widely available. 

Until then, the most common activities will be fairly simple keyword searches, and text 

retrieval and discovery. These types of research can be powerful and important for many 

scholars, but do not begin to tap the potential of humanities computing. 

Still, even noting their inadequacies, these collections are finding audiences. The E-text 

Center at the University of Virginia reports that their collections receive millions of uses 

every year. Making of America at the University of Michigan reports similar use. The 

Victorian Women Writers Project (VWWP), which contains about 200 works by lesserknown 

nineteenth-century British women writers, receives over 500,000 uses per year. 

The most popular works in the VWWP collection include such books as Caroline 

Norton's study English Laws for Women, Vernon Lee's gothic stories Hauntings, and 

Isabella Bird's travel writings. These writers are better known than most of the others in 

the collection. Nevertheless, the most heavily used work in the collection is Maud and 

Eliza Keary's Enchanted Tulips and Other Verse for Children, which receives thousands 

of uses per month. This is one indication that scholars are not the only audience for 

electronic text. In the end, the general reader, students, and elementary and secondary 

school teachers, particularly those in developing countries without access to good 

libraries, may be the largest and most eager audience for these works. 

Electronic texts give humanists access to works previously difficult to find, both in terms 

of locating entire works, with the Internet as a distributed interconnected library, and in 

access to the terms and keywords within the works themselves, as a first step in analysis.

As reliable and accurate collections grow, and as humanists come to understand their 

scope and limitations, the use of e-texts will become recognized as a standard first step in 

humanities research. As tools for the use of e-texts improve, humanists will integrate etexts 

more deeply and broadly into their subsequent research steps. Until then, electronic 

texts will remain, in Jerome McGann's words, a "vast concordance" and library with great 

potential. 

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19. 

Modeling: A Study in Words and Meanings 

Willard McCarty 

Out on site, you were never parted from your plans. They were your Bible. They got dogeared, 

yellowed, smeared with mud, peppered with little holes from where you had 

unrolled them on the ground. But although so sacred, the plans were only the start. Once 

you got out there on the site everything was different. No matter how carefully done, the

plans could not foresee the variables. It was always interesting, this moment when you 

saw for the first time the actual site rather than the idealised drawings of it. 

He knew men who hated the variables. They had their plans and by golly they were 

going to stick to them. If the site did not match the drawings it was like a personal insult. 

He himself liked the variables best. He liked the way that the solution to one problem 

created another problem further down the line, so that you had to think up something 

else, and that in turn created another problem to solve. It was an exchange, backwards 

and forwards. Some men thought of it as a war, but to him it was more like a 

conversation. 

Kate Grenville, The Idea of Perfection (Sydney: Picador, 1999): 62–3 

Introduction 

The question of modeling arises naturally for humanities computing from the prior 

question of what its practitioners across the disciplines have in common. What are they 

all doing with their computers that we might find in their diverse activities indications of 

a coherent or cohesible practice? How do we make the best, most productive sense of 

what we observe? There are, of course, many answers: practice varies from person to 

person, from project to project, and ways of construing it perhaps vary even more. In this 

chapter I argue for modeling as a model of such a practice. I have three confluent goals: 

to identify humanities computing with an intellectual ground shared by the older 

disciplines, so that we may say how and to what extent our field is of as well as in the 

humanities, how it draws from and adds to them; at the same time to reflect experience 

with computers "in the wild"; and to aim at the most challenging problems, and so the 

most intellectually rewarding future now imaginable. 

My primary concern here is, as Confucius almost said, that we use the correct word for 

the activity we share lest our practice go awry for want of understanding (Analects 13.3). 

Several words are on offer. By what might be called a moral philology I examine them, 

arguing for the most popular of these, "modeling." The nominal form, "model", is of 

course very useful and even more popular, but for reasons I will adduce, its primary 

virtue is that properly defined it defaults to the present participle, its semantic lemma. 

Before getting to the philology I discuss modeling in the light of the available literature 

and then consider the strong and learned complaints about the term. 

Background 

Let me begin with provisional definitions 1 . By "modeling" I mean the heuristic process of 

constructing and manipulating models', a "model" I take to be either a representation of 

something for purposes of study, or a design for realizing something new. These two 

senses follow Clifford Geertz's analytic distinction between a denotative "model of" such 

as a grammar describing the features of a language, and an exemplary "model for" such

as an architectural plan (Geertz 1973: 93) 2 . In both cases, as the literature consistently 

emphasizes, a model is by nature a simplified and therefore fictional or idealized 

representation, often taking quite a rough-and-ready form: hence the term "tinker toy" 

model from physics, accurately suggesting play, relative crudity, and heuristic purpose 

(Cartwright 1983: 158). By nature modeling defines a ternary relationship in which it 

mediates epistemologically, between modeler and modeled, researcher and data or theory 

and the world (Morrison and Morgan 1999). Since modeling is fundamentally relational, 

the same object may in different contexts play either role: thus, e.g., the grammar may 

function prescriptively, as a model for correct usage, the architectural plan descriptively, 

as a model of an existing style. The distinction also reaches its vanishing point in the 

convergent purposes of modeling: the model of exists to tell us that we do not know, the 

model for to give us what we do not yet have. Models realize. 

Perhaps the first question to ask is what such a process has to do with computing, since as 

the examples suggest neither of the two senses of "model" assumes it unless the 

definition is further qualified. In history, for example, Gordon Leff has argued that 

models have always been implicit in scholarly practice (Leff 1972). Leff cites, e.g., the 

historic-graphical notion of "epoch", but any well-articulated idea would qualify as a 

model of its subject. Nevertheless, Leff notes that as M. I. Finley said in Ancient History: 

Evidence and Models, "model-construction is rare among all but economic historians"; 

Finley recommends Max Weber's parallel concept of "ideal types", which "expresses 

clearly the nature and function of models in historical inquiry" (1986: 60f). Explicit 

model-construction is still rare in mainstream humanities scholarship. Even for noncomputational 

research in the social sciences, it is more common, as Finley's demarcation 

suggests. For example, political schemes by nature model for a better or at least different 

world, even if like Marx's historiography they begin as models of it; delineating them as 

models is the scholar's obvious work (Mironesco 2002). Nevertheless, outside 

computationally affected scholarly practice Marvin Minsky's simple, straightforward 

definition remains alien in style and in thought: "To an observer B, an object A* is a 

model of an object A to the extent that B can use A* to answer questions that interest him 

about A" (Minsky 1995). 

A strong temptation for us here is to dismiss the residual alienness of Minsky's 

formulation and to accept, as we have accepted computing, the reified, explicit "model" 

of Minsky's definition as what we really have been doing all along. This would, however, 

be a serious error. As with the relationship of hypertext to earlier ways of referring 

(McCarty 2002), the new form of expression, with its vocabulary and tools, means an 

altered way of thinking. A historical imagination is required to see what this means. 

Two effects of computing make the distinction between "idea" or other sort of mental 

construct on the one hand, and on the other "model" in the sense we require: first, the 

demand for computational tractability, i.e., for complete explicitness and absolute 

consistency; second, the manipulability that a computational representation provides. 

The first effects a sea-change by forcing us to confront the radical difference between 

what we know and what we can specify computationally, leading to the epistemological

question of how we know what we know. On the one hand, as Michael Polanyi observed, 

"we can know more than we can tell" (1966: 4–5). Computational form, which accepts 

only that which can be told explicitly and precisely, is thus radically inadequate for 

representing the full range of knowledge – hence useful for isolating the tacit or inchoate 

kinds. On the other hand, we need to trust what we somehow know, at least provisionally, 

in order not to lose all that goes without saying or cannot be said in computational form. 

Take, for example, knowledge one might have of a particular thematic concentration in a 

deeply familiar work of literature. In modeling one begins by privileging this knowledge, 

however wrong it might later turn out to be, then building a computational representation 

of it, e.g., by specifying a structured vocabulary of word-forms in a text-analysis tool. In 

the initial stages of use, this model would be almost certain to reveal trivial errors of 

omission and commission. Gradually, however, through perfective iteration trivial error 

is replaced by meaningful surprise. There are in general two ways in which a model may 

violate expectations and so surprise us: either by a success we cannot explain, e.g., 

finding an occurrence where it should not be; or by a likewise inexplicable failure, e.g., 

not finding one where it is otherwise clearly present. In both cases modeling 

problematizes. As a tool of research, then, modeling succeeds intellectually when it 

results in failure, either directly within the model itself or indirectly through ideas it 

shows to be inadequate. This failure, in the sense of expectations violated, is, as we will 

see, fundamental to modeling. 

The second quality of "model" that distinguishes it from "idea" is manipulability, i.e., the 

capability of being handled, managed, worked, or treated by manual and, by extension, 

any mechanical means (OED: la.). Change is common to both models and ideas, but at 

greater or lesser metaphorical distance, "model" denotes a concrete, articulated plan 

inviting the etymological sense of action-by-hand (L. manus) in response. Manipulation 

in turn requires something that can be handled (physical objects, diagrams, or symbols of 

a formal language) – and a time-frame sufficiently brief that the emphasis falls on the 

process rather than its product. In other words, the modeling system must be interactive. 

Manipulable objects from the physical to the metaphorical have characterized 

mathematics, engineering, the physical sciences, and the arts ab wo, but with exceptions 

the necessary time-frame, allowing for interactivity, has been possible only with 

computing. With its advent, Minsky has noted, models could be "conceived, tested, and 

discarded in days or weeks instead of years" (1991). Computing met research easily in 

fields where modeling was already an explicit method because, Brian Cantwell Smith has 

pointed out, models are fundamental to computing: to do anything useful at all a 

computer must have a model of something, real or imaginary, in software. But in the 

context of computing, models per se are not the point. What distinguishes computers 

from other kinds of machines, Smith notes, is that "they run by manipulating 

representations, and representations are always formulated in terms of models" (Smith 

1995/1985: 460; cf. Fetzer 1999: 23). 

In other words, computational models, however finely perfected, are better understood as 

temporary states in a process of coming to know rather than fixed structures of 

knowledge. It is of course possible to argue ideologically, as some still do, that we are

converging on and will achieve such structures, 3 but in any case these structures would 

not then be models and would no longer have reason to exist in software. (Note that the 

history of computing is the story of ever more complex and extensive software, not less, 

despite the fact that implementations in hardware are faster and can be cheaper.) For the 

moment and the foreseeable future, then, computers are essentially modeling machines, 

not knowledge jukeboxes. To think of them as the latter is profoundly to misunderstand 

human knowledge – and so to constrict it to the narrow scope of the achievably 

mechanical. 

In analytical terms, as I have suggested, modeling has two phases: first, construction; 

second, manipulation. Examples come readily to mind from ordinary technical practice, 

e.g., building a relational database, then querying the data thus shaped to explore 

emergent patterns. As experience with databases shows, the two phases often blur into 

each other especially in the early stages when use uncovers faults or suggests 

improvements that direct redesign. A model 0/and a model for may be distinct types – 

because in our terms they are fixed objects. But modeling of something readily turns into 

modeling for better or more detailed knowledge of it; similarly, the knowledge gained 

from realizing a model for something feeds or can feed into an improved version. This 

characteristic blurring of design into use and use into (re)design is what denies modeling 

of any sense of closure. Modeling for, Utopian by definition, is denied it in any case. 

Learned Complaints 

So far so good – but at the cost of averting our gaze from the problems with the word 

"model." Indeed, the extensive and growing literature on the topic may seem adrift in a 

hopeless muddle. "I know of no model of a model", physicist H. J. Groenewold declared 

many years ago (1960: 98). The philosopher Peter Achinstein has warned us away even 

from attempting a systematic theory (1968: 203). The word itself is indeed astonishingly 

polysemous – or promiscuous, as Nelson Goodman puts it. "Model", he complains, can 

be used to denote "almost anything from a naked blonde to a quadratic equation" (1976: 

171). Nevertheless, the word is often used as if its semantic complexity either did not 

exist or could be safely ignored. The muddle of partly overlapping, partly contradictory 

senses is proof enough that we ignore it at our peril. Nor can we simply avoid the 

problem by dismissing "model" altogether, as Goodman and others recommend, without 

(as I will argue) hobbling our ability to understand inter alia those aspects of computing 

most important to research – one might even say, as I do, its essence. Despite several 

other, supposedly less confusing terms on offer, the word remains stubbornly popular in 

the literature of the social and physical sciences, the history and philosophy of science, 

cognitive science, artificial intelligence, and related areas. 

Theoretical physicist John Ziman and philosopher Stephen Toulmin, for example, 

recommend "map" on the basis of its conceptual clarity and fitness for describing the 

relationship between theoretical knowledge and reality (Ziman 2000: 126–38, 147–50; 

Toulmin 1953: 94–109). Goodman would have us collapse modeling into diagramming, 

which he thinks less troublesome (1976: 171–3). But preference needs to be based on 

more than such criteria; "map", for example, serves experiment much less well than

theory, as I will show. We require a close and careful look at the semantic fields of all 

major alternatives, including "map", for their disjunctions and overlaps. We need to 

scrutinize each of these, asking what does it denote and connote that the others do not, 

and vice versa? What do all have in common? What are their individual tendencies of 

mind, and which of these best suits computing as we are learning to conceive it? 

Philological Analysis of Related Terms 

So far I have used the term "model" as the default, partly for purposes of convenience, 

partly because, as I argue, it is right for the job. To answer the learned criticisms and 

further clarify our topic, however, I propose to question it by comparison against the 

major alternatives: "analogy", "representation", "diagram", "map", "simulation", and 

"experiment." As we have seen, the two decisive criteria are that the thing named by the 

chosen term be computationally tractable and manipulable. Tractability in turn requires 

complete explicitness and absolute consistency; manipulability resolves into mechanical 

action and interactivity. Hence the term must denote a continual process of coming to 

know, not an achievement but an approximation. As I have argued, it is from the 

difference between the approximation and the reality approximated – which ultimately 

for the humanities is our humanly known apprehension of that reality – that we learn. 

For each of the alternative terms I ask whether and to what degree the word normally 

denotes a dynamic process and whether it refers to a concrete, i.e. manipulable, form – 

the requirements of anything whose function is fulfilled through being changed. 

Corresponding to the two senses of "model" I identified earlier, the denotative model of 

and the exemplary model for, I also ask whether each word tends to the mimetic 

(imitation) or proleptic (anticipation). The distinction helps in determining whether the 

action denoted by a term may be said to be bounded, either by a fixed form, as is the case 

with "analogy", or by an inherent tendency to reach a definitive or satisfactory 

conclusion, as in "representation." 

Thus bringing "model" into focus against the semantic background of these other terms 

will show that the problem has not so much been too many meanings for "model" as use 

without regard to any of them, often as if the sense of it simply goes without saying. It 

doesn't. But perhaps the most important lesson we learn from seeing the word in the 

context of its synonym set is not the range and variety of its meanings; rather, again, its 

strongly dynamic potential. Apart from the popularity of "model" taken at face-value, the 

word would have little to recommend it (and, as the complainers say, much against it) but 

for the open-ended present-participial strength of "modeling." 4 Indeed, the manifest 

confusion in the literature on the topic may be primarily due to a mistaken preference for 

the noun – as if getting a model right, and so promoting it to the status of theory, were the 

point. Modeling has an entirely different role to play. There are several better terms if 

what one wants is to name a stable conceptualization.

Analogy 

"Analogy" (Gk. ναλoγ α, "equality of ratios, proportion") is, like "model", a highly 

polysemous term with a long and complex career 5 . John Stuart Mill complained that "no 

word … is used more loosely, or in a greater variety of senses, than Analogy" (A System 

of Logic, 1882). Yet Dr Johnson's pithy definition, "resemblance of things with regard to 

some circumstances or effects", and Mill's even pithier one, "resemblance of relations", 

give us an idea of why it is so fruitful. From its original meaning in Greek mathematics, 

analogy specifies a structured relationship between pairs of related quantities, which for 

convenience may be represented in the form of an equation, "A/B = C/D", read "as A is 

to B, so C is to D." Extended beyond mathematics to other modes of reasoning, analogy 

yields a powerful (but still poorly understood) means of inferring from the one relation to 

the other. Like most of the words in our domain, "analogy" is proleptic, a means of 

inference, based on conjecture, to something unknown or uncertain. Examples in the 

history of science are plentiful, e.g., Kepler's discovery of the vis matrix, or cause of 

planetary motion, by reasoning that as the sun radiated light, so it must also radiate this 

motive power (Gentner 2002). 

Here I wish only to argue two points. The first is that analogy is basic to the entire 

vocabulary. Although not every model is as strictly based on an analogy as Kepler's, 

modeling is inherently analogical, with just the features that make the idea attractive for 

our purposes. Thus we require a structured correspondence between model and artifact, 

so that by playing with one we can infer facts about the other. (For example, by adjusting 

choice of words and weightings for a distribution-display across a textual corpus, one can 

investigate the effect of vocabulary on the interplay of meanings in that corpus.) The 

second point is that "analogy" is inherently static: it means either a type of relationship or 

an instance of one, never an object and not, literally or directly, a process. Action is 

implied in the ratio of quantities – thus Kepler's "as A does B, so C does D" – but acting 

is not denoted by the analogy. The word has no commonly used verbal form ("analogize" 

and "analogizing" are rare if not strange). Although an analogy may be algebraically or 

geometrically expressed and may refer to concrete objects, it itself is abstract. 

Because analogy works so well as a way of describing how we often think, efforts to 

understand acquisition of new knowledge tend to engage with theories of analogy and to 

propose many mechanisms, e.g., in cognitive science, educational theory, and artificial 

intelligence (Hoffman 1995). Because modeling is analogical, this work is potentially 

relevant to questions raised in computing the artifacts of the humanities. We need to pay 

attention here. 

Representation 

"Representation" in Nelson Goodman's terms is defined by a symbolic denotative 

correspondence, not likeness or imitation (1976: 3–41) 6 . In a less philosophically precise 

sense, however, we may say that "representation" displays strong mimetic tendencies, 

e.g., in the definition given by the OED: "An image, likeness, or reproduction in some 

manner of a thing …. A material image or figure; a reproduction in some material or

tangible form; in later use esp. a drawing or painting (of a person or thing)." The history 

of aesthetics from earliest times, in fits and starts of fashion, demonstrates that the copytheory 

of representation, though its falsity names its achievement in a trompe I'oeil, 

remains a habit of mind. If in aesthetics, why not in computer science? 

A well-attested present participle and a full complement of verbal forms establishes the 

action of representing, but semantically this action is bounded by its relationship to the 

represented object, whether this be symbolic or imitative. 

As with "analogy", the semantic fields of "model" and "representation" clearly overlap, 

but the situation is more complex because of the mimetic and proleptic kinds of "model." 

Hence (Platonism aside) we may say that modeling of is representational but not 

modeling for. In fact a model of is a manipulable variety of representation – which any 

representation in software would of course be. The crucial difference between model 

0/and representation is the quality of the action implied. Unlike representing, modeling of 

is denied closure, as I noted earlier. It has no satisfactory trompe I'oeil or symbolizing 

conclusion. If the researcher calls a halt, then the last state of the system, as it were, is 

better called a "representation." 

In the context of computing, the meaning of "representation" is dominated by the subfield 

of artificial intelligence known as "knowledge representation" (KR). Given the scope of 

this essay I can do little more than make a few observations, chiefly about the 

assumptions built into the name and apparently into tendencies in some KR work. In 

brief, my argument concerning KR is that it needs to be understood as a particularly 

rigorous control on model-building suitable to that which can be stated in prepositional 

form. 7 

To the connotations of "representation" I have already reviewed, KR adds the demarcational 

term "knowledge." The point I wish to draw from current epistemology is a simple 

one, for which I quote Michael Williams – at length, because the issues are consequential 

for us: 

"Knowledge" is an honorific title we confer on our paradigm cognitive achievements …. 

More generally, "know" is a "success-term", like "win" or "pass" (a test). Knowledge is 

not just a factual state or condition but a particular normative status. Such statuses are 

related to appropriate factual states: winning depends on crossing the line before any 

other competitor. But they also depend on meeting certain norms or standards which 

define, not what you do do, but what you must or ought to do. To characterize someone's 

claim as expressing or not expressing knowledge is to pass judgement on it. Epistemic 

judgements are thus a particular kind of value-judgement …. 

This normative dimension distinguishes philosophical theories ofknowledge from 

straightforwardly factual inquiries and explains why demarcational (and related 

methodological) issues are so significant. Because epistemological distinctions are 

invidious, ideas about epistemological demarcation always involve putting some claims 

or methods above others: mathematics above empirical science, empirical science above

metaphysics or religion, logic above rhetoric, and so on. Demarcational projects use 

epistemological criteria to sort areas of discourse into factual and non-factual, truthseeking 

and merely expressive, and, at the extreme, meaningful and meaningless. Such 

projects amount to proposals for a map of culture: a guide to what forms of discourse are 

"serious" and what are not. Disputes about demarcation – induding disputes about 

whether demarcational projects should be countenanced at all – are disputes about the 

shape of our culture and so, in the end, of our lives. 

(2001: 11–12) 

Projects such as Cyc, based on what Northrop Frye characterized as the discredited 

Wissenscbaft-theory of knowledge – that its accumulation in vast quantities will one day, 

somehow, result in understanding 8 – clearly assume if not perfect closure, then a 

threshold beyond which lack of perfection ceases to matter. But to whom, and for what 

purposes? 9 Apart from such questions, and the serious doubts within computer science on 

the wisdom of building massive knowledge-bases for expert systems 10 – there are, again, 

the very serious demarcational issues. When, for example, one of the leading theorists of 

KR writes in passing that, "Perhaps there are some kinds of knowledge that cannot be 

expressed in logic" (Sowa 2000: 12), our intellectual claustrophobia tells an important 

tale. Not, of course, the only one. If the point of modeling is to fail well, then KR has a 

vital quality-control function to serve. 

Diagram 

A diagram (Gk. δι γραµµα, "that which is marked out by lines, a geometrical figure, 

written list, register, the gamut or scale in music") 11 is an analogical drawing, "a figure 

drawn in such a manner that the geometrical relations between the parts of the figure 

illustrate relations between other objects": thus the physicist James Clerk Maxwell on the 

graphic, symbolic, and hybrid kinds (1911). Such a diagram ranges from the precisely 

drawn schematic, whose measurements are significant, to the rough sketch intended to 

express symbolic relations only. But what makes a graphic a diagram, properly so called, 

is the way in which it is read, not its resemblance to anything 12 . Reviel Netz argues the 

point for the lettered diagram in Greek mathematical texts: "It is only the diagram 

perceived in a certain way which may function alongside the text" – irrelevant 

discrepancies are overlooked, the important matters denoted by the lettering: "All 

attention is fixed upon the few intersecting points, which are named" (1999: 33–5). Even 

when the diagrammed entity is a physical object, the diagram represents structure and 

interrelation of essential parts, foregrounding interpretative choice and conscious 

purpose. The ability to manipulate structures and parts may be implied. 

The word "diagram" doubles as noun and verb and has a full range of verbal inflections. 

Like "represent", its action is bounded, but more by ideas than appearance, even when 

that appearance is precisely delineated. As Maxwell notes for physics, diagrams often 

represent force or movement, even if only implicitly, though the form is static. As a 

means of communication, e.g., in a lecture or discussion between collaborators,

diagramming is the point, not the static trace left behind. That trace may in fact be 

unintelligible apart from the discussion of which it was a dynamic part. 

The focus on ideas rather than things per se, the role of manipulation where diagramming 

is heuristic and the kinaesthetics of the action suggest the close relationship between 

diagramming and modeling for which Goodman argues. Models of, he declares, "are in 

effect diagrams, often in more than two dimensions and with working parts … [and] 

diagrams are flat and static models" (1972: 172–3). Nevertheless, the two-dimensionally 

graphic, geometrical, and finally static qualities of the diagram define it, not "model", 

which has considerably broader applications. 

Sun-Joo Shin and Oliver Lemon note that although diagramming is very likely one of the 

oldest forms of communication, modern logicians and philosophers have tended until 

recently to regard it as only of marginal importance. 13 That is changing very rapidly now. 

As a cognitive, reasoning process it is studied in relation to Greek mathematics and 

geometry in particular (Netz 1999). Modern philosophical attention can be traced from 

Descartes's "La Geometric" (1637) and Kant's Critique of Pure Reason II.1.1 (1781) to 

Peirce's "existential graphs" in the late nineteenth century, significantly as part of his 

much broader interest in scientific discovery, to which I will return. His work is now 

central to much current research. 

Shin and Lemon delineate three branches of research since the mid-1990s: (1) multimodal 

and especially non-linguistic reasoning, in the philosophy of mind and cognitive 

science; (2) logical equivalence of symbolic and diagrammatic systems, in logic; and (3) 

heterogeneous systems implementing theories of multi-modal reasoning, in computer 

science. The close relationship of diagramming and modeling make this research 

immediately relevant. 

Map 

A map may be defined as a schematic spatial representation, or following Maxwell, a 

diagram of "anything that can be spatially conceived." 14 Indeed, if not for the 

geographical focus of mapping, the semantic fields of "map" and "diagram" would 

completely overlap: both are fully verbal, their action bounded by a graphical 

representation that has both strongly mimetic and ideational aspects; both manipulate 

data for specific purposes and introduce fictional elements to serve these purposes. But 

the long history of mapping the physical world for exploration and description gives 

"map" specific (and evidently powerful) connotations. 

Mapping is particularly characteristic of an early, exploratory period, when a territory is 

unknown to its discoverers (or conquerors). Mapping constructs the world it represents, 

selectively, therefore shaping thought and guiding action. It orients the newcomer, giving 

him or her control of the mapped terrain, at the same time expressing, though perhaps 

covertly, a perspective, a set of interests, and a history. Mapping attaches meaning to 

place. Like modeling it can be either of or for a domain, either depicting the present

landscape or specifying its future – or altering how we think about it, e.g., by renaming 

its places. A map is never entirely neutral, politically or otherwise. 

As I have noted, John Ziman, following Stephen Toulmin, has argued persuasively for 

describing scientific research as mapping – hence the immediate relevance of the 

cartographic imagination to my project. Mapping particularly fits a Kuhnian view of 

research: long periods of stable activity within a settled terrain interspersed by 

revolutionary, and perhaps incommensurable, reconceptualizations of that terrain. 

Mapping is for our purposes more to the point than representation, because we always 

know that there can be many maps for any territory and that all of them have a fictional 

character (which is why the map is a standard example of an interpretative genre for 

data 15 ). But because its action is bounded and its result defines a world, mapping better 

suits the theoretician's than the experimenter's view. 

In computer science, mapping is used in knowledge and argument representation and 

implementation of schemes for depicting cyberspace in general and the Web in particular. 

The term surfaces in "Topic Maps", "Concept Maps", and is implicit in talk about, e.g., 

"semantic networks." This interest seems to originate with Toulmin's mapping of 

argument (Toulmin 1958), which suggests techniques of automated inferencing in AI. 

(Maps of argument look like flowcharts.) As a form of data-visualization, mapping also 

connects with a very strong, recent interest in humanities computing (Kirschenbaum 

2002), and so connects this interest with modeling. 

Simulation 

"Simulation" is "The technique of imitating the behaviour of some situation or process … 

by means of a suitably analogous situation or apparatus" (OED). 16 Its mimetic tendencies 

and so bounded action are perhaps most emphatic among the terms we are considering. 

Again, total replication is not at issue; a simulation attends to selected details of the 

world, thus can be exploratory, as when relevant conditions of flight are simulated for 

purposes of study or training. Simulation also relies on abstraction from the original to 

the analogue system (Simon 1969: 15–18), which makes it a kind of representation, 

subject to the same philosophical argument including the caveat respecting mimesis. But 

in usage, the connotation, if not denotation, of an exact correspondence between 

simulation and original remains paradoxically alongside knowledge of real difference. 17 

That knowledge, however uneasily, can be put aside, as in what we now call "virtual 

reality" (VR). 18 

In its current form VR is of course quite a recent phenomenon, but the essential 

movement of simulation on which it is based, from self-conscious imitation to 

displacement of reality, is attested from the get-go of applied computing, in the weapons 

research in nuclear physics immediately following World War II. "Proven on the most 

complex physical problem that had ever been undertaken in the history of science", Peter 

Galison notes, simulation came to replace experimental reality, thus blurring multiple 

boundaries that had previously defined research, redefining it in new terms (1997: 690f). 

Since then the turn away from traditional analytic methods to simulation has spread to

several other fields (Burch 2002). As the biologist Glenn W Rowe points out, with this 

turn has come the realization that "a great many systems seem to have an inherent 

complexity that cannot be simplified" 19 – and so must be studied as simulations. Thus 

simulation has opened our eyes to the new problems with which it can deal. In the 

humanities we have known for some years that computer-based simulations, in the form 

of pedagogical games, can play a role in teaching. An old but very good example is The 

Would-Be Gentleman, a re-creation of economic and social life in seventeenth-century 

France in which the student-player must realize and put aside his or her modern 

preconceptions in order to win (Lougee 1988). In other words he or she must become a 

seventeenth-century Frenchman mentally and emotionally. From more recent and far 

more technically advanced VR applications, such as Richard Beacham's and Hugh 

Denard's reconstruction of the theater of Pompey in Rome (Beacham and Denard 2003), 

one can predict a scholarly future for simulation in many areas of humanistic research. 

Simulation, like game-playing, tends to forgetfulness of the mechanism by which it is 

created, so long as its terms of engagement (expressed in parameters and algorithms) are 

fixed. Unfix them – e.g., in The Would-Be Gentleman by allowing the player to change 

the encoded attitude toward marrying above or below one's station – and the simulation 

becomes a modeling exercise directed to exploring the question of that attitude. Thus 

simulation crosses over into modeling when the constants of the system become 

variables. Modeling, one might say, is a self-conscious simulation, and simulation an 

assimilated modeling. 

Experiment 

In common usage "experiment" (L. experiri, to try) is either "An action or operation 

undertaken in order to discover something unknown …" or "The action of trying 

anything, or putting it to proof; a test, trial …" (OED). In its broadest sense, the word 

embraces "modeling", indeed any heuristic experience of the world, especially that which 

involves conscious purpose, defined procedure, or its material instantiation in equipment. 

Like "modeling", "experiment" refers to a process whose ending is constructed rather 

than given: as Peter Galison has argued for the physical sciences, the experimenter 

decides if and when the attempt has succeeded or failed, in "that fascinating moment … 

when instrumentation, experience, theory, calculation, and sociology meet" (Galison 

1987: 1). Modeling and experimenting are by nature open-ended; indeed they are often at 

the time ill-defined, even quite messy. 20 

The semantic overlap of "modeling" and "experiment" is so close that the two can be 

quite difficult to separate (Guala 2002). Mary S. Morgan, writing about modeling in 

economics, has argued that they may be discriminated by the degree to which the former 

involves hypothesis, the latter reality (2002: 49). But, as she notes, hybrids provide 

exceptions and thought experiments – "devices of the imagination used to investigate 

nature" (Brown 2002) – a very close analogue to modeling. 21 Perhaps the relationship is 

most clearly stated by saying that in the context of research a model is an experimental 

device, modeling an experimental technique.

The point of considering "experiment" here is, however, primarily to locate our topic 

within the context of a particular history of ideas and so to engage with large and 

important areas of historical and philosophical research. Indeed, as an experimental 

technique modeling has shared the fate of experiment in the specialist literature, and so 

also in the popular understanding. Allow me briefly to summarize the background. 22 

By the mid-nineteenth century, understanding of scientific work had begun to polarize 

into two epistemological conditions, which physicist and philosopher Hans Reichenbach 

later famously named "the context of discovery" and "the context of justification." 23 By 

the early to mid-twentieth century, attention had shifted almost completely to justification 

and so, as Thomas Nickels has said, discovery was expelled from mainstream 

epistemology (2000: 87). Experiment, the means of discovery, was in consequence also 

demoted and theory, the focus of justification, promoted. "The asymmetric emphasis on 

theory in the historical literature", Peter Galison explains, meant that attention was 

confined "to the invention and testing of theories" (1987: 8), the actual conduct and 

intellectual role of experiments being largely overlooked. In philosophy too, "experiment 

for theory" dominated until (in Ian Hacking's paraphrase of Nietzsche) Thomas Kuhn's 

The Structure of Scientific Revolutions "unwrapped the mummy of science" by 

historicizing it (Hacking 1983: If) – What had actually always been happening in 

experimental work could then become a proper subject of investigation. Furthermore, 

Kuhn's ample demonstration of "the essential role theory plays in the conduct of 

experimentation, the interpretation of data, and in the definition of 'relevant' phenomena" 

depolarized theory and experiment (Galison 1987: 80- In other words, from an entirely 

subordinate and observational role, experiment emerged alongside theory as an 

interdependent partner. 

Subsequently, through the work of Hacking, Feyerabend, Galison, and several others, the 

fiction of a unitary "scientific method", in which theory cleanly defines the role of 

experiment, has been dispelled. As Hacking says, calling for a "Back-to-Bacon" 

movement, "Experiment has a life of its own" (1983: 150), sometimes preceded by 

theory, sometimes not. But the consequent liberation of experiment from the debilitating 

pretense of grubby handmaiden to "pure" theory has at the same time directed attention 

back to the very hard, old problem of discovery: how does this happen? 

Conclusion 

Why do we need an answer to this question? Because, I have argued, ours is an 

experimental practice, using equipment and instantiating definite methods, for the skilled 

application of which we need to know what we are doing as well as it can be known. I 

have labeled the core of this practice "modeling", and suggested how, properly 

understood, modeling points the way to a computing that is of as well as in the 

humanities: a continual process of coming to know by manipulating representations. We 

are, I have suggested, in good epistemological company. But this only sharpens the 

epistemological question. The signified of modeling vanishes into the murk because we 

lack a disciplined way of talking about it. Methods are explicit, actions definite, results 

forthcoming, yet we have been unable fully and persuasively to articulate the intellectual

case for the means by which these results are produced. Hence the just-a-tool status of 

computing, the not-a-discipline slur, the tradesman's entrance or other back door into the 

academy. No one doubts the usefulness of the practice. Rather it's the intellection of 

praxis to which the next stage in the argument I have begun here must turn. 

Note 

1 My definitions reflect the great majority of the literature explicitly on modeling in the 

history and philosophy of the natural sciences, especially of physics (Bailer-Jones 1999). 

The literature tends to be concerned with the role of modeling more in formal scientific 

theory than in experiment. The close relationship between modeling and experimenting 

means that the rise of a robust philosophy of experiment since the 1980s is directly 

relevant to our topic; see Hacking (1983); Gooding (2000). Quite helpful in rethinking 

the basic issues for the humanities are the writings from the disciplines other than 

physics, e.g., Clarke (1972) on archaeology; Wimsatt (1987) on biology; Del Re (2000) 

on chemistry; and on the social sciences, the essays by de Callatay, Mironesco, Burch, 

and Gardin in Franck (2002). For interdisciplinary studies see Shanin (1972) and 

Morrison and Morgan (1999), esp. "Models as Mediating Instruments" (pp. 10–37). For 

an overview see Lloyd (1998). 

2 Cf. Goodman's distinction between "denotative" and "exemplary" models, respectively 

(1976: 172–3); H. J. Groenewold's "more or less poor substitute" and "more or less 

exemplary ideal" (1960: 98). Similar distinctions are quite common in the literature. 

3 This is usually done in "the rhetoric of technohype … the idiom of grant proposals and 

of interviews in the Tuesday New York Science Times: The breakthrough is at hand; this 

time we've got it right; theory and practice will be forever altered; we have really made 

fantastic progress, and there is now general agreement on the basics; further funding is 

required" (Fodor 1995). More serious criticism is leveled by Terry Winograd (1991: 207– 

8); see below. 

4 I have in mind the present-participial imagination described by Greg Dening, with 

which we may "return to the past the past's own present, a present with all the 

possibilities still in it, with all the consequences of actions still unknown" (Dening 1998: 

48; see also Dening 1996: 35–63). 

5 For the concept in imaginative language and thought see Gibbs (1994; reviewed by 

Turner 1995), Turner (1996); in computer science, Hoffman (1995) – whose summary of 

research is quite valuable; in cognitive science, including psychology, Mitchell (1993), 

Holyoak and Thagard (1997); in the philosophy of science, Achinstein (1968), 

Leatherdale (1974), Gentner (2002), Shelley (2002); in relation to modeling, Bailer-Jones 

(1999), Bailer-Jones and Bailer-Jones (2002). I do not deal here with metaphor in relation 

to modeling, for which see Black (1979), Johnson (2002). 

6 Goodman dismantles the copy-theory of representation, arguing that representation is 

not mimetic but symbolic: object X is always represented as Y, which means that Y is

selective with respect to X and stands in symbolic relationship to it. See also Elgin 

(1998), Hopkins (2000). 

7 Possibly the best and least problematic view is afforded by Davis et al. (1993); see also 

Sowa (2000); Barr and Feigenbaum (1981). Lenat (1998) illustrates the problematic 

tendencies in this field; Winograd (1991) and Dreyfus (1985) provide the antidote. 

8 Frye (1991: 4), to which compare Winograd's analysis of the "almost childish leap of 

faith" made, e.g., by Marvin Minsky in his "Society of Mind" thesis that "the modes of 

explanation that work for the details of [the artificial micro-worlds thus represented] will 

be adequate for understanding conflict, consciousness, genius, and freedom of will" 

(Winograd 1991: 204–7) – as the ambitious claim; see also Winder (1996). 

9 Note the boast that "Cyc knows that trees are usually outdoors, that once people die 

they stop buying things, and that glasses of liquid should be carried rightside-up" (Cycorp 

Company Overview, at http://www.cyc.com/overview.html, accessed September 22, 

2003. 

10 Winograd and Flores (1986: 97–100, 131–3, 174–7); Dreyfus (1985). See also Brooks 

(1991). 

11 See, however, the discussion in Netz (1999: 35–8). 

12 Goodman (1976: 170f), who distinguishes between analogue and digital diagrams. As 

Netz explains, the lettered diagram provides a good example of the latter (1999: 34f). 

13 Shin and Lemon (2002); note the extensive bibliography. 

14 Robinson and Petchenik (1976); see also Monmonier (1996); Wood (1992). Turnbull 

(1994) argues specifically for the link between maps and theories. 

15 See Bateson (2002: 27–8), who cites Alfred Korzybski's principle that "the map is not 

the territory" (Korzybski 1933) and points out that "the natural history of human mental 

process" nevertheless tells a different tale: part of us in fact regularly identifies map and 

territory, name and thing named. See also Goodman (1972: 15); Kent (2000/1978: xix). 

16 On the semantic overlap of "simulation" with "experiment" and "model", see Guala 

(2002), who also stresses the necessity of including the designer or initiator as part of the 

simulation. 

17 Hence, perhaps, the deception attributed to the word: "intent to deceive" in a "false 

assumption or display, a surface resemblance or imitation …" (OED 1 .a., 2) – an 

animated trompe l'oeil. 

18 Recent research in psychology and cognitive science, working with the 

representational model of mind, might be summarized by the proposition that reality as

we know and participate in it is simulated. Thus mental simulation is used to explain 

aspects of cognition (see, e.g., Markman and Dietrich 2000; Davies and Stone 2000). 

Especially relevant here is the idea that perceptual simulations play a significant role in 

cognition, as when the replay of a kinaesthetic memory, awakened by some 

corresponding movement or gesture, lends meaning to a diagram or physical model. This 

is why animations can in principle be more effective than static diagrams: they are more 

knowledgeable (Craig et al. 2002) 

19 Rowe (1994), quoted by Burch (2002: 245); see also Winsberg (2001). 

20 This is true for scientific experiment much more often and more significantly than 

popular and earlier philosophical accounts would have us believe. Ian Hacking illustrates 

the point in an illuminating discussion of the famous Michelson-Morley experiment, "a 

good example of the Baconian exploration of nature" (1983: 254); see his discription 

(1983: 253–61), and esp. the book as a whole. See also Gooding (2000) and Morrison 

(1998) for an overview of experiment in current philosophy of science; the triplet of 

articles presented at a symposium on "The Philosophical Significance of 

Experimentation", Hacking (1988), Heelan (1988), and Galison (1988); and Franklin 

(2002) for physics in particular. 

21 See also Brown (2000), Gooding (1998) and note esp. the careful argument in Kuhn 

(1964) toward an understanding of how thought experiment can lead to new knowledge, 

not simply expose logical contradictions or confusions. 

22 I rely primarily on Galison (1987), Hacking (1983) and Nickels (2000). 

23 Reichenbach introduced the distinction to mark "the well-known difference" between 

"the form in which thinking processes are communicated to other persons [and] the form 

in which they are subjectively performed", i.e., justification and discovery, respectively 

(Reichenbach 1938, chapter 1); compare Feyerabend (1993: 147–58). This distinction 

involves a long-standing argument that goes back to the debate between William 

Whewell (first to use the term "philosophy of science") and John Stuart Mill; it was then 

taken up by Charles Sanders Peirce on the one hand and Karl Popper on the other. 

Popper, in Logik der Forschung (translated as The Logic of Scientific Discovery) 

characteristically ruled discovery out of court by identifying it as a matter for psychology, 

recalling Duhem's and Reichenbach's use of that term: "The question of how it happens 

that a new idea occurs to a man … may be of great interest to empirical psychology; but 

it is irrelevant to the logical analysis of scientific knowledge" (Popper 1959/1935: 7). See 

Nickels (2000). 

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20. 

Stylistic Analysis and Authorship Studies 

Hugh Craig 

Introduction 

Stylistics and authorship studies are siblings with obvious differences and important 

underlying similarities. Stylistic analysis is open-ended and exploratory. It aims to bring 

to light patterns in style which influence readers' perceptions and relate to the disciplinary 

concerns of literary and linguistic interpretation. Authorship studies aim at "yes or no" 

resolutions to existing problems, and avoid perceptible features if possible, working at the 

base strata of language where imitation or deliberate variation can be ruled out. 

Authorship attribution has a forensic aspect – evidence of authorship based on Stylistics 

has been accepted in British courtrooms in particular – and this notion of determining a 

legally enforceable responsibility has given a particular intensity to scrutiny of its 

reliability. Yet stylistic analysis needs finally to pass the same tests of rigor, repeatability,

and impartiality as authorship analysis if it is to offer new knowledge. And the measures 

and techniques of authorship studies must ultimately be explained in stylistic terms if 

they are to command assent. 

In this chapter I offer first an example of a study in computational Stylistics to set the 

scene. Then I discuss the most important theoretical challenge to Stylistics, and go on to 

sketch a methodological basis for the practice. Turning to authorship attribution, I treat 

some underlying ideas about authorship itself, then theoretical issues in attribution 

studies, and finally some practical considerations. 

Computational Stylistics aims to find patterns in language that are linked to the processes 

of writing and reading, and thus to "style" in the wider sense, but are not demonstrable 

without computational methods. We might, for instance, wish to examine patterns of 

association and difference among Shakespeare plays, based on their spoken dialogue. In 

these plays there are the obvious groupings of genre, and chronological divisions into 

early, middle, and late. There are clusters which have been intensively discussed by 

critics at various times, like the "problem plays" and the "four great tragedies." But how 

would the plays arrange themselves if only internal and empirical evidence was used? 

With the idea of starting with as few presumptions as possible, we take twenty-five 

Shakespeare plays (a good proportion of the thirty-eight complete plays normally 

included in a Complete Shakespeare) and calculate which are the dozen most common 

words in them overall. We can make a table of counts of each of these twelve words in 

each of the twenty-five plays. 

Principal components analysis (PCA) is a statistical technique much used in 

computational stylistics for analyzing the variation in a table like this. It creates new 

composite variables which are combinations of the original ones, with each of the latter 

given a separate weighting. The aim is to simplify the data by finding a few new 

variables which account for most of the relationships revealed by the table. To take a 

commonplace example, it might turn out that after collecting statistics on individuals' 

height and weight in a sample the two prove to be so closely related that a single new 

variable, size – height times weight, a composite of the original two – represents most of 

the variation. PCA vectors, "principal components", are an extension of this principle. 

The weightings in a component are worked out so as to create, first, the vector which 

accounts for the greatest proportion of the variance in the original table. Then the 

procedure finds a second independent vector which accounts for the next greatest 

proportion, and so on. If there are strong associations between variables, then the first 

few of these new composite variables will account for most of what is going on. In the 

Shakespeare plays table the first principal component accounts for 33 percent of the total 

variance, and the second for 19 percent, so we are justified in thinking that there are 

strong associations and contrasts in the way the variables behave. If there were not, and 

the variation was purely random, then we could expect each of the twelve principal 

components to have shared the variation equally, i.e., to account for around 8 percent. 

The first step is to look at the variable scores relative to each other on the first and second 

principal components, as in Figure 20.1.

Looking at the X axis first, the highest score is for frequencies of I; those of is, you, and it 

are most closely associated with it. These variables evidently behave like each other – in 

plays where one of them is notably frequent, the others tend to be frequent too, and where 

one of them is notably scarce, the others will tend to be scarce also. At the other end, of is 

the extreme, associated with and and the. The first vector would seem to be a contrast 

between the variables which are associated with interactive dialogue, where pronouns 

like / and you are very common, and those associated with description and narration. The 

plays can also be given scores on the same principal components, as in Figure 20.2. The 

contrast along the X axis here emerges as one between the history plays and the others. 

All the history plays included are to the left of the rest. This fits well with the idea that 

the first principal component, the most important line of difference through the plays, is a 

contrast between plays in which description and its associated function words is stronger 

and those where interaction between characters predominates. Comedy and tragedy do 

not therefore present the biggest contrast in Shakespearean dialogue in these terms. Both 

the comedy and the tragedy groups are well mixed on the first axis. Within the comedy 

group the range is from Love's Labour's Lost (most disquisitory) to Two Gentlemen of 

Verona (most interactive); within the tragedies the range is from Hamlet to Othello. 

(Here, as very often in computational stylistics, we are relating two kinds of variables: 

fixed, externally determined attributes of the particular text like genre, known as 

independent variables, and counts of internal features, which are dependent variables. 

Correlation of the first with the second is the primary tool of computational stylistics.)

20.1 Figure Variable weightings in a PCA of 12 word-variables in 25 shakespeare 

plays 

The second component, the vertical axis in the graphs, runs from to to a and from 

Richard II to Merry Wives of Windsor. High frequencies of to indicate a formal style 

(infinitives and prepositional uses contribute equally, and one arises through more 

complex constructions, the other by more precise specification), and high frequencies of 

a are associated with a more casual one. Once the disquisitory-interactive differences are 

accounted for, it seems that a high-style-low-style contrast is next most important in the 

plays. Along this axis the history plays are strung out rather than bunched together, 

Richard II most formal of all and Henry IV Part 1 close to the opposite, informal 

extreme. The second component is not associated with the regular generic categories. 

Figure 20.1 and Figure 20.2 represent a sketch-map of the style of Shakespeare's plays, 

and of course raise more questions than they answer. In taking such a study beyond the 

sketch stage one would want to know how constant these patterns are when a play is 

added to the set or taken away, for instance, or if different variables are used, for instance 

by splitting to into infinitive, prepositional, and adverbial uses. In such work, a little 

paradoxically, one wants to be reassured by seeing patterns already familiar from the way 

the texts are usually discussed, yet also to be surprised so that they seem more than a 

restatement of the obvious. If the dispositions of these graphs do stand up to scrutiny, 

then they invite further exploration. The first principal component has many similarities 

with the factor that emerged as the strongest in Biber's 1988 study of a large mixed 

sample of modern English. Biber labels the factor "high informational density and exact 

informational content versus affective, interactional, and generalized content" (1988: 

107). A similar contrast is dominant in a number of different studies of this kind, across 

many periods and text types. Should we think of this opposition as the fundamental one 

in categorizing texts? In Shakespeare studies the findings might be compared with 

Lancashire (1997), and the suggestion that the pattern of phrasal repetends indicates a 

segmentation of semantic knowledge in Shakespeare's mind between comedies and 

tragedies on the one hand and poetry and the histories on the other (1997: 182). Then, 

more locally still, one might ask what it is exactly that brings the two Richard plays 

together, which are from different tetralogies and not often discussed alongside each 

other, at the bottom of Figure 20.2. Again, what does it mean that six of the tragedies lie 

in a close cluster, occupying quite a narrow range on both of these components? The 

outliers on both axes are histories or comedies: tragedies are more middling in style, or 

more mixed (an important difference, which might be explored by looking at segments 

from within the plays). 

Beyond these are questions of the design of the experiment. Why choose words, and why 

these words? Why choose plays, when one might choose oeuvres or periods as larger 

aggregations, or characters or scenes as segmentations, or some combination? How 

would the main lines of differentiation in a larger set of Elizabethan and Jacobean plays, 

going beyond Shakespeare, compare?

These are questions that are internal to stylistics, and can be answered in its own terms. 

There are other more fundamental ones. What, for example, is the status and nature of the 

axes of differences that the procedure has derived? They have a precise definition – each 

variable and each case has a score for each vector – but this says nothing about the 

stylistic context. Should an axis of this kind be regarded as a stylistic reality, figuring as 

part of the writing or the reading or hearing process, or just as an artifact of the method? 

Then, even if an axis of this kind is allotted some importance in relation to style, one 

must be cautious about describing it. Any departure from the purely enumerative ("I has 

the highest score on the first principal component") is an act of judgment and is open to 

question. Then, if we accept that the vector has an authentic relationship to style, and is 

adequately interpreted, one can still ask whether anything of interest has been gained in 

relation to the questions that interest those who read and study Shakespeare plays. 

This example will indicate what is at stake in computational stylistics: at best, a powerful 

new line of evidence in long-contested questions of style; at worst, an elaborate display 

of meaningless patterning, and an awkward mismatch between words and numbers and 

the aesthetic and the statistical. For the moment we can note that this sort of work 

remains under challenge, and is still largely ignored by mainstream humanities 

disciplines. It is worth reflecting also that though all its procedures individually predate 

the digital age, the combination in this sort of analysis is inconceivable without the 

computer. The labor involved with counting features like instances of word-types by 

hand and processing the results with a multivariate procedure like PCA the same way, 

would rule out embarking on such a project. Indeed, with no way of assembling and 

manipulating counts of word-variables, researchers in earlier periods assumed that very 

common words varied in frequency only in trivial ways among texts (Ellegard 1962: 15– 

16). 

A Challenge to Stylistics 

Stanley Fish has provided the most root-and-branch challenge to stylistics. He finds the 

whole project of identifying formal structures in a text and then interpreting them in 

relation to its meaning so flawed as to make stylistics entirely vacuous. A central problem 

for Fish is the assumption that meaning resides within the text rather than being created 

as it is read. He argues that the formal features described by stylisticians are meaningless 

except in relation to the reader's perception of them within a reading situation. When 

abstracted from this setting they refer to nothing but themselves and so any further 

analysis of patterns within their use, or comparison with the use of others or in other 

texts, or relating of them to meaning, is entirely pointless. Fish does not deny that there 

may be formal features which can enable categorical classification such as authorship 

(and so he is less hostile to authorship studies using computational stylistics), but he 

insists that these features cannot offer any information of interest about the text – just as a 

fingerprint may efficiently identify an individual but reveal nothing about his or her 

personality. 

The two essays on stylistics reprinted in Fish's 1980 book discuss work by stylisticians 

which he declares to be either circular, so that findings based on the analysis of formal

features are simply restated in "slightly different terms" (1980: 71–2), or arbitrary, 

because there is no principled way to link the meaning asserted with the feature. This 

rules out any claims to objectivity, since either there is no necessary connection between 

the features observed and the interpretation asserted for them, or the interpretation is built 

into the choice and description of the features themselves. He can envisage a pure 

stylistics which confines itself to the description of formal features, or a confessedly 

impure stylistics which would make no claim to objectivity, but suggests that as practiced 

in the studies he analyzes it is worthless. 

Fish should be compulsory reading for any beginning stylistician. His papers belong with 

Schoenbaum's (1966) book as sources for cautionary tales in poor method. Any worker in 

the field will have felt tempted to do as one study Fish describes does, and slide from 

labeling a verb "active" to calling it "dynamic." Fish's comment is that the first "locates 

the verb in a system of formal differences", while the second "semanticizes, even 

moralizes" it (1980: 81). Again, it does seem illegitimate to move from "syntactic 

preferences" to "habits of meaning" or "conceptual orientation" (1980: 75), because, as 

Fish demonstrates, the step from preferences to significance can be made in any number 

of equally legitimate directions. 

Fish is speaking as a humanist: he sees stylistics as an attempt to make interpretation 

mechanical and thus to exclude the human. His affective stylistics, later modified so as to 

make the agent interpretative communities rather than individual readers, is intended to 

locate the study of meaning in the proper creators, readers, or groups of readers, rather 

than in the linguistic features themselves. This is an attempt to incorporate the insight that 

"formal units are always a function of the interpretive model one brings to bear (they are 

not 'in the text')" (1980: 13). 

Fish wants to do more than ridicule the occasional excesses of stylistics: he wants to 

demolish it entirely. The urgency in his analysis comes from his distrust of the claims of 

stylistics to a special "scientific" validity and of what he sees as its underlying antihumanist 

motivation, the exclusion of the human factor in reading and interpretation. It is 

possible, however, to propose an alternative motivation for stylistics, that is, the 

uncovering of patterns of language use which because of their "background" quality, or 

their emergence on a superhumanly wide scale, would otherwise not be noticed; and the 

testing of hypotheses about language use where some empirical validation seems possible 

and appropriate. An example of the former would be patterns of use of thou and you 

forms within one dramatist; an example of the latter would be curiosity about whether 

men and women write differently from one another. This need not be anti-humanist, since 

it would recognize the role of the interpreter in relating any empirically derived results to 

readers' experiences, and to the myriad ways in which texts participate in a culture. 

A Methodological Basis for Stylistics 

A well-founded computational stylistics works with tendencies rather than rules; the 

semantic operation of language is so variable that the relationship of feature to meaning 

can never be fixed. Each element, and each combination of elements, can be used in new

contexts to mean new things. Yet there are also continuities and patterns. These require a 

mixed theory of meaning. A textual form has meaning in its local context, but also as part 

of a collective. Thus an instance of/ has a local meaning, a deixis in which the referent is 

the speaker, but also has a meaning as part of a wider abundance or scarcity of this 

pronoun in the text, reflecting a discourse more or less frequently framed as first-person 

expression. 

Tendencies are best observed in multiple instances, and it is here that the superhuman 

reach and memory of the computer can be of most assistance. Computational stylistics is 

thus extensive by nature, and it may be that the greatest potential for important 

discoveries lies in large-scale comparisons, rather than in the intensive study which is the 

staple of traditional philology. An obvious example would be change in language over 

time, within a writer's career or more largely over a collective artistic enterprise, or 

between periods. There have been promising forays also into groupings of texts by 

gender and nationality. 

Stylistics may be thought of as the epidemiology of textual study: its methods allow 

general conclusions about the relationship between variables. Those which have high 

counts together form a syndrome. Once this is identified, the researcher can seek out a 

mechanism to explain it, as medical researchers might isolate the cell pathology which 

lies behind the epidemiological link between the incidence of smoking and that of lung 

cancer. 

Stylistics in its exploratory form has had a great deal in common with sociolinguistics, 

which relies on correlations between the frequency of linguistic features and categorical 

independent variables like class and gender, with the more or less explicit assumption 

that language patterns are explained by these independent variables. Individualist 

linguistics and cognitive Stylistics, on the other hand, are not entirely sympathetic with 

strictly quantitative work, since they share an underlying belief that the most important 

dimensions of individuality cannot be captured in a common grid of frequencies. 

Johnstone, for example, argues for the value of the qualitative as against the quantitative, 

for small as against large amounts of data, greater as against lesser detail, and the 

methods of cases and interpretation as against those of rules and instances (1996: 23–4). 

Stylistics is perhaps best seen as the offspring of Saussurean linguistics, prizing what she 

calls "the knowledge that can be modelled with rules and conventions" (1996: 188) both 

for categorical assignations (as in authorship studies) and for explorations of relations of 

form and meaning (as in Stylistics). The tension can be seen as one between an interest in 

the "more tractable explanatory variables" (1996: 15–16) favored for sociolinguistic 

study and a sense that these are not adequate to explain language behavior. For 

individualists, a full explanation returns to the irreducibly unique self-expression of the 

human agent. This kind of humanism links them with Fish, who finds the particular 

instance always triumphant in its circumstantial complexity over the rule which is 

imposed on it. In their practice both Johnstone and a practitioner of cognitive Stylistics 

like Lancashire favor a more conventional explication de texte, an intensive examination 

of a single passage, albeit against the background of a larger work or oeuvre. This 

Johnstone calls "modern philology" (1996: 180–8), and consciously aligns with the

practice of literary criticism. In their practice the new individualists create a foundation 

for authorship attribution (via the motivation and practice of individual language users, or 

the nature of the cognitive process) but this is so extremely individualist that it militates 

against the kind of systematic comparison which has been the basic method of 

stylometrics and the more recent computational Stylistics. 

There must be room both for the insight that any text, and any collection of texts, has 

elements which will never be reducible to tabular form, as well as for the knowledge that 

many of the elements of the individual case will form part of a pattern. There is a strong 

instinct in human beings to reduce complexity and to simplify: this is a survival 

mechanism. Rules of thumb can save a great deal of time and effort. Stylistics is born of 

this instinct. What seems hard to explain in the individual case may be easier to 

understand when it is seen in a larger context. If these elements can be put in tabular 

form, then one can harness the power of statistics and of numerous visualizing and 

schematizing tools to help in the process of finding patterns. 

With a statistical method comes the possibility of falsifiability. This can be thought of in 

terms of a wager. If one researcher has a hunch about a literary problem – say, that drama 

written for the private theaters in the Shakespearean period is distinct in its style from 

that written for the public theaters – and bets a colleague that this is true, the wager can 

only be decided under strict conditions. The two will have to agree on how to judge 

difference; on which are clearly private-theater, and which clearly public-theater plays; 

and on how many of them amount to an adequate sample. Then the threshold conditions 

have to be agreed. What test will be used on what data from the plays, and which range of 

values will constitute difference, and which sameness, for the purposes of awarding the 

wager to one side or the other? Here random selection is a useful tool, in cases where it is 

not possible to use all the possible data, and both sides of a question will want to escape 

preconceptions about relevant attributes and groups. 

This follows the lines of a more traditional, hypothesis-driven design. The alternative 

approach is through exploratory data analysis, in which the researcher changes all 

possible parameters in the search for a revealing finding. Performed with due cautions, 

this may lead to discoveries that might be obscured by the starting conditions of a more 

fixed study. As the cost in time of collecting data and manipulating it and presenting it 

visually has come down, the attractiveness of exploratory analysis has increased. 

In a sense (as is often pointed out by critics) computational stylistics merely creates 

another text, which, far from doing away with or automating interpretation, itself requires 

it. But this secondary text is related to the first in a systematic and repeatable way, and 

may be revealing about it. It offers a new perspective on it, like a spectroscopic analysis. 

The unfamiliar and often elaborate and arcane presentation of the results of statistical 

study in stylistics should not obscure what it has in common with traditional practices. It 

is common in any analysis to move from relatively uncontroversial to relatively 

controversial observations. This definition of some readily agreed-on aspects of a text is a 

prelude to interpretation. These results may be gathered by hand, as in the observation 

that plays written by women tend to have more women characters, and that women

characters in them are more likely to start and end a scene. In principle, work in 

computational stylistics is no different. 

In recent years the most-used markers for computational stylistics have been function 

words. Other very common words which do not appear to be unduly sensitive to subject 

matter are also attractive. Then there are combinations of word-types: in Lancashire's 

terminology, the fixed phrase, collocation (pair of words within a certain number of 

words of each other), and word cluster (combination of word phrase and collocation) 

(Lancashire 1997: 172). Lancashire's understanding of language production as mostly 

instinctive, and based on an associative memory, leads him to highlight phrases as the 

key markers of idiolect – phrases of up to seven words since that is the limit of short-term 

or working memory. "We speak and write in chunks", he says (1997: 178–80). The 

existence of large commercial online full-text databases makes it now possible to use this 

kind of authorship marker against the background of a large corpus, which can show if 

any parallels between the phrases of a doubtful text and those of a target author are truly 

unusual. There are some sample studies in Jackson (2002). Among the other most 

important style markers have been choices among pairs of words which can be readily 

substituted for each other in a sentence, such as while and whilst, has and hath, on and 

upon, and the words used to begin sentences. McMenamin details a vast range of these 

markers; the range itself, and the dangers of arbitrary choices among them, have 

contributed to doubts about the overall validity of such studies (Furbank and Owens 

1991). 

The best method of guarding against these dangers is careful testing of the method 

alongside its application to the immediate problem. If the markers and procedures chosen 

provide an efficient separation of samples into authorial or other groups in a closely 

comparable set – ideally, drawn at random from the main set and then reserved for testing 

– then that provides a guide to the reliability of the method when it comes to the 

contested texts. Discriminant analysis, a method for data reduction with some similarities 

to PCA, provides a good illustration. Discriminant analysis provides a weighted vector 

which will maximize the separation between two groups of samples named in advance. 

The vector can then be held constant and a score can be worked out for a mystery 

segment. The procedure even supplies a probability that any given segment belongs to 

one group or the other. This seems ideal for authorship problems: simply provide some 

samples of author A, some of author B, calculate the Discriminant function, and then test 

any doubtful segment. Yet Discriminant results are notoriously optimistic: the separation 

of the segments into named groups is maximized, but we have no way of knowing if they 

are representative of the population of the works of Author A or B – which, in the fullest 

sense, is the range of works it was (or is) possible for Author A or B to write. There is the 

danger of "overtraining" – the method will work superbly for these particular samples, 

but what it is providing is exactly a separation of those samples, which may be a very 

different matter from a true authorial separation. Good practice, therefore, is to reserve 

some samples (as many as 10 percent) as test samples. They do not contribute to the 

"training" exercise, the formation of the function, and therefore are in the same position 

as the doubtful sample or samples. If these are correctly assigned to their group, or most 

of them are, then one can have some confidence that the result on the mystery sample is

eliable. "Testing the test" in this way should be the first move in any statistical authorial 

experiment. The ease of use of statistical packages makes it possible to do a large amount 

of testing even with scarce data by "bootstrapping" – a single item can be withdrawn 

from the training set and tested, then returned to the set while a second is extracted and 

tested, and so on. 

The claim for computational stylistics is that it makes available a class of evidence not 

otherwise accessible (i.e., not to the naked eye). This evidence is comparable to the 

evidence interpreters always use, if not to form their views, then at least to persuade 

others that they are true. 

The computational varieties of stylistic analysis and authorship studies require some 

considerable immersion in traditional humanities disciplines (scholarly, i.e., 

bibliographical, literary-historical and textual; and critical, i.e., interpretative and 

theoretical); in humanities computing generally (in particular the compiling and editing 

of literary texts and assembling them into corpora); and statistical (involving the 

understanding of statistical techniques and the practicalities of statistics and spreadsheet 

programs). All three present enough challenges and large enough bodies of knowledge to 

occupy the working lifetimes of individual researchers by themselves. Most commonly, 

single practitioners are experts in one or possibly two areas, very rarely in three, and have 

to make do in various more or less satisfactory ways in a third. Often the requisite skills 

have been assembled in two practitioners (as in the case of Burrows and Love). 

Authorship 

Authorship attribution is as old as writing itself, and its history displays a fascinating 

variety of problems and solutions. Groupings of texts (Homer, the Bible, Shakespeare) 

may have been created at times when their coherence was not especially significant, but 

later generations have placed enormous importance on the difference between the 

canonical and the apocryphal in each case. The modern interest in authorship attribution 

derives from the Renaissance, when the availability of texts made comparative study 

possible, and a new critical spirit went with the linguistic and textual disciplines of 

Humanism. The demonstration by Lorenzo Valla in the fifteenth century that the 

Donation of Constantine, which gave the western part of the Roman Empire to Pope 

Sylvester, was a forgery, is perhaps the most famous example (Love 2002: 18–19). 

In the modern era most texts come securely attributed on external evidence. The title 

page of the first edition announces the author, or some knowledgeable contemporary 

assigns the work to the author. Some exuberant authors include their name in the text 

itself, as Ben Jonson does in his ode to Lucius Gary and Henry Morison. These 

attributions may be a little deceptive in their straightforwardness – there are 

collaborators, editors, and writers of source materials to complicate matters – but for 

reasons of economy of effort such approximations (Charles Dickens wrote Great 

Expectations) are allowed to stand.

Then for texts without this explicit and decisive external evidence there are numerous 

considerations of topic, approach, attitudes, imagery, turns of phrase, and so on which 

have always served scholars as foundations for attributions. The balance between the 

credibility of this internal evidence and the external kind has swung back and forth. The 

1960s were a low point for internal evidence, as a reaction to the undisciplined 

accumulation of parallel passages and the wholesale "disintegration" of canons like 

Shakespeare's. This skepticism about internal evidence can be seen in Schoenbaum's 

(1966) book and in the 1966 Erdman and Fogel collection. 

Humanities computing is involved with a second generation of attribution based on 

internal evidence, depending on measuring stylistic features in the doubtful and other 

texts, and comparing the results. An interesting case study is the debate over attributing 

the Funerall Elegie for William Peter by W. S. to Shakespeare, which began with Foster's 

(1989) book. The only evidence in favor of the attribution was internal, and stylistic in 

the specialized sense, being quantitative on the one hand and concerned with largely 

unnoticed idiosyncracies of language on the other. Both sides of the debate agreed that 

the poem's style, in the more usual sense of the imagery, diction, and language use 

noticed by readers, was unlike canonical Shakespeare. The proponents of the attribution 

argued that since the quantitative and stylistic evidence was so strong, the generally held 

view of the "Shakespearean" would just have to change. (Recently, another candidate, 

John Ford, has been proposed, for whom there were none of the cognitive dissonances 

just mentioned, and whose work appears to satisfy both readers and stylisticians as a 

good match for the Elegie.) 

Underpinning any interest in assigning authorship is a model of the author. Since the 

1970s the traditional scholarly activity of determining authorship has been conducted 

with a certain unease, resulting from the work of the French post-structuralists Roland 

Barthes, Michel Foucault, and Jacques Derrida, who, in undermining what they saw as 

the bourgeois individual subject, displaced the author as the primary source of meaning 

for texts. In the literary sphere this individual subject had reached an apogee in the 

Romantic idea of the heroic individual author. Since structuralism, there has been more 

interest in the role of discourse, culture, and language itself in creating texts. 

There are, as Furbank and Owens (1988) elaborate, special cautions which should attach 

to the activity of adding items to a canon. The more items included, the wider the canon, 

and the easier it is to add further ones (1988: 4). There is no such thing as adding a work 

temporarily to a canon (1988: 30). They urge resistance to the pressure to assign authors, 

since doing this has so many consequences for the canon to which the work is added: 

after all, "works do not need to be assigned to authors and it does not matter that 

anonymous works should remain anonymous" (1988: 30). It is not enough that a work is 

a "plausible" addition to a canon, since once added, works are very hard to subtract 

(1988: 15). 

There is also a more traditional objection to the energy that has gone into attribution. In 

Erasmus's words in his edition of St Jerome, "What is so important about whose name is 

on a book, provided it is a good book?" (1992: 75). Erasmus's answer is that this may

indeed not matter in the case of a mere playwright like Plautus, whose works he has 

discussed earlier (1992: 71), but is vital in the case of "sacred writers and pillars of the 

church" like Jerome. If "nonsense" by others is presented as the work of such writers, and 

the imposture is not detected, then readers are forced to remain silent about any doubts or 

to accept falsehood (1992: 76). (Erasmus's attribution methods are discussed in Love 

2002: 19–22). A modern critic might argue that authorship is important even in the case 

of a dramatist like Plautus. Attaching a name to a work "changes its meaning by changing 

its context … certain kinds of meaning are conferred by its membership and position in 

the book or oeuvre. Hamlet by William Shakespeare is a different play from Hamlet by 

the Earl of Oxford or Francis Bacon" (Love 2002: 46). Kermode suggests that "a 

different and special form of attention" is paid to the work of a famous writer (quoted in 

Furbank and Owens 1988: 44). Even within the world of scholarship, discovery that an 

essay was not after all by an important thinker like Foucault would make a considerable 

difference (Love 2002: 96–7). 

The signs are that models of authorship are evolving from the post-structuralist "author 

function", an intersection of discourses, toward a concept more influenced by the 

workings of cognitive faculties. If the long-term memory store of human beings works 

not systematically but by "casting out a line for any things directly or indirectly 

associated with the object of our search", then "the organisation of memories … reflects 

the person's own past experience and thought rather than a shared resource of cultural 

knowledge" and this would imply a "unique idiolect" for each individual's speech or 

writing (Lancashire 1997: 178). 

Then there has been a renewed interest in the linguistics of the individual speaker, whose 

differences from other speakers, according to Johnstone, should be accorded 

"foundational status" (1996: 21). She shows that, even in highly constraining situations 

like academic discourse or conducting or answering a telephone survey, speakers tend to 

create an individual style, and to maintain this style across different discourse types. 

Sociolinguistics explains difference through social categories (most often class, gender, 

and race) or rhetorical ones (purpose and audience), but Johnstone argues that these 

should be seen as resources from which the individual constructs difference rather than 

the determinants of it (1996: ix-x). She is prepared to envisage a return to the Romantic 

view of the importance of the individual in language, overturning the highly influential 

arguments of Saussure that language study should concern itself with langue, the system 

of a language, rather than parole, the individual instance of language production (1996: 

20) and challenging the prestige of abstract scientific laws which have meant that, in the 

words of Edward Sapir, "[t]he laws of syntax acquire a higher reality than the immediate 

reality of the stammerer who is trying 'to get himself across'" (quoted in Johnstone 1996: 

20). 

Theoretical Considerations for Attribution 

The practicalities of attribution by stylistic means hinge on the question of the variation 

in the style of an author. In Sonnet 76 Shakespeare's speaker laments that he cannot vary 

his style in line with the poetic fashion, with the result that everything he writes is

hopelessly easy to identify as his own, because it is "So far from variation or quick 

change." (There is a special reason in this case: he always writes on the same subject, on 

his beloved and his love.) In the eighteenth century, Alexander Pope thought that 

attributing authorship by style was foolish – on the grounds, it seems, that it was too easy 

for an author to "borrow" a style (quoted in Craig 1992: 199). Variation, in other words, 

was unlimited. Samuel Johnson later in the same century took the opposite point of view. 

His friend and biographer James Boswell asked him if everyone had their own style, just 

as everyone has a unique physiognomy and a unique handwriting. Johnson answered 

emphatically in the affirmative: "Why, Sir, I think every man whatever has a peculiar 

style, which may be discovered by nice examination and comparison with others: but a 

man must write a great deal to make his style obviously discernible" (quoted in Love 

2002: 7). 

The evidence from the vast activity in empirical work on authorship supports a qualified 

version of Johnson's view. An attribution to satisfy most standards of proof is possible on 

internal grounds provided the doubtful sample is of sufficient length, and sufficient 

samples for comparison in similar text types by candidate writers are available. It is 

reasonable to say that the extreme skeptics about "stylometry" or "non-traditional 

authorship attribution studies", those who suggested that it had similar claims to useful 

information about authorship to those of phrenology about personality (Love 2002: 155), 

have been proved wrong. 

Statistics depends on structured variation – on finding patterns in the changes of items 

along measurable scales. It is easy to see that language samples must vary in all sorts of 

ways as different messages are composed and in different modes and styles. The claims 

of statistical authorship attribution rest on the idea that this variation is constrained by the 

cognitive faculties of the writer. The writer will compose various works in the same or 

different genres and over a more or less extended career. Language features must vary in 

frequency within his or her output. Chronology and genre are readily detectable as 

sources of change; readers might expect to tell the difference between early and late 

Henry James, or between the writing in a comic novel and a serious essay by the same 

writer. Play dialogue presents an expected sharp variation in style within the same work, 

which may contain old and young speakers, men and women, the rich and the poor, the 

witty and the dull, and so on. The approach to authorial idiolect through cognitive science 

and neurology offers its own reinforcement of the notion that different genres are treated 

differently, and that word-patterns can be acquired and also lost over a lifetime 

(Lancashire 1997: 182, and 1999: 744). 

There is, however, evidence which suggests that authorial consistency is quite a strong 

factor in most eras, so that one can expect to find other sources of systematic variation 

nested within it. Early James and late James are different, but not so different as to 

override the difference between James and Thomas Hardy. The characters created by a 

single dramatist scatter on many variables, but the scatter may still be constrained enough 

so that their multidimensional "territory" does not overlap with a second dramatist of the 

same period writing in the same genres. There is contradictory evidence on this matter 

from empirical studies. Burrows (1991) shows that Henry Fielding's style in his parody of

Samuel Richardson remained close enough to his usual pattern to group his parody, 

Sbamela, with his other writing, though it did move some way toward the style of 

Richardson himself. On the other side of the ledger is the case of Remain Gary, who in 

the 1970s wrote two novels under a pseudonym in an effort to escape the reception which 

he felt his established reputation influenced too heavily. These novels were successful, 

and indeed after the publication of the second of them Gary won a second Prix Goncourt 

under his nom de plume, Emile Ajar. Tirvengadum (1998) reports that in the second of 

the Ajar novels Gary was able to change his style so radically as to make the profile of 

his common-word usage distinct from that in his other work. 

The search for stylistic markers which are outside the conscious control of the writer has 

led to a divergence between literary interpretation and stylometry, since, as Horton puts 

it, "the textual features that stand out to a literary scholar usually reflect a writer's 

conscious stylistic decisions and are thus open to imitation, deliberate or otherwise" 

(quoted in Lancashire 1998: 300). In support of these unconscious style markers are the 

studies that show that much of language production is done by parts of the brain which 

act in such swift and complex ways that they can be called a true linguistic unconscious 

(Crane 2001: 18). Lancashire (1997: 177) adds: "This is not to say that we cannot 

ourselves form a sentence mentally, edit it in memory, and then speak it or write it, just 

that the process is so arduous, time-consuming, and awkward that we seldom strive to use 

it." 

Naturally it is incumbent on the researchers to show their audiences how markers 

invisible to writers or readers can be operative in texts at more than a narrowly statistical 

level, to avoid what Furbank and Owens call "the spectre of the meaningless" (1988: 

181). As Love argues, a stylistic explanation for grouping or differentiating texts is 

always to be preferred to a stylometric or "black-box" one (2002: 110). Unlike most 

research in the humanities, results of the purely computational kind cannot be checked 

against the reader's recollection or fresh study of the texts: in principle, the only check is 

a replication of the statistical tests themselves. 

Practical Considerations in Attribution 

The first factor to consider in an authorship question is the number of candidates 

involved. There may be no obvious candidates indicated by external evidence; a group of 

candidates, from two to a very large but still defined number; or there may be a single 

candidate with an unlimited group of other possible authors. Commentators have 

generally been pessimistic about all but two-author problems. Furbank and Owens 

(1988), assessing the potential usefulness of quantitative stylistics in testing attributions 

in the very large group of texts associated with Daniel Defoe, concluded that these new 

methods "were not yet in a position to replace more traditional approaches" (1988: 183). 

They proceeded to work on external and purely qualitative internal evidence in 

establishing a revised Defoe canon. The most-cited successful attribution on quantitative 

measures, the assignation of a group of essays in The Federalist, is an adjudication 

between two candidates. The disputed papers could only have been written by Alexander

Madison or John Hamilton; the problem was solved by Mosteller and Wallace (1964) 

using function-word markers. 

There are grounds for optimism, on the other hand, that with advances in technique and 

in the availability of good-quality, well-suited data, attribution by computational stylistics 

can make a contribution even in complicated cases. Burrows (2002), for instance, reports 

success with a recently developed technique adapted for multiple-candidate problems. It 

establishes multiple authorial profiles and determines a distance from each of these to the 

target text, as a measure of which author's work the mystery piece is "least unlike." 

The way forward would seem to be by a double movement: calibrating the reliability of 

attribution methods by good technique and sheer collective experience, and on the other 

hand, with the increasing availability of text for comparison, advancing on even the most 

difficult problems (especially those involving a single author as against an unlimited 

group of others, and searching for good candidates from a very large pool). It may be 

misguided to aim for complete certainty in attaching a single name to a doubtful text. 

There is, after all, much to be gained from eliminating one or more candidates, while 

remaining uncertain about the actual author, or narrowing authorship to a small group of 

names. 

It may be useful to conclude with a brief list of commonly encountered pitfalls in 

authorial attribution by computational stylistics: 

1 Assuming that, if groups separate according to author, the separation is authorial. 

This is obvious when one authorial group is all tragedies and the second comedies. Even 

if all texts are from the same genre, there must always be the question whether the 

difference is really between (say) comedies set in the city and those set in the court. 

2 Assuming that parallels between elements of a doubtful text and a given author 

provide an argument for authorship when those elements may be found in any number of 

other authors outside the sample tested. This was the downfall of what Schoenbaum calls 

the "parallelographic school" (1966: 94). 

3 Selecting those techniques, and those features, which favor one case, while ignoring 

others ("cherry-picking"). Furbank and Owens (1991: 243) put it this way: "any liberty to 

choose one test, rather than another for a given authorship problem will lay you open to 

incalculable, perhaps subliminal, temptations to choose the one that will help you to 

prove what you want to prove." 

4 Assuming that an author cannot vary from his or her normal style when carrying out 

a particular assignment, or under a particular internal or external influence. 

5 Using a new attribution, not yet generally accepted, as the basis for further attribu 

tions – what has been called in art-historical studies "the forging of chains", Friedlander's 

term for works in the visual arts added to an oeuvre on the basis of connoisseurship and 

then used to justify the addition of further works (cited in Furbank and Owens 1988: 47).

More positively, one might consider a set of ideal conditions for an attribution on stylistic 

grounds: 

1 The doubtful text is long. (In the experiment in assigning Restoration poems to their 

authors described in Burrows (2002), reliability increases with sample length.) 

2 There is a small number of candidates (two is ideal, as in the case of the federalist 

papers). 

3 There is no significant involvement in the doubtful text by other writers as collabor 

ators or revisers, and very few changes have been made by third parties such as printers 

and editors between composition and the surviving texts. 

4 There is a lot of securely attributed text by the candidates, in a similar genre and 

from a similar period to the doubtful text. 

Purists would say that all these conditions must be met before a secure attribution can be 

made, but it may be more realistic to see a continuum of reliability to which all these 

conditions contribute. Attribution work may still yield valuable results, in eliminating 

some of the candidates, for example, where there are significant deficits in one or more of 

these conditions. 

Conclusion 

Computer-assisted stylistics and authorship studies are at an interesting stage. A 

remarkable range of studies has been built up; methods have been well calibrated by a 

variety of researchers on a variety of problems; and even if some studies have proved 

faulty, the vigorous discussion of their shortcomings is a resource for those who follow. 

Well-tested methods can now provide multiple approaches to a given problem, so that 

results can be triangulated and cross-checked. There is an ever-increasing volume of text 

available in machine-readable form: this means that a large purpose-built corpus can now 

be assembled quite quickly in many areas. There are enough successes to suggest that 

computational stylistics and non-traditional attribution have become essential tools, the 

first places one looks to for answers on very large questions of text patterning, and on 

difficult authorship problems. It is worth noting, too, that the lively debate provoked by 

computational work in stylistics and authorship is an indication that these activities are 

playing a significant part in a much wider contemporary discussion about the relations of 

the human and the mechanical. 


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20.2 Figure Scores for texts in a PCA of 12 word-variables in 25 Shakespeare plays 

21. 

Preparation and Analysis of Linguistic Corpora 

Nancy Ide 

The corpus is a fundamental tool for any type of research on language. The availability of 

computers in the 1950s immediately led to the creation of corpora in electronic form that

could be searched automatically for a variety of language features, and compute 

frequency, distributional characteristics, and other descriptive statistics. Corpora of 

literary works were compiled to enable stylistic analyses and authorship studies, and 

corpora representing general language use became widely used in the field of 

lexicography. In this era, the creation of an electronic corpus required entering the 

material by hand, and the storage capacity and speed of computers available at the time 

put limits on how much data could realistically be analyzed at any one time. Without the 

Internet to foster data sharing, corpora were typically created, and processed at a single 

location. Two notable exceptions are the Brown Corpus of American English (Kucera 

and Francis 1967) and the London/Oslo/Bergen (LOB) corpus of British English 

(Johansson et al. 1978); both of these corpora, each containing 1 million words of data 

tagged for part of speech, were compiled in the 1960s using a representative sample of 

texts produced in the year 1961. For several years, the Brown and LOB were the only 

widely available computer-readable corpora of general language, and therefore provided 

the data for numerous language studies. 

In the 1980s, the speed and capacity of computers increased dramatically, and, with more 

and more texts being produced in computerized form, it became possible to create 

corpora much larger than the Brown and LOB, containing millions of words. The 

availability of language samples of this magnitude opened up the possibility of gathering 

meaningful statistics about language patterns that could be used to drive language 

processing software such as syntactic parsers, which sparked renewed interest in corpus 

compilation within the computational linguistics community. Parallel corpora, which 

contain the same text in two or more languages, also began to appear; the best known of 

these is the Canadian Hansard corpus of Parliamentary debates in English and French. 

Corpus creation still involved considerable work, even when texts could be acquired from 

other sources in electronic form. For example, many texts existed as typesetter's tapes 

obtained from publishers, and substantial processing was required to remove or translate 

typesetter codes. 

The "golden era" of linguistic corpora began in 1990 and continues to this day. Enormous 

corpora of both text and speech have been, and continue to be, compiled, many by 

government-funded projects in Europe, the USA, and Japan. In addition to monolingual 

corpora, several multilingual parallel corpora covering multiple languages have also been 

created. A side effect of the growth in the availability and use of corpora in the 1990s was 

the development of automatic techniques for annotating language data with information 

about its linguistic properties. Algorithms for assigning part of speech tags to words in a 

corpus and aligning words and sentences in parallel text (i.e., associating each word or 

sentence with its translation in the parallel version) were developed in the 1990s that 

achieve 95–98 percent accuracy. Automatic means to identify syntactic configurations 

such as noun phrases, and proper names, dates, etc., were also developed. 

There now exist numerous corpora, many of which are available through the Linguistic 

Data Consortium (LDC) (http://www.ldc.upenn.edu) in the USA and the European 

Language Resources Association (ELRA) (http://www.elra.org) in Europe, both of which 

were founded in the mid-1990s to serve as repositories and distributors of corpora and

other language resources such as lexicons. However, because of the cost and difficulty of 

obtaining some types of texts (e.g., fiction), existing corpora vary considerably in their 

composition; very few efforts have been made to compile language samples that are 

"balanced" in their representation of different genres. Notable exceptions (apart from the 

early Brown and LOB) are the British National Corpus (BNC) 

(http://www.hcu.ox.ac.uk/BNC/) and the American National Corpus (ANC) 

(http://www.Amer-icanNationalCorpus.org), as well as (to some extent) the corpora for 

several western European languages produced by the PAROLE project. In fact the 

greatest number of existing text corpora are composed of readily available materials such 

as newspaper data, technical manuals, government documents, and, more recently, 

materials drawn from the World Wide Web. Speech data, whose acquisition is in most 

instances necessarily controlled, are more often representative of a specific dialect or 

range of dialects. 

Many corpora are available for research purposes by signing a license and paying a small 

reproduction fee. Other corpora are available only by paying a (sometimes substantial) 

fee; this is the case, for instance, for many of the holdings of the LDC, making them 

virtually inaccessible to humanists. 

Preparation of Linguistic Corpora 

The first phase of corpus creation is data capture, which involves rendering the text in 

electronic form, either by hand or via optical character recognition (OCR), acquisition of 

word processor or publishing software output, typesetter tapes, PDF files, etc. Manual 

entry is time-consuming and costly, and therefore unsuitable for the creation of very large 

corpora. OCR output can be similarly costly if it requires substantial post-processing to 

validate the data. Data acquired in electronic form from other sources will almost 

invariably contain formatting codes and other information that must be discarded or 

translated to a representation that is processable for linguistic analysis. 

Representation formats and surrounding issues 

At this time, the most common representation format for linguistic corpora is XML. 

Several existing corpora are tagged using the EAGLES XML Corpus Encoding Standard 

(XCES) (Ide et al. 2000), a Text Encoding Initiative (TEI) – compliant XML application 

designed specifically for linguistic corpora and their annotations. The XCES introduced 

the notion of stand-off annotation, which requires that annotations are encoded in 

documents separate from the primary data and linked to them. One of the primary 

motivations for this approach is to avoid the difficulties of overlapping hierarchies, which 

are common when annotating diverse linguistic features, as well as the unwieldy 

documents that can be produced when multiple annotations are associated with a single 

document. The stand-off approach also allows for annotation of the same feature (e.g., 

part of speech) using alternative schemes, as well as associating annotations with other 

annotations rather than directly to the data. Finally, it supports two basic notions about 

text and annotations outlined in Leech (1993): it should be possible to remove the

annotation from an annotated corpus in order to revert to the raw corpus; and, conversely, 

it should be possible to extract the annotations by themselves from the text. 

The use of stand-off annotation is now widely accepted as the norm among corpus and 

corpus-handling software developers; however, because mechanisms for inter-document 

linking have only recently been developed within the XML framework, many existing 

corpora include annotations in the same document as the text. 

The use of the stand-off model dictates that a distinction is made between the primary 

data (i.e., the text without additional linguistic information) and their annotations, in 

particular, what should and should not be marked in the former. The XCES identifies two 

types of information that may be encoded in the primary data: 

1 Gross structure: universal text elements down to the level of the paragraph, which is 

the smallest unit that can be identified language-independently; for example: 

• structural units of text, such as volume, chapter, etc., down to the level of paragraph; 

also footnotes, titles, headings, tables, figures, etc.; 

• features of typography and layout, for previously printed texts: e.g., list item markers; 

• non-textual information (graphics, etc.). 

2 Segmental structure: elements appearing at the sub-paragraph level which are usually 

signaled (sometimes ambiguously) by typography in the text and which are languagedependent; 

for example: 

• orthographic sentences, quotations; 

• orthographic words; 

• abbreviations, names, dates, highlighted words. 

Annotations (see next section) are linked to the primary data using XML conventions 

(XLink, Xpointer). 

Speech data, especially speech signals, are often treated as "read-only", and therefore the 

primary data contain no XML markup to which annotations may be linked. In this case, 

stand-off documents identify the start and end points (typically using byte offsets) of the 

structures listed above, and annotations are linked indirectly to the primary data by 

referencing the structures in these documents. The annotation graphs representation 

format used in the ATLAS project, which is intended primarily to handle speech data, 

relies entirely on this approach to link annotations to data, with no option for referencing 

XML-tagged elements.

Identification of segmental structures 

Markup identifying the boundaries of gross structures may be automatically generated 

from original formatting information. However, in most cases the original formatting is 

presentational rather than descriptive; for example, titles may be identifiable because 

they are in bold, and therefore transduction to a descriptive XML representation may not 

be straightforward. This is especially true for sub-paragraph elements that are in italic or 

bold font; it is usually impossible to automatically tag such elements as emphasis, foreign 

word, etc. 

Creation of linguistic corpora almost always demands that sub-paragraph structures such 

as sentences and words, as well as names, dates, abbreviations, etc., are identified. 

Numerous programs have been developed to perform sentence "splitting" and word 

tokenization, many of which are freely available (see, for example, the tools listed in the 

Natural Language Software Registry (http://www.dfki.de/lt/registry/) or the SIL Software 

Catalogue (http://www.sil.org). These functions are also embedded in more general 

corpus development tools such as GATE (Cunningham 2002). Sentence splitting and 

tokenization are highly language-dependent and therefore require specific information 

(e.g., abbreviations for sentence splitting, clitics and punctuation conventions for 

tokenization) for the language being processed; in some cases, language-specific software 

is developed, while in others a general processing engine is fed the language-specific 

information as data and can thus handle multiple languages. Languages without wordboundary 

markers, such as Chinese and Japanese, and continuous speech represented by 

phoneme sequences, require an entirely different approach to segmentation, the most 

common of which is a dynamic programming algorithm to compute the most likely 

boundaries from a weighted transition graph. This, of course, demands that the 

probabilities of possible symbol or phoneme sequences are available in order to create the 

weighted graph. 

Within the computational linguistics community, software to identify so-called "named 

entities" (proper names designating people, places, organizations, events, documents, 

etc.), as well as dates and other time expressions, has been developed, and many of these 

tools are freely available for research. However, most of these tools have been developed 

using existing corpora which consist of newspapers and government reports and are 

unlikely to perform well on the kinds of data that interest humanists, such as literary 

works, historical documents, etc. This is just one example of the broader situation in the 

development of corpus-handling tools: the available data, which are often highly skewed 

for genre, drive tool, and algorithm development, and therefore applicability to more 

generalized corpora is often limited. 

Corpus annotation 

For computational linguistics research, which has driven the bulk of corpus creation 

efforts over the past decade, corpora are typically annotated with various kinds of 

linguistic information. The following sections outline the major annotation types.

Morpho-syntactic annotation 

By far the most common corpus annotation is morpho-syntactic annotation (part-ofspeech 

tagging), primarily because several highly accurate automatic taggers have been 

developed over the past fifteen years. Part-of-speech tagging is a disambiguation task: for 

words that have more than one possible part of speech, it is necessary to determine which 

one, given the context, is correct. Although, in English, close to 90 percent of the words 

in the language have only one part of speech, in actual use (e.g., in a corpus), part of 

speech for as many as 40 percent of the word instances may be ambiguous (DeRose 

1988), due in large part to ambiguity for a handful of high-frequency words such as 

"that", which can be either a determiner ("that girl") or a complementizer ("He heard that 

you came home"). Beyond this, the most common ambiguity in English is between verb 

and noun, e.g., "hide", "dog", "brand", "felt", etc. 

Taggers fall into two general classes: rule-based (e.g. ENGTWOL, Voutilainen, 1995), 

which use manually generated rules to assign part of speech, and stochastic, which rely 

on probabilities for n-grams – sequences of n (usually, 2 or 3) tags that are known to 

occur in real data. Stochastic taggers learn these probabilities by being "trained" on 

previously tagged data that have been hand-validated for correctness. A third type of 

tagger, often called the "Brill tagger" after its developer (Brill 1995), uses a hybrid 

approach which learns its tagging rules from a previously tagged training corpus. 

Obviously, the more accurately tagged data a tagger can use for training, the more likely 

its probabilities will be correct. This fact has led to the creation of corpora that have been 

hand-annotated (or in which automatically produced tags have been hand-validated) 

specifically intended for training, in order to enable automatic taggers to produce even 

more tagged corpora. 

The tags generated by a part-of-speech tagger provide more information than simple 

word class (noun, verb, adjective, etc.). Various levels of morpho-syntactic information 

can be represented; the more detailed the information, the bigger the tagset, and the 

bigger the tagset, the less accurate an automatic tagger will be. For this reason, tagsets 

including 50–100 tags that collapse or eliminate detailed morpho-syntactic information – 

such as the information in the morpho-syntactic specifications for western and eastern 

European languages produced by the EAGLES project 

(http://www.ilc.pi.cnr.it/EAGLES/home.html) – are most typically used in automatic 

tagging. 

There are several tagsets in common use for English, most of which evolved from the 87 

tags used in the Brown corpus (http://www.hit.uib.no/icame/brown/bcm.html). Probably 

the most widely used is the 45-tag set of the Penn Treebank project (Marcus et al. 1993), 

of which only 36 are actual morpho-syntactic categories (the rest are for punctuation, list 

markers, etc.). The Penn tagset is a variant of the Brown tagset that eliminates 

information retrievable from the form of the lexical item. It therefore includes only one 

tag for different forms of the verbs "be", "have", and "do", whereas the Brown (and other 

common tagsets for English) provide a different tag for each of these forms. Another 

well-known tagset for English is the 61-tag C5 tagset of the CLAWS (Constituent

Likelihood Automatic Word-tagging System) tagger, developed at the University of 

Lancaster (Garside and Smith 1997) and used to tag the British National Corpus. 

Part-of-speech taggers rely on lexicons that provide all of the possible part of speech 

assignments for a lexical item found in the input, from which it must choose the most 

probable given the immediate context. The information in the lexicons must therefore 

match – or at least, be mappable to – the tagset used by the tagger. It is an unfortunate 

fact that it is often extremely difficult and sometimes impossible to map one tagset to 

another, which has resulted in much re-creation of lexical information to suit the needs of 

a particular tagger. 

Many lexicons include lemmas (root forms), and morpho-syntactic annotation may 

produce lemmas as well as part-of-speech tags in their output. The presence of lemmas in 

an annotated text enables extraction of all orthographic forms associated with a given 

lemma (e.g., "do", "does", "doing, "did" for the lemma "do"). However, although 

relatively easily produced, many existing corpora do not include lemmas; notable 

exceptions are the Journal of the Commission corpus, the Orwell multilingual corpus, and 

the SUSANNE corpus. 

Parallel alignment 

In addition to algorithms for morpho-syntactic annotation, reliable algorithms for 

alignment of parallel texts – i.e., texts for which there exist translations in two or more 

languages – have been developed. Probability information about correspondences 

between words, phrases, and other structures derived from aligned corpora are used to 

choose from multiple possible translations that may be generated by a machine 

translation system. Parallel corpora have also been used to automatically generate 

bilingual dictionaries (e.g., Dagan and Church 1997) and, more recently, as a means to 

achieve automatic sense tagging of corpora (e.g., Ide et al. 2001). 

Two types of parallel alignment are common: sentence alignment and word alignment. 

Sentence alignment is by far the easier and more accurate of the two, the major problem 

being to determine cases in which one-to-many or partial mappings exist. Many sentence 

and word-aligned corpora exist, the vast majority of which include only two languages. 

Probably the best known is the English-French Hansard Corpus of Canadian 

Parliamentary debates, which has served as the basis for numerous translation studies. 

Multilingual parallel corpora are much more rare; the difficulty is not in the alignment 

itself, but rather in the availability of texts in multiple translations (in particular, texts that 

are not bound by copyright or other restrictions). Existing multilingual aligned corpora 

include the United Nations Parallel Text Corpus (English, French, Spanish), the Journal 

of the Commission (JOC) corpus (English, French, German, Italian, Spanish), the Orwell 

1984 Corpus (Bulgarian, Czech, English, Estonian, Hungarian, Latvian, Lithuanian, 

Romanian, Serbo-Croat, Slovene), Plato's Republic (Bulgarian, Chinese, Czech, English, 

German, Latvian, Polish, Romanian, Slovak, Slovene) and the Bible (Chinese, Danish, 

English, Finnish, French, Greek, Indonesian, Latin, Spanish, Swahili, Swedish, 

Vietnamese).

Syntatic annotation 

There are two main types of syntactic annotation in linguistic corpora: noun phrase (NP) 

bracketing or "chunking", and the creation of "treebanks" that include fuller syntactic 

analysis. Syntactically annotated corpora serve various statistics-based applications, most 

notably, by providing probabilities to drive syntactic parsers, and have been also used to 

derive context-free and unification-based grammars (Charniak 1996; Van Genabith et al. 

1999). Syntactically annotated corpora also provide theoretical linguists with data to 

support studies of language use. 

The best known and most widely used treebank is the Penn Treebank for English (Marcus 

et al. 1993). Ongoing projects exist for the development of treebanks for other languages, 

including German (the NEGRA corpus, Brants et al. 2003; Stegman et al. 1998), Czech 

(The Prague Dependency Treebank, Hajic 1998), French (Abeille et al. 2000), and 

Chinese (Xue et al. 2002). The Penn Treebank and the Chinese Treebank, both created at 

the University of Pennsylvania, use LISP-like list structures to specify constituency 

relations and provide syntactic category labels for constituents, as shown below: 

Although they differ in the labels and in some cases 

the function of various nodes in the tree, many treebank annotation schemes provide a 

similar constituency-based representation of relations among syntactic components. In 

contrast, dependency schemes do not provide a hierarchical constituency analysis, but 

rather specify grammatical relations among elements explicitly; for example, the sentence 

"Paul intends to leave IBM" could be represented as follows: 

Here, the predicate is the relation type, the first 

argument is the head, the second the dependent, and additional arguments may provide 

category-specific information (e.g., introducer for prepositional phrases). Finally, socalled 

"hybrid systems" combine constituency analysis and functional dependencies, 

usually producing a shallow constituent parse that brackets major phrase types and 

identifies the dependencies between heads of constituents (e.g., the NEGRA Corpus).

Although most modern treebanks utilize an SGML or XML encoding rather than list 

structures, syntactic annotation is invariably interspersed with the text itself. This makes 

it difficult or impossible to add other kinds of annotations to the data, or to provide 

alternative syntactic annotations. As noted earlier, the use of stand-off annotations is 

increasingly encouraged. A stand-off scheme for syntactic annotations, which also serves 

as a. "pivot" format for representing different types of syntactic annotation with a 

common scheme, has been developed by Ide and Romary (2001). 

Semantic annotation 

Semantic annotation can be taken to mean any kind of annotation that adds information 

about the meaning of elements in a text. Some annotations that may be considered to 

provide semantic information – for example, "case role" information such as agent, 

instrument, etc. – are often included in syntactic annotations. Another type of semantic 

annotation common in literary analysis (especially in the 1960s and 1970s) marks words 

or phrases in a text as representative of a particular theme or concept. At present, the 

most common type of semantic annotation is "sense tagging": the association of lexical 

items in a text with a particular sense or definition, usually drawn from an existing sense 

inventory provided in a dictionary or online lexicon such as WordNet (Miller et al. 1990). 

The major difficulty in sense annotation is to determine an appropriate set of senses. 

Simply examining the differences in sense distinctions made from one dictionary to 

another demonstrates the difficulty of this task. To solve the problem, some attempts 

have been made to identify the kinds of sense distinctions that are useful for automatic 

language processing: for example, tasks such as information retrieval may require only 

very coarse-grained sense distinctions – the difference between "bank" as a financial 

institution vs. a river bank – whereas others, and in particular machine translation, require 

finer distinctions – say, between "bank" as a financial institution and as a building. 

However, by far the most common source of sense tags used for semantic annotation is 

WordNet, an online dictionary that in addition to providing sense lists, groups words into 

"synsets" of synonymous words. WordNet has been updated several times, and as a 

result, its sense list may differ for a particular word depending on the WordNet version 

used for the tagging. 

It is widely acknowledged that the sense distinctions provided in WordNet are far from 

optimal. However, this resource, which is among the most heavily used in natural 

language processing research over the past decade, will likely continue to serve as a basis 

for sense tagging for at least the foreseeable future, if for no other reason than that it 

continues to be the only freely available, machine-tractable lexicon providing extensive 

coverage of English. The Euro WordNet project has produced WordNets for most 

western European languages linked to WordNet 1.5 (the current version of the English 

WordNet is 2.0), and WordNets for other languages (e.g., Balkan languages) are under 

development, which is likely to extend the research community's reliance on its sense 

inventory. In any case, no clearly superior alternative source of sense distinctions has 

been proposed.

Because sense-tagging requires hand annotation, and because human annotators will 

often disagree on sense assignments even given a predefined sense inventory, very few 

sense-tagged corpora exist. Examples include the Semantic Concordance Corpus 

(SemCor; Miller et al. 1993), produced by the WordNet project, which assigns WordNet 

sense tags for all nouns, verbs, and adjectives in a 250,000-word corpus drawn primarily 

from the Brown Corpus; the DSO Corpus, containing sense-tags for 121 nouns and 70 

verbs in about 192,800 sentences taken from the Brown Corpus and the Wall Street 

Journal; and Hector, containing about 200,000 tagged instances of 300 words in a corpus 

of British English. 

Automatic means to sense-tag data have been sought ever since machine-readable texts 

became available. This area of research, called "word sense disambiguation", remains to 

this day one of the more difficult problems in language processing. Although rule-based 

approaches have been developed, the most common approaches to word sense 

disambiguation over the past decade are statistics-based, relying on the frequency with 

which lexical items (or categories of lexical items) in the context under consideration 

have been found in the context of a word in a given sense. Some recent research (Ide 

2000; Ide et al. 2001; Doab and Resnik 2002) has explored the use of information 

gleaned from parallel translations to make sense distinctions. For a comprehensive 

overview of approaches to word sense disambiguation see Ide and Veronis (1998). 

Discourse-level annotation 

There are three main types of annotation at the level of discourse: topic identification, coreference 

annotation, and discourse structure. 

Topic ientification 

(also called "topic detection") annotates texts with information about the events or 

activities described in the text. Automatic means for topic detection over streams of data 

such as broadcast news and newswires is under development, primarily in the context of 

the DARPA-sponsored Topic Detection Task. A subtask of this kind of annotation is 

detection of boundaries between stories/texts, which may also be included in the 

annotation. 

Co-reference annotation 

Links referring objects (e.g., pronouns, definite noun phrases) to prior elements in a 

discourse to which they refer. This type of annotation is invariably performed manually, 

since reliable software to identify co-referents is not available. For example, the MUG 7 

(Message Understanding Conference) corpus contains hand-generated co-reference links 

(Hirschman and Chinchor 1997) marked in a non-stand-off SGML format similar to the

following: 

Discourse structure annotation 

Identifies multi-level hierarchies of discourse segments and the relations between them, 

building upon low-level analysis of clauses, phrases, or sentences. There are several 

theoretical approaches to the analysis of discourse structure, differing in terms of the span 

of text (clause, sentence, etc.) that is considered to be the atomic unit of analysis, and in 

the relations defined to exist between units and higher-level structures built from them. 

Most common approaches are based on "focus spaces" (Grosz and Sidner 1986) or 

Rhetorical Structure Theory (Mann and Thompson 1988). To date, annotation of 

discourse structure is almost always accomplished by hand, although some software to 

perform discourse segmentation has been developed (e.g., Marcu 1996). 

Annotation of speech and spoken data 

The most common type of speech annotation is an orthographic transcription that is timealigned 

to an audio or video recording. Often, annotation demarcating the boundaries of 

speech "turns" and individual "utterances" is included. Examples of speech corpora in 

this type of format include the Child Language Data Exchange System (CHILDES) 

corpus (MacWhinney 1995), the TIMIT corpus of read speech (Lamel et al. 1990), and 

the LACITO Linguistic Data Archiving Project (Jacobson and Michailovsky 2000). 

The orthographic transcription of speech data can be annotated with any of the kinds of 

linguistic information outlined in previous sections (e.g., part of speech, syntax, coreference, 

etc.), although this is not common. In addition, speech data may be annotated 

with phonetic segmentation and labeling (phonetic transcription), prosodic phrasing and 

intonation, disfluencies, and, in the case of video, gesture. 

Most types of annotation particular to speech data are time-consuming to produce 

because they must be generated by hand, and because the annotators must be skilled in 

the recognition and transcription of speech sounds. Furthermore, speech annotation is 

problematic: for example, phonetic transcription works on the assumption that the speech 

signal can be divided into single, clearly demarcated sounds, but these demarcations are 

often rather unclear. Prosodic annotation is even more subjective, since the decision 

about the exact nature of a tone movement often varies from annotator to annotator. The 

kinds of phenomena that are noted include onset, rising tone, falling tone, rising/falling 

tone, level tone, pause, overlap, etc. The notations for prosodic annotation vary widely

and they are not typically rendered in a standard format such as XML. As a result, the 

few existing corpora annotated for prosody are widely inconsistent in their format. One of 

the best known such corpora is the London-Lund Corpus of Spoken English (Svartvik 

1990). 

Corpus annotation tools 

Over the past decade, several projects have created tools to facilitate the annotation of 

linguistic corpora. Most are based on a common architectural model introduced in the 

MULTEXT project, which views the annotation process as a chain of smaller, individual 

processes that incrementally add annotations to the data. A similar model was developed 

in the TIPSTER project. 

Among the existing annotation tools for language data are LT XML (University of 

Edinburgh), which directly implements the MULTEXT model and is especially geared 

toward XML-encoded resources; GATE (University of Sheffield), based on the TIPSTER 

model and including tools for tokenization, sentence splitting, named entity recognition, 

part of speech tagging, as well as corpus and annotation editing tools. The Multilevel 

Annotation, Tools Engineering (MATE) project provides an annotation tool suite 

designed especially for spoken dialogue corpora at multiple levels, focusing on prosody, 

(morpho-)syntax, co-reference, dialogue acts, and communicative difficulties, as well as 

inter-level interaction. ATLAS (Architecture and Tools for Linguistic Analysis Systems) 

is a joint initiative of the US National Institute for Standards and Technology (NIST), 

MITRE, and the LDC to build a general-purpose annotation architecture and a data 

interchange format. The starting point in ATLAS is the annotation graph model, with 

some significant generalizations. 

Currently, a subcommittee of the International Standards Organization (ISO) – ISO TC37 

SC4 – is developing a generalized model for linguistic annotations and processing tools, 

based on input from developers of the annotation tool suites mentioned above as well as 

from annotation scheme designers. The goal is to provide a common "pivot" format that 

instantiates a generalized data model of linguistic resources and their annotations to and 

from which existing formats – provided they are consistent with the model – can be 

mapped in order to enable seamless interchange. At present, data annotated within one 

project, using XML, annotation graphs, or a proprietary annotation scheme, are typically 

difficult to import for manipulation using another tool suite, either for further annotation 

or analysis. The developing ISO model is intended to allow annotators to use any of a 

variety of schemes to represent their data and annotations, and map it to a common 

format for interchange. Users of other schemes would then import the data in the 

common format to their own schemes. This means, for example, that a corpus marked up 

using TEI or XCES conventions can be mapped to the common format and, from that, 

mapped again to, say, an annotation graph representation that will enable manipulation of 

the data by tools implementing that model, without information loss.

The future of corpus annotation 

Recent developments in the XML world, primarily in the scope of work within the World 

Wide Web Consortium (W3C), have focused attention on the potential to build a 

Semantic Web. This possibility has interesting ramifications for both corpus annotation 

and analysis, in two (related) ways. First, the underlying technology of the Semantic Web 

enables definition of the kinds of relationships (links) one resource – where a "resource" 

can be any fragment of a document or the document as a whole – may have with another. 

For example, a "word" may have a link labeled "part of speech" with another resource 

that represents (possibly, as a simple string) "noun" or "noun singular masculine." 

Because annotation is, at the base, the specification of relationships between information 

in a corpus and linguistic information that describes it, the development of technologies 

such as the Resource Description Framework (RDF) can have significant impact on the 

way annotations are associated with primary language resources in the future. 

A second activity within the Semantic Web community that has ramifications for both 

annotation and analysis of linguistic data is the development of technologies to support 

the specification of and access to ontological information. Ontologies, which provide a 

priori information about relationships among categories of data, enable the possibility to 

apply inferencing processes that can yield information that is not explicit in the data 

itself. For example, to properly parse a sentence such as "I ate a fish with a fork" – that is, 

to attach the prepositional phrase "with a fork" to the verb "eat" and not as a modifier of 

"fish" – we can check an ontology that specifies that "fork" IS-A sub-class of 

"instrument", and that "eat" has a USES-A relation with things of type "instrument." 

More generally, we may be able to identify the topic of a given document by consulting 

an ontology. For example, the ontology may specify that the word "bank" can represent a 

sub-class of "financial institution" or "geological formation"; if the document contains 

several other words that are also related to "financial institution" such as "money", 

"account", etc., we may conclude that the document has financial institutions as one of its 

topics (and, as a side effect, that the word "bank" is being used in that sense in this 

document). 

In order to be able to exploit ontological information, it must be created and stored in a 

universally accessible form. The W3C group developing the Ontological Web Language 

(OWL) is providing a standard representation format. It is now up to the computational 

linguistics community to instantiate the relevant ontological information and use it to 

annotate and analyze language data. Development and use of ontologies has in fact been 

a part of the field for several years – in particular, the ontology in the WordNet lexicon 

has been widely used for annotation and analysis of language data. Semantic Web 

technologies will enable development of common and universally accessible ontological 

information. 

Analysis of Linguistic Corpora

There are two major uses for linguistic corpora: statistics-gathering to support natural 

language processing; and language analysis to support language learning and dictionary 

creation. 

Statistics gathering 

A corpus provides a bank of samples that enable the development of numerical language 

models, and thus the use of corpora goes hand in hand with empirical methods. In the late 

1980s, the increased availability of large amounts of electronic text enabled, for the first 

time, the full-scale use of data-driven methods to attack generic problems in 

computational linguistics, such as part-of-speech identification, prepositional phrase 

attachment, parallel text alignment, word sense disambiguation, etc. The success in 

treating at least some of these problems with statistical methods led to their application to 

others, and by the mid-1990s, statistical methods had become a staple of computational 

linguistics work. 

The key element for many statistics-based approaches to language processing (in 

particular, so-called "supervised" methods) is the availability of large, annotated corpora, 

upon which annotation algorithms are trained to identify common patterns and create 

transformations or rules for them. The stochastic part-of-speech taggers described above 

are probably the best-known application relying on previously annotated corpora, but 

similar approaches are also used for word sense disambiguation, probabilistic parsing, 

and speech recognition. In word sense disambiguation, for example, statistics are 

gathered reflecting the degree to which other words are likely to appear in the context of 

some previously sense-tagged word in a corpus. These statistics are then used to 

disambiguate occurrences of that word in untagged corpora, by computing the overlap 

between the unseen context and the context in which the word was seen in a known 

sense. 

Speech recognition is, in fact, the area of computational linguistics in which corpora were 

first put into large-scale use in support of language processing, beginning with the 

utilization of Hidden Markov Models (HMMs) in the 1970s. This paradigm required data 

to statistically train an acoustic model to capture typical sound sequences and a language 

model to capture typical word sequences, and produced results that were far more 

accurate and robust than the traditional methods. It was not until the late 1980s that the 

statistical approach was applied to other areas, one of the first of which was machine 

translation. Following the same approach as the speech recognition systems, researchers 

automatically trained a French-English correspondence model (the Translation Model) on 

3 million sentences of parallel French and English from the Canadian Parliamentary 

records, and also trained a Language Model for English production from Wall Street 

Journal data. To translate, the former model was used to replace French words or phrases 

by the most likely English equivalents, and then the latter model ordered the English 

words and phrases into the most likely sequences to form output sentences. 

Probabilistic parsing is one of the more recent applications of statistical methods to 

language processing tasks. Again, large bodies of data previously annotated and validated

for syntactic structure are required in order to provide statistics concerning the probability 

that a given syntactic construction is the correct one in its context. Syntactic structure can 

be very ambiguous; traditional parsers often produced numerous alternative structural 

analyses for an input sentence. A probabilistic parser uses previously gathered statistics 

to choose the most probable interpretation. 

Issues for corpus-based statistics gathering 

In order to be representative of any language as a whole, it is necessary that a corpus 

include samples from a variety of texts that reflect the range of syntactic and semantic 

phenomena across that language. This demands, first of all, that the data be adequately 

large in order to avoid the problem of data sparseness that plagues many statistical 

approaches. For example, for tasks such as word sense disambiguation, data must be 

extensive enough to ensure that all senses of a polysemous word are not only represented, 

but represented frequently enough so that meaningful statistics can be compiled. 

Although it has been extensively used for natural language processing work, the million 

words of a corpus such as the Brown Corpus are not sufficient for today's large-scale 

applications: many word senses are not represented; many syntactic structures occur too 

infrequently to be significant, and the corpus is far too small to be used for computing the 

bi-gram and tri-gram probabilities that are necessary for training language models for 

speech recognition. 

Unfortunately, in the main, the large corpora freely available for research consist of texts 

that can be easily acquired and are available for redistribution without undue problems of 

copyright, etc. Because of this, corpora used for statistics gathering for language 

processing are vastly over-representative of certain genres, in particular newspaper 

samples, which constitute the greatest percentage of texts currently available from, for 

example, the LDC, and which also dominate the training data available for speech 

recognition purposes. Other available corpora typically consist of technical reports, 

transcriptions of parliamentary and other proceedings, short telephone conversations, and 

the like. The upshot of this is that corpus-based natural language processing has relied 

heavily on language samples representative of usage in a handful of limited and 

linguistically specialized domains. This can lead to drastically skewed results: for 

example, in newspaper data, there is a disproportionate number of complex NP 

complements for some verbs, which appear in sentences typical of newspaper style, such 

as "The price rose two percent to 102 dollars per share from 100 dollars per share." 

Similar problems arise in work on word sense disambiguation: it has been noted that for 

some typical test words such as "line", certain senses (for example, the common sense of 

"line" as in the sentence, "He really handed her a line") are absent entirely from resources 

such as the Wall Street Journal. 

The problem of balance is acute in speech recognition. Speech recognition systems are 

notoriously dependent on the characteristics of their training corpora. Corpora large 

enough to train the tri-gram language models of modern speech recognizers (many tens 

of millions of words) are invariably composed of written rather than spoken texts. But the 

differences between written and spoken language are even more severe than the

differences between balanced corpora like the Brown and newspaper corpora like the 

Wall Street Journal. Therefore, whenever a state-of-the-art speech recognition research 

effort moves to a new domain, a new large training corpus of speech must be collected, 

transcribed at the word level, and the transcription aligned to the speech. 

Language analysis 

The gathering of authentic language data from corpora enables a description of language 

that starts from the evidence rather than from imposing some theoretical model. Because 

speakers and writers produce language with real communicative goals, corpora of nativespeaker 

texts provide, in principle, samples of genuine language. For this reason, one of 

the oldest uses of corpora is for dictionary-making, or lexicography, and in particular, 

lexicography with the goal of producing so-called "learners' dictionaries" designed for 

those learning a new language. 

The COBUILD corpus was compiled starting in the early 1980s, at which time it 

included about 7 million words. This corpus was used to create the Collins COBUILD 

English Dictionary, the first dictionary relying fully on corpora for its creation. Following 

this lead, over the course of the next decade most British dictionary publishers began to 

use corpora as the primary data source for their dictionaries, although interestingly, 

American dictionary publishers are only now beginning to rely on corpora to guide 

lexicography. 

The basic lexicographic tool for analyzing a corpus is a concordancer, a program that 

displays occurrences of a word in the middle of a line of context from the corpus. 

However, the huge increase in available data has led to a situation where lexicographers 

are presented with hundreds or even thousands of concordance lines for a single word. As 

a result, lexicographers have begun to rely on techniques derived from computational 

linguistics to summarize concordance data. The most common is the "Mutual 

Information" (MI) score, a statistical measure that shows how closely one word is 

associated with others based on the regularity with which they co-occur in context. For 

example, in English, the mutual information score for the words "strong" and "tea" is 

much higher than the score for "powerful" and "tea", despite the fact that "strong" and 

"powerful" have similar meanings. This kind of information is invaluable for 

lexicographers, especially for the creation of language learners' dictionaries that must 

provide this kind of distinction. 

Most recently, dictionary makers have teamed with researchers in the field of 

computational linguistics to glean even more precise information from corpus data. For 

example, MI scores can show that "strong" collocates with northerly, showings, believer, 

currents, supporter, and odor, while "powerful" collocates with words such as tool, 

minority, neighbor, symbol, figure, weapon, and post; but the MI score does not indicate 

the kinds of grammatical relations that exist among a word and its collocates. 

Supplemental grammatical information can provide even more precise understanding of 

word usage. For example, the word "tea" collocates with words like spoon, milk, and 

sugar when these words appear as the object of the preposition "with"; with mug, saucer,

and cup when they are objects of the preposition "into"; with coffee, toast, sugar, etc., 

when joined by "and" or "or"; and "tea" is most commonly the object of verbs such as 

drink, sip, pour, finish, and make. 

To gather this information, relatively sophisticated language-processing software is 

required that can annotate the data for one or more of the types of information outlined in 

the previous sections. The need for more informative results to serve the needs of 

lexicography has therefore led to increased collaboration with computational linguists. 

We see, as a result, two groups of researchers who had previously worked independently 

coming together to tackle common problems. 

In general, researchers in the humanities and researchers in computational linguistics 

have not collaborated, despite their common problems and goals. With the advent of the 

World Wide Web, this should change soon. Humanists have increased access to 

information about work in computational linguistics as well as to tools and resources 

developed by that community. Computational linguists, on the other hand, are likely to 

face new language processing challenges due to the need to handle a greater variety of 

web-accessible materials, including literary works, historical documents, and the like. 

The eventual collaboration of these two groups should lead, in the end, to vastly 

increased capabilities for both. 


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22. 

Electronic Scholarly Editing 

Martha Nell Smith 

Though they are not bound together in this multi-authored, multi-edited volume and are 

not likely to be in any other, this essay is the second in a series I am writing musing on 

the effects and meanings of the kinds of electronic scholarly editing, work extending well 

beyond the spatial and typographically fixed limitations of the codex, which I and others 

have been doing and that has become increasingly visible over the past decade. Johanna 

Drucker's observations about the nature of writing as she reflects upon the word itself 

convey well, in order to reflect upon editing, its nature and character. Writing (editing) is 

"a noun as well as a verb, an act and a product, a visual and verbal form, the composition 

of a text and trace of the hand"; "letters, words, and pictorial elements all participate in 

producing a work with complex textual value. At its most fundamental writing is 

inscription, a physical act which is the foundation of literary and symbolic activity" 

(Drucker 1998: 3). Editing is a physical as well as a philosophical act, and the medium in 

which an edition is produced (or an edition's place in the material world) is both part of 

and contains the message of the editorial philosophies at work. As Adrienne Rich 

remarked about poetry (in "North American Time"), so editing "never stood a chance / of 

standing outside history." Indeed, we have entered a different editorial time, one that 

demands the conscious cultivation of and by many hands, eyes, ears, and voices. While 

print editions are containers for static objects, artifacts that are by definition 

unchangeable once produced, the world of digital surrogates practically demands new 

models for editorial praxes in which editors and readers work together. Such models are 

encouraged by the fact that in a world with access to photographic copies of texts and 

images, no one has to bear the burden of forging the perfect linguistic description of the 

artifact, and by the fact that digital artifacts are by definition alterable once produced. 

After all, digital surrogates featuring high-quality color images of a writer's manuscripts 

offer a more ample sense of their textual conditions, including the conditions of the 

writing scene in which they were produced. Informed more fully about textual conditions, 

readers can collaborate with the postulating editor in making the editorial artifacts for 

electronic media in ways not possible when decisions have already been made to exclude 

or include data and seal the resulting artifact into a printed state. 

That this series of my essays examining and hypothesizing about these new editorial 

praxes does not share the binding of a book's spine is appropriate for a critique of work

on scholarly projects that do not have clear end points – the boundaries that variorums 

and reading editions (such as the printed volume of Emily Dickinson's writings on which 

I collaborated) do. The first essay – "Computing: What's American Literary Study Got to 

Do with IT?" – was directed toward my Americanist colleagues who have not embraced 

new media for their scholarly work. The present essay is directed toward upper-level 

undergraduate and graduate students and instructors, seasoned scholars and editors, as 

well as other interested readers, many of whom may have embraced new media for their 

academic work while others may just be exploring the territory, as it were. Like that first 

essay, this one is concerned with the changing sociologies surrounding new media critical 

endeavors, and is focused particularly on the impact of those sociologies on the work and 

workers involved in electronic scholarly editing and the development of its principles 

(described in Part Two of this book). The changes evident in digital humanities in how 

we work in, as, with, and for groups constitute a profound shift in humanities knowledge 

production. The new editorial praxes made possible, indeed demanded, by the critical 

environments that new media call into being are pivotal to that shift. Those editorial 

praxes are not only made visible, but are constituted by some of the new technologies in 

digital humanities, technologies that have gone under-remarked, even by specialists. 

These praxes and technologies are this essay's primary foci, because they make possible 

the digital resources that, as I remarked in my earlier piece, are more than advantages for 

humanities knowledge production: they are necessities. As such, these praxes and 

technologies should be taken into account by anyone working or interested in humanities 

knowledge production. 

Technologies bring scientific knowledge to bear on practical purposes in a particular 

field, and are thus the means by which we accomplish various ends – the tools and 

devices on which our critical suppositions rely. Some digital technologies, the machines 

and software with which we produce and consume scholarly editions, are prey to 

obsolescence (HyperCard, for example, is for all practical purposes already unusable, as 

are DOS-based machines; the SGML document delivery system DynaWeb has 

limitations, at least aesthetically, that are much lamented; examples abound). By contrast, 

all but one of the technologies under review here were already used in some form for 

book production and are obviously not susceptible to such rapid obsolescence. In fact, in 

migrating to the electronic realm the technologies have become much more visible and 

productively exploitable. Constitutive of digital humanities, the technologies are: access, 

multimedia study objects, collaboration, and increased self-consciousness. Central to the 

formulations and speculations of this critique is evaluating the effects of a technology 

implicitly invoked by all four, that of audience. If one considers a sense of audience a 

technology (with explanation and performance as kinds of knowledge application), then 

the technology of audience provides analytical perspectives that would not have been 

obtained had I been writing with only one audience in mind. Those different viewpoints 

have in turn deepened my understanding of just how profound are the shifts under way in 

scholarly applications of digital resources to the study of arts and humanities. That I and 

other enthusiasts have underestimated the degree of that profundity is remarkable. 

Finally, this chapter makes explicit the importance of standards, as well as the importance 

of querying the prides and prejudices of those standards and the implications for 

interpretation made possible in this new editorial work, invoking as a representative

touchstone the new Guidelines for Electronic Editions just approved for use by the 

Modern Language Association (MLA)'s Committee on Scholarly Editions. 

The fruits of several evolutions converged to produce a mode of editing suited to the new 

media: post-structuralist re-inflections of theories of editing; the proliferation of 

affordable, portable personal computers; the networking, on an unprecedented scale, of 

homes, individuals, institutions of all sorts (educational, governmental, commercial, 

religious, and medical), and nations. A fantasy unimaginable as part of the real world just 

a very short time ago, such networking and the accelerated communication it brings are 

now facts of everyday life throughout much of the world. Concomitantly, academics have 

moved beyond worried resistance to the work made possible by new media and computer 

technologies, and worked through much of the reactionary skepticism so widely and 

forcefully articulated in the 1990s, in books like Sven Birkerts's The Gutenberg Elegies 

(1994). By contrast, at the time of writing, under-informed skepticism has been replaced 

by the realization that critical engagements with new technologies are the best hope for 

advancing knowledge production in the humanities. That these advances are within our 

grasp is abundantly clear in the field of scholarly editing. 

For example, one happy consequence of new media revolutions is that breathtaking 

advances in networking and communication have enabled editorial praxes previously 

thought too impractical to enact, whatever their intellectual value. Instead of being bound 

to representative samples, editions can now include images of all primary documents 

included in an edition. The surrogates possible in digital humanities are more than just 

singular photographic examples meant to represent hundreds or even thousands of 

instances. Thus editorial claims to being "comprehensive" (a staple of the print variorum 

relying on representative examples) accrue whole new meanings in the extraordinarily 

capacious digital realm. Ironically, though, electronic textual editors would not be likely 

to make such claims, realizing just how vast is the information one could choose to 

include in a "comprehensive" edition. Those same editors would nevertheless demand 

inclusion of images of all primary documents (manuscripts or corrected proofs, for 

example) rather than only the analytical descriptions thereof one finds in the editorial 

apparatus of print translations (iterations mechanically reproduced for public distribution 

and annotated for scholarly consumption). Also, editions can now implement new kinds 

of scholarly annotation by including, where applicable, sound and even video 

reproductions simply impossible to contain as a constitutive part of a book (though a CD 

might be slipped inside the cover as a companion or a chip ingeniously packaged and 

placed for a reader's play). 

Networking and communication have also made imaginable and agreeable editorial 

praxes traditionally thought impracticable or undesirable. These practices modify the 

very nature of scholarly editing itself. For example, if an editor chooses, the much 

theorized common reader can now provide immediate and actual (in contrast to 

imagined) critical feedback that might in turn be usefully incorporated into the work of a 

scholarly edition, making the collaboration between reader and author that is 

characteristic of reading itself something more than the "creative regeneration" [of texts] 

by readers invested with and in private intentions such as those D. F. McKenzie describes

in Bibliography and the Sociology of Texts (1999). All of these factors will be examined 

in this essay as examples of under-analyzed technologies, but first some observations 

about editing and the phrase that provides my title are in order. 

Distinguished editing of literary and artistic work is a matter of the heart (emotions), and 

at least four of our five senses (physical perceptions), as well as of the head (logic). 

Astute editors take advantage of the acumen offered by these diverse ways of 

apprehending the world, and though it is in one sense bound by algorithms, digital editing 

is no exception in terms of its characteristics (of the heart, the physical senses, the head). 

Reflecting on the individual terms embodied by the phrase, and the traditions they 

bespeak, deepens and broadens understandings of "electronic scholarly editing", both in 

its present forms and in the theories about those praxes and the possibilities that inhere in 

multimedia. Editing makes works (poems, plays, fiction, film footage, musical 

performances, and artistic and documentary material) publishable (in books, films, 

television and radio, and recordings) by eliminating unwanted material and organizing 

what remains for optimal and intelligible presentation to audiences. In other words, 

editing translates raw creative work into an authoritative (not to be confused with 

definitive or authoritarian) form. Scholarly editing is editing performed under the aegis of 

research, learning, sustained instruction, mastery, knowledge building, standard setting. 

Electronic scholarly editing consciously incorporates phenomena associated with the 

movement and manipulation of electrons, those indivisible charges of negative electricity, 

through wires and radio waves onto screens and through speakers. Though it incorporates 

the principles of conventional scholarly editing (that is, in and for books) into its own 

methods, electronic scholarly editing is not performed to be read through the machine of 

the book but via the hardware of computers. Other nomenclatures for this field include 

"digital scholarly editing", exploiting to the fullest the obvious pun that enables clear 

contrastive play between the manual and the automated, and those pertaining to editing 

itself, especially in the world of ubiquitous computers and the connective possibilities of 

the World Wide Web. Electronic scholarly editing now inevitably involves networking, 

both among the texts being worked through and the workers performing the editing 

(including readers viewing the edited works), in ways never realized by that magnificent 

tool of knowledge transmission that has served so splendidly well for the past several 

centuries, the book. 

Previously I have examined the implications for knowledge production of the fact that in 

the digital environment, access – both to numbers of study objects and to numbers of 

audience members – is facilitated on an unprecedented scale. I considered at length the 

possibilities for literary criticism and theory when the staggering data storage capacities 

of computers enable so much more visibility of what I refer to as BE-O objects – artifacts 

that have customarily been viewed By Experts Only. Agreeing that the skepticism with 

which many mainstream scholars regard "quantitative research methods" was not really 

misplaced "so long as the computer's primary role in the humanities was, ostensibly, to 

compute", Matthew Kirschenbaum proclaims that access to a digital surrogate of a 

"Rossetti painting" or "of one of Emily Dickinson's turbulent manuscripts" erases or at 

least reverses that skepticism. The visibilities that enable the unprecedented access to 

images that were previously locked away in library and museum archives for exclusive

view by a very few is probably the technological boon with which scholars and readers 

are most familiar, for that perpetually available and repeatable access is really quite a big 

deal. The ability to provide artifacts for direct examination (rather than relying on 

scholarly hearsay) has altered the reception of humanities computing in the disciplines of 

the humanities so that skepticism is "at least replaced with more to-the-point questions 

about image acquisition and editorial fidelity, not to mention scholarly and pedagogical 

potential." These are indeed "questions of representation, and they are eminently relevant 

to the work of the humanities" (Kirschenbaum 2002: 4). As Ray Siemens's introduction 

to A New Computer-assisted Literary Criticism? makes plain, enthusiasts of text analysis 

would take issue with Kirschenbaum's assertion about quantitative research methods, but 

the point about the power of visibility, of making accessible for as many pairs of eyes as 

possible, images (rather than their analytical textual descriptions) for critical analysis, 

cannot be overstated. When editors make as much about a text visible to as wide an 

audience as possible, rather than silencing opposing views or establishing one definitive 

text over all others, intellectual connections are more likely to be found than lost. 

Though it will not necessarily do so, that access to primary digital artifacts can in turn 

create extraordinary access to the editorial process itself, especially if editors and readers 

alike are proactive in taking advantage of these opportunities by building "sound 

infrastructure, and the organizational and financial structures … essential to consolidate, 

to preserve, and to maintain" the "new organizational forms" created by electronic media 

(D'Arms 2000). The standards described by the MLA's Guidelines for Scholarly Editing, 

print and electronic, are commonsensical and bound to ensure quality: they ask for 

"explicitness and consistency with respect to methods, accuracy with respect to texts, 

adequacy and appropriateness with respect to documenting editorial principles and 

practice." However practical and necessary for meeting principled goals and however 

derived by scholarly consensus, uses of various media constrain and enable adherence to 

these benchmarks to different degrees. Indeed, one clear example lies in the fact that 

explicitness, consistency, and accuracy are all finally matters of faith in the bibliographic 

realm. 

Considering an actual instance will make clear the importance of enhanced visibility and 

the more critical reading it enables. My examples will be drawn from the editorial 

resources with which I have been most involved, both as a user and a maker, those of the 

writings of the nineteenth-century American poet Emily Dickinson. Because the practices 

involved in their making are so widespread in the production of scholarly editions, and 

similar principles to those employed in producing Dickinson are so widely accepted for 

making accessible the texts of other authors, these specific instances serve well as 

exemplifications of general rules and procedures for scholarly textual production. R. W. 

Franklin spent scrupulous decades producing a three-volume variorum edition of The 

Poems of Emily Dickinson for Harvard University Press, a publisher highly esteemed for 

producing study objects meeting the highest standards. Franklin's stated goal is to give a 

"comprehensive account" of all of Dickinson's texts, as well as any texts directly bearing 

on her compositional practices (1998: 28). In a comprehensive account, one would expect 

to see all omissions, additions, alterations, emendations of which the editor is aware 

marked in some way for his readers. Thus any ellipsis, any omission of data, would be

signaled by familiar punctuation (…) and probably explained in editorial notes. Yet this 

is not the case, and readers cannot readily know when an omission has occurred or a 

change silently made because their access to the objects edited is limited and the evidence 

of explicitness, consistency, and accuracy cannot be seen. Instead, readers have to trust 

that the printed texts they see are explicit, consistent, and accurate about all textual data. 

Thus when Franklin erases 74 words from his representation of an epistolary manuscript 

involving the composition of Dickinson's poem "Safe in their Alabaster Chambers" and 

does so without commentary and without signifying punctuation, readers of his print 

variorum have no way of knowing that the omission has been made, much less of 

evaluating its import (FP 124C). Besides being misinformed about the nature of this text 

because of omission of information, access to the editorial process itself is limited, the 

implicit contract with the reader (that such treatment of documentary evidence would be 

signaled in some readable way) having been violated. Franklin's change might, one could 

argue, simply be chalked up to bad editorial work. Yet he silently enacts other 

emendations as a course of standard, widely accepted editorial practice. Much ink has 

been spilled by Dickinson and American poetry scholars about her use of punctuation 

marks that most have labeled "the dash." Strong positions have been taken about whether 

the shorter en- or the longer em-mark most faithfully translates Dickinson's mark into 

print, and Franklin has resolved the matter with the authoritarian stance that neither the 

en- nor the em-suffice: according to him, the shorter-than-either hyphen best conveys 

Dickinson's practice of using a horizontal or angled mark rather than a comma. The 

problem with staking out a hard and fast position on this matter (as many a Dickinson 

critic has done) is the "one size fits all" perspective practically required by print 

production. Examining almost any poem written during Dickinson's middle years shows 

that within individual poems, indeed within individual lines, she did not consign herself 

to a single sort of mark but used marks of varying lengths and angles (see, for example, 

"The name -of it -is 'Autumn'___" (FP 465; facsimile available in Franklin's Manuscript 

Books of Emily Dickinson, p. 494). Editing nearly 1,800 poems, Franklin makes the 

decision for readers that a hyphen will suffice to represent Dickinson's diverse, varied 

marks, and thus also decides for readers that the differences among her punctuating 

inscriptions are not poetically significant. 

Trust in any print edition, then, whether it be that made by Franklin or any other editor, 

including myself, is necessarily faith-based, for readers cannot adequately see the 

documentary evidence that determines everything from genre to suitability for inclusion 

in a scholarly edition. Indeed, such matters of representation are more than eminently 

relevant to humanities; they are central to humanities knowledge production itself. 

Opportunities for analysis of who made the scholarly objects we study and for what 

purposes, and of which parts of those objects are constitutive and worthy of study, are 

proportionate to the visibility of foundational documentary evidence enabled by the 

medium of representation. 

In contrast to the constrained visibilities of book representations, access to questions of 

editorial fidelity and therefore to the editorial process itself is much more obtainable in an 

electronic edition featuring images of all documents edited as well as their translations

into typography. In such a realm, 74 words cannot simply be excised without 

commentary and marks translated with a "one size fits all" authoritarian stance and go 

unnoticed, because a digital image of the document is available for readers to view, 

assess, compare with editors' representations. Representation via digital facsimiles of 

original documents changes access to the foundational materials of scholarly editions, the 

contours of expertise, and even standard-setting itself. 

Consciously foregrounding the ontological differences between electronic and 

bibliographic scholarly editions is necessary for understanding ways in which this access 

can work, for learning how electronic editions made might best be used, and for planning 

and developing electronic editions that are more and more well made (in that they are 

designed for optimum usability as well as optimum incorporation of relevant but 

scattered data and commentary). Introducing Harvard University Press's latest variorum 

edition of The Poems of Emily Dickinson, Franklin claims that although that threevolume 

edition "is a printed codex", it is practically electronic, for the edition "has an 

electronic database" and the "poems are in bits and bytes." He then flatly states that 

"other outputs are possible, including other printed editions, organized or presented 

differently. Dickinson and Hypertext may well be matched, and images are particularly 

useful with an unpublished poet who left her poems unprepared for others" (1998: 27–8). 

Yet electronic editions are by their very constitution markedly different, both from the 

print edition Franklin made and the hypertext one he imagines. For one thing (which he 

acknowledges), the bits and bytes to which he refers do not contain images and thus do 

not provide multifaceted viewpoints, the visibility of digital surrogates of original 

manuscripts presented in an environment where the distance that separates the libraries in 

which they are held presents no obstacle for direct comparison of texts within each of 

those collections spread across oceans and nations, separated by geography, national 

boundaries, and disparate laws. Collected into an electronic edition, Blake or Dickinson 

manuscripts logically related to one another but dispersed across various collections are 

gathered together so they can be studied and synthesized to make new knowledge; 

collected into the same electronic edition, they no longer lie like scattered pieces of a 

puzzle, their connections splintered, difficult to see as possibly yoked, and thus next to 

impossible to imagine. Another difference unacknowledged by Franklin is that the 

typographical bits and bytes to which he refers are those of word processing, which only 

encode the textual data found in Dickinson's works. A scholarly electronic edition would 

likely be prepared in XML or SGML (probably following the guidelines of the Text 

Encoding Initiative), and would capture logical, structural, and artifactual aspects of the 

original, as well as its textual content. An electronic edition also might include databases, 

Flash, video, and sound presentations. Different editions are not simply "outputs" of the 

same bits and bytes, which Franklin's declaration assumes. The statement makes plain 

that he envisions electronic editions as typographically edited texts with illustrations that 

can be accessed with the click of a mouse. But electronic scholarly editions provide much 

greater opportunities than texts with links to illustrations and to one another, enabling a 

range of manipulation not possible for those bound to the printed page. 

Far more than display, replications constitute the textual reproductions and 

representations in scholarly editions such as The Blake Archive, the Rossetti Archive, The

Walt Whitman Archive, The Canterbury Tales Project, the Dickinson Electronic 

Archives. Scholarly electronic editions are not simply a matter of bits and bytes and how 

they have been encoded to link to one another and to appear in particular browsers. 

Dynamic rather than static, deeply encoded, they are designed to enable readers to 

achieve better understanding of texts and open up possibilities for more sophisticated 

interpretations. Susan Schreibman and others have chronicled the fact that by the mid- 

1980s it had become clear to numbers of scholars already involved in the creation of 

electronic resources for the humanities that a standard for encoding texts needed to be 

developed that was not dependent upon a particular platform or software package 

(Schreibman 2002a). Achieving platform-independence requires separating the 

information content of documents from their formatting, which is what led an 

international group of those concerned with publishing and preserving government 

documents to develop GML (Generalized Markup Language) in the 1970s, and then 

SGML (Standard Generalized Markup Language), adopted as an international standard in 

the mid-1980s. Working with SGML, a group of humanities scholars and librarians 

launched the Text Encoding Initiative (TEI) in 1987. 

Though markup has a performative significance, the TEI is designed to represent already 

existing literary texts. For electronic scholarly editors, "markup is supplied as part of the 

transcription in electronic form of pre-existing material…. Markup reflects the 

understanding of the text held by the transcriber; we say that the markup expresses a 

claim about the text" (see Allen Renear, in chapter 17, this volume, and in writings 

elsewhere, and many others, for example Susan Hockey, Michael Sperberg-McQueen, 

Jerome McGann: see entries in the References for Further Reading). John Unsworth's 

claim that "any attempt to systematically express one's understanding of a text in the kind 

of internally consistent, explicit and unambiguous encoding that is required in the 

creation of a computable SGML or XML edition will produce some intellectual benefit 

and will ensure some degree of integrity", is indisputable (Unsworth, website). Indeed, 

the technology of self-consciousness required by computer encoding of texts, especially 

that such as TEI-conformant XML encoding, produces a healthy self-consciousness about 

what Bruno Latour and Steve Woolgar describe in Laboratory Life as "black-boxing" – 

which occurs when one "renders items of knowledge distinct from the circumstances of 

their creation" (1986: 259n). In black-boxing, critical opinion becomes "fact"; more often 

than not, amnesia sets in after that factual instantiation, and having been effectively 

black-boxed, "fact" becomes "truth." As in the case of Dickinson discussed here, 

wittingly or unwittingly bibliographic editors sometimes remove elements that are (or 

well might be) constitutive of its poetics when transmitting a text from the messy 

circumstance of its creation to the ordered pages of print. The challenge for electronic 

scholarly editors is not to perpetrate similar distortions when enthusiastically embracing 

the ordering tools of encoding to convey literary expression, which by its very nature 

often perplexes the orders of expository logic. 

Hypertext theorists and practitioners, who have been (sometimes roundly) critiqued for 

being too exuberant, are sometimes echoed by the encoding specialist's enthusiasm for 

important developments in XML: "One of the most powerful features of the XML family 

of technologies is Extensible Stylesheet Language Transformations (XSLT), a language

that facilitates transformation of text rather than display (as HTML's Cascading Style 

Sheet language does)…. The 'infinitely recenterable system whose provisional point of 

focus depends on the reader' (Landow 1997, p. 36) is made realizable through XML- 

XSLT technology, and is much closer to the democratized space envisioned by early 

hypertext theorists" (Schreibman 2002b: 85). Without a doubt, though, new technologies 

for structuring data, coupled with the guidelines provided by the Committee on Scholarly 

Editions and the TEI's commitment to work out of a "broad, community-based 

consensus", are crucial for realizing advances in editorial praxes. Also necessary is the 

commitment to facilitate difference by evolving "guidelines … to accommodate a greater 

variety of editorial methods and a broader range of materials and periods" (MLA 

Committee on Scholarly Editions, website), and by the TEI's "extension mechanism, as 

well as its consistent insistence… on international and interdisciplinary representation in 

its governing bodies, its workgroups, its funding sources, and its membership" 

(Unsworth, website). The collaborative development of these standards is absolutely 

essential for any demotic ethic valued as a worthwhile goal, and the disambiguation 

forced upon knowledge workers who thrive on ambiguity often proves critically 

profound, because texts are seen as never before, both on the surface and in their deep 

structures. 

Yet none of these advances in markup, nor any of these guidelines, is robust enough to 

accommodate all facets of the actual textual experience of editors working with primary 

artifacts. Original documents, the raw materials with which editors must work, are by 

their very nature queer, and must be normalized to some degree in order to be put into an 

edition. In part, this is a result of the fact that "there are certain basic – irresolvable – 

philosophical tensions in language – most particularly between its capacity to represent 

knowledge systematically and its capacity to be knowledge experientially and 

perceptually – and this tension intensifies in an electronic environment as a tension 

between machine language and natural language – since it is a reductive hybrid version of 

the one which can be encoded/encrypted in order to serve as the basis of the other" 

(Drucker 1998: 219). This characteristic of language itself is much more visible in an 

environment in which 

the more sophisticated we are the more we normalize textual incommensurables. We 

have internalized an immensely complicated, many-leveled set of semiotic rules and 

signs, and we control the contradictions of actual textual circumstances by various 

normalizing operations. We can hope to expose these normalizations – which are 

themselves deformative acts – by opening the conversation… between analogue and 

digital readers. We begin by implementing what we think we know about the rules of 

bibliographical codes. The conversation should force us to see – finally, to imagine – 

what we don't know that we know about texts and textuality. At that point, perhaps, we 

may begin setting philology – "the knowledge of what is known", as it used to be called – 

on a new footing. 

(McGann 2001: 207)

In a case such as the Dickinson Electronic Archives, the editions are explicitly designed 

not to define and normalize texts, as has been the objective of most bibliographic 

scholarly editions, and has been the practice of many electronic scholarly editions. Many 

electronic editions are still framed by the good work of the book because the dream of 

shedding the inertia imported from the bibliographical realm is only gradually finding its 

modes of practice. The TEI was developed to represent already existing literary texts in 

electronic media, but in its years of development it has been confronted by queerer and 

queerer texts and by editors whose commitment to integrity means that they cannot 

simply normalize those texts to fit a predetermined grid. This, in turn, demands that we 

grasp the real significance of the truism that editing is a kind of encoding and encoding is 

a kind of editing, and it also requires that we probe the politics of the encoding standards 

we are embracing. Readers too 

rarely think about the myriad of databases, standards, and instruction manuals subtending 

our reading lamps, much less about the politics of the electric grid that they tap into. And 

so on, as many layers of technology accrue and expand over space and time. Systems of 

classification (and of standardization) form a juncture of social organization, moral order, 

and layers of technical integration. Each subsystem inherits, increasingly as it scales up, 

the inertia of the installed bases of systems that have come before. 

(Bowker and Star 1999: 33) 

Besides asking who made our objects of study, generative questions about standard 

setting, who made them, and for what purposes should be posed, and in ways, as McGann 

argues, not previously imagined. "There is more at stake – epistemologically, politically, 

and ethically – in the day-to-day work of building classification systems and producing 

and maintaining standards than in abstract arguments about representation" (Bowker and 

Star 1999: 10). Standards are of course crucial for realizing reliability, sustainability 

(both in terms of being intellectually substantive and in terms of residing in a preservable 

medium), and interoperability among different works and even different systems. Those 

editing for new media are carrying on this responsibility by working with and helping to 

develop the new international standards and guidelines. In this ongoing work, we must 

self-consciously pose questions about the consequences of standardizing, classifying, and 

categorizing. In previous scholarly editions, classifications or the qualifying criteria 

dictating how an entity would be classified have been invisible. One benefit of encoding 

is "to understand the role of invisibility in the work that classification does in ordering 

human interaction" and thus to keep an eye on the "moral and ethical agenda in our 

querying of these systems. Each standard and each category valorizes some point of view 

and silences another. This is not inherently a bad thing – indeed it is inescapable" 

(Bowker and Star 1999: 5). Standards and categories become problematic when they are 

insufficiently critiqued, and when "a folk theory of categorization itself" prevails. That 

folk theory "says that things come in well-defined kinds, that the kinds are characterized 

by shared properties, and that there is one right taxonomy of the kinds" (Lakoff 

1987:121). Both the TEI Consortium and the MLA's Committee on Scholarly Editions 

have said that principled accommodation is necessary, and that's as it must be, if 

electronic scholarly editing is to realize its potential by pre-empting this compulsion

toward standard setting as a kind of intellectual police force. The community has 

established standards that are flexible and adjustable, adopting what in the builder's trade 

is called a lesbian rule – a mason's rule of lead, which bends to fit the curves of a 

molding; hence, figuratively, lesbian rules are pliant and accommodating principles for 

judgment (OED). Commitment to such principles for judgment must be vigilantly 

maintained, with editors relentlessly asking: "What work are the classifications and 

standards doing? Who does that work? What happens to the cases that do not fit the 

classification scheme? What happens to cases in which several classification schemes 

apply?" After all, automatons do not make literary texts: people do. 

Understanding the poetics and principles of electronic scholarly editing means 

understanding that the primary goal of this activity is not to dictate what can be seen but 

rather to open up ways of seeing. The disambiguating codes are tools to understand texts, 

not to define them. Though consensus may develop around certain elements of what is 

seen, no consensus need be developed on how to read those elements. In fact, the 

different perspectives offered via the encoding, image-based digital surrogates, and direct 

critiques from "common" readers that are possible in electronic scholarly editing create a 

climate of possibility for interpretation that is precluded by the invisible controls placed 

on literary works and their interpretation in bibliographic editing. The editorial goal of 

electronic scholarly editing is not to find the proper literary container for "poems" (or 

whatever genre is being produced) but to find the medium that transmits more, rather 

than fewer, of the techniques inscribed and found on the literary page. Propriety (which 

inheres in the notion of the proper container) is not the issue, hence the TEI's extension 

mechanism. The issue is textual pleasure and access to greater understanding, and 

enabling audiences to avail themselves of as many aspects as possible of the scriptures 

under study. 

So in scholarly editing, the challenge of using these electronic tools that create so many 

advantages for storage of data (including sound and images), retrieval, and searching is to 

develop them so that editorial praxes themselves are truly advanced and the hieratic ethic 

of editing for books is not simply imported into a new, more proficient medium (as was 

originally the case with the TEI). Derrida's deconstruction of the meanings of "archive" 

(Derrida 1996) and its connotations of "commandment" and "commencement" help 

clarify distinctions between hieratic and demotic and show why the latter is an advance 

when it comes to editorial approaches. By tracing his emphasis on a hierarchy of genres, 

one can see that commandment – stressing authority, social order – describes the guiding 

principle of Franklin's variorum. Thus in spite of his emphasis on versioning in his 

iteration of 1,789 Emily Dickinson poems, order is imposed on that which is otherwise 

unruly – the messy handwritten artifacts of poems, letters, letter-poems, scraps, notes, 

fragments. The idea of "poem" disciplines and contains views and the version represented 

of Dickinson's writings so that they conform to social order and literary law, whatever the 

material evidence may suggest. According to this principle of commandment, the material 

evidence, the manuscripts, contain the idea of "poem" and an editor's job is to deliver that 

idea in a container that makes "poem" extractable. Here textual boundaries are clear, 

commanded as they are by the ideas that demarcate genres for books. The temptation in 

disambiguating markup is to repeat that strategy of containment, for it is much easier

simply to claim that a text is prose or poetry rather than to acknowledge that it is much 

more complicated than that, a blend of the two, and requires innovative extensions of the 

predetermined TEI markup schemes. 

By contrast, commencement – physical, historical, ontological beginning – describes the 

guiding principle of the Dickinson Electronic Archives' production, including our 

markup. Unpersuaded that "poem" is an "idea" easily separable from its artifact, the 

editors of the electronic archives feature images of Dickinson's manuscript bodies in their 

multiple sizes and shapes, in all their messiness. Though our markup designates verse, 

letter, verse-letter, letter with embedded verse, and letter with enclosed verse, what 

constitutes a "poem" and poetic meanings is left up to the reader 

(http://jefferson.village.virginia.edu/dickinson/nosearch/documentation/). A work might 

sport a stamp or a cutout from Dickens placed by Dickinson into the literary scene that 

the reader deems part of a "poem", and the editors of the electronic archives refuse to 

bind those elements as extra-literary but put them on display and include them in the 

claims our markup makes about the text. Finger smudges, pinholes, paste marks, coffee 

stains, and traces of ribbons, flowers, or other attachments offer a view into the 

manuscript circulation and exchange so central to Dickinson's literary world, and we 

likewise put them on display and include questions about such signs in our editorial 

submission forms so that markup and notes recognize these materialities as part and 

parcel of literary production and circulation. Also featured are images of the printed 

pages, the bodies that have transmitted Dickinson's writings to the world, and in these are 

stories of Dickinson's history as a poet whose writings are read and enjoyed by a wide 

audience. The tidy organizations of those printings bound into The Poems of Emily 

Dickinson and The Letters of Emily Dickinson juxtaposed with the not fully intelligible 

bindings of manuscripts (into the manuscript books found in her room or her 

correspondences to 99 recipients) by Dickinson herself, as well as with her many writings 

unbound (single sheets, notes, drafts, fragments, scraps), renew ontological questions 

about the identities of these many writings. Textual boundaries are not clear, hence on 

our submission form for co-editors, the word "genre" in the question asking them to 

select one for encoders is in quotation marks to underscore the fact that such denotation is 

forced by the encoding scheme. Underscored both in the markup and the display of 

original documents is the fact that though an ideal idea of "poem" or "letter" must 

dominate for the writings to be neatly divided by bibliograph-ically determined genre, 

and though some denotation must be made for the meta-information of markup, 

electronic editing enables a much more flexible approach to understanding Dickinson's 

writings and her manipulations of genre. With many of the documents, readers cannot 

help but begin to ask "what is this?" "What is this writer doing?" The electronic archives 

are designed to enable much more extensive considerations of such questions, to confront 

the philosophical tensions in language rather than smoothing them over and masking 

them via editorial "clean-up." 

Thus if editors self-consciously work beyond the inertia inherited from bibliographic 

editing, the editorial environment made possible by these technologies of publication can 

have profound implications for the interpretation of texts and for knowledge production. 

Though editing might pretend to be objective, it is always someone enacting his or her

critical prerogatives. Presenting artistic and critical works can never be done without 

some bias being conveyed. Though the fact that editing is always interpretation was 

realized long before their incorporation into humanities work, the new media, and new 

and newly recognized technologies facilitate accommodation of this fluidity of authorial 

and editorial/authorial intentions. Editorial theorists as diverse as Tanselle, McGann, 

Bornstein, Bryant, Shillingsburg, McKenzie, and Grigely in one way or another 

acknowledge the philosophical tensions in language in which these fluidities inhere and 

point out that "writing is fundamentally an arbitrary hence unstable hence variable 

approximation of thought" (Bryant 2002: 1). In the context of these recognitions, all of 

these values are profoundly affected: authority, literariness, authenticity, sociology, 

access, reproductivity, original/originary texts/moments, editorial responsibilities, 

authorship, intention. Electronic editions are most valuable in their capaciousness, not 

only in their ability to offer different versions but also in their ability to facilitate varying 

views and valuations, both of what is seen and of how it is comprehended, as text or as 

extra-textual. 

Though major editorial projects have often involved collaborators, the collaborations 

enabled by electronic editions can be something different than previously seen in 

humanities research, as are the editions themselves. Because websites can be mounted by 

individuals and because most have not been vetted, peer review has been a contentious 

issue in the first generation of digital humanities production. Publishers, libraries, and 

other institutions invested in quality control routinely vet their digital publications in 

ways akin to those used for print publications: two or three readers' reports by experts in 

the field are presented to a board who then votes to accept or reject. This is important and 

valuable, but critical review need not stop there. In electronic publishing, tools such as 

dynamic databases enable a more sustained and collaborative critical engagement of 

reader with editor than has previously been seen. The Dickinson Electronic Archives has, 

for example, opened a critical review space 

, making user feedback an integral part 

of the editorial process. In this virtual space users have the opportunity to post 

commentary, reviews, or critical analysis of a particular section(s) or of the site as a 

whole. Because comments are stored (either anonymously or with signature) in a 

searchable database, users have the option of reading and responding to previous 

commentary. Such a space enables interactions with the editors and with readers and 

provides a generative, previously unavailable, context for critical responses. The dynamic 

interplay of the audience, the original writer who inscribes the marks, and the editors 

communicating those marks to posterity is thereby more likely to open what Dickinson 

herself would call "doors and windows of possibility." In turn, these myriad perspectives 

can enable a much more sustained reflection on how our critical findings are produced 

and then transmitted, on the mechanisms of authorization and critical review, and on the 

criteria for authenticity. These assessments can then begin to penetrate critical mystiques 

in ways likely to expand rather than restrict knowledge, and to focus attention more on 

the knowledge itself than on the individual responsible for bringing it to the fore. Access 

to such knowledge can in turn foster a variety of new co-editorial collaborations among 

authors, editors, and readers. Digital surrogates alone make definitive analytical 

descriptions, on which readers of scholarly editions have depended, neither possible nor

desirable. Instead, such analytical descriptions accompany images that readers can 

examine, make judgments about, and then use to assess the editorial commentary. The 

Dickinson Electronic Archives is complementing the Open Critical Review database by 

importing and making available to users the Virtual Lightbox 

, a software tool via which images can be 

compared and evaluated online, and the Versioning Machine 

, a software tool designed by a 

team of programmers, designers, and literary scholars for displaying and comparing 

multiple versions of texts. Both products are open source and have been produced and 

maintained at Maryland Institute for Technology in the Humanities (MITH) 

. The database and the sophisticated online tools for 

comparing images and texts make it possible for users not only to examine the editorial 

process, but even to become part of that process. Such collaborations are ways of turning 

the editorial process inside out in order to advance its integrity, its scope, and its reach, as 

is the choreography of guest co-editors working with a group of general editors. 

On multivolume editions of a major author's work, guest co-editors have worked under a 

rubric framed by (a) presiding editor(s), but electronic editions provide new opportunities 

for more extensive knowledge transfer and diverse yet coherent knowledge production in 

electronic scholarly editions. As do their print predecessors, electronic co-editors act as 

second and third readers, the first readers being the writer at work on creating text to be 

disseminated and the general editor conceiving of an edition appropriately designed for 

the electronic field. Since not simply the originating writer but also editors are creative 

agents, coordinating groups of qualified editors to work on a vast body of work rather 

than relying on a single master editor or master pair or trio of editors to bring a scholarly 

edition into being practically insures, as do the disambiguating codes of TEI-conformant 

XML, greater integrity in textual production. 

As we have seen, new materialities of editing, the fact that multimedia representations 

make it possible for readers to examine documentary evidence previously hidden from 

their view, that dynamic databases make it possible for critical readers to record and store 

their feedback in a manageable format, and that the electronic work produced by editors 

has been instantiated in forms much more manipulable than the rigid freeze of print make 

innovations in the work of editing itself possible. Teams of editors, rather than a solitary 

master with her assistants, can work on projects as never before possible. However, 

multiply edited electronic editions run the risk of inducing a kind of interpretative vertigo 

in the perspective of readers. After all, though the objective is no longer to produce the 

definitive, immutable text, electronic editions do aim to produce greater understandings. 

As readers' critical responses are managed through an online database, so editors' work 

can be managed via a database-driven editorial submission form such as that provided by 

the Dickinson Electronic Archives , 

with its guidelines for encoding, statement of 

editorial principles, and documentation with links to regularization databases, lists of 

hands identified on the Dickinson manuscripts, lists of manuscript repositories, and so 

forth.

Just as any editor has to take into account authorial intentions, and any reader is wise to 

consider both the writer's and subsequent editors' authorial intentions, multiple editors all 

working on parts of the same textual body must take into account the intentions of their 

co-editors and collaborators, those readers willing to commit time and energy and to 

abide by the principles established for scholarly textual production. Such forced 

engagements with the sociologies of intention that frame and inhere in any and all textual 

productions are immensely valuable for creating editorial environments that are not only 

more trustworthy but that are bound to advance critical understandings, teaching scholars 

to ask questions heretofore unimagined. As I noted in an earlier essay, Lucy Suchman 

makes insightful observations about the conditions necessary for optimizing knowledge 

production. Instead of viewing the "objective knowledge" proffered by a critical editon 

"as a single, asituated, master perspective that bases its claims to objectivity in the 

closure of controversy", "objective knowledge" in the production of a dynamic critical 

edition online can more easily be seen as "multiple, located, partial perspectives that find 

their objective character through ongoing processes of debate." Since critical vision is 

parallactic rather than unidimensional, the processes of comparing and evaluating those 

different angles of seeing as one compares and evaluates different images or different 

perspectives of the same images is essential in order to see more clearly and accurately. 

The locus of objectivity is not "an established body of knowledge… produced or owned 

by anyone", but "knowledges in dynamic production, reproduction and transformation, 

for which we are all responsible." By contrast, the hieratic models of the master editorial 

perspective do not acknowledge how "layered and intertwined" are the "relations of 

human practice and technical artifact" and how such individualistically driven 

productions can tend to obstruct rather than facilitate intellectual connections, treating 

editorial and critical works as "finished… achievements" rather than as ongoing research 

activities and part of a "process of accretion" of editorial technique and knowledge, part 

of midrash, as it were (Suchman, website). 

Lessons learned by the makers and users of electronic scholarly editions can help answer 

the call of the late John D'Arms and others to "move the scholarly monograph into the 

second generation of the digital age", as the electronic scholarly edition has moved into 

the first generation. As President of the MLA, Stephen Greenblatt issued a letter to the 

membership about the plight of scholarly publishing in the humanities and a call to action 

to address the "systemic, structural, and at base economic" crises confronting twentyfirst-century 

humanities scholars committed to the free flow of information that enables 

knowledge building ("Call for Action on Problems in Scholarly Book Publishing" 

); as President of the Association for Computers and the 

Humanities and Chair of the TEI Board of Directors, John Unsworth answered 

Greenblatt's call in presentations such as "The Emergence of Digital Scholarship: New 

Models for Librarians, Scholars, and Publishers" 

and in his work on the MLA 

Committee on Scholarly Editions and the TEI. 

In order to move the scholarly monograph into the digital realm, humanists need to 

embrace the new technologies developing in digital humanities communities of practice, 

technologies making not only new work but new ways of working possible, especially

those that will not become obsolete (access, multimedia study objects, collaboration, selfconsciousness, 

audience). 

Thus digital humanities needs to move from producing primarily the scholarly archive 

(first generation of electronic literary projects) to also producing digital monographs or 

multiply (and to varying degrees) authored works – polygraphs. There is no reason that 

tightly focused critical inquiry cannot be produced by a group. Revealing and practical 

questions about reproductive fidelity – such as "what degree of imaging accuracy is 

needed for critical inquiry?" and "who should be responsible for maintaining and making 

available the highest quality copies?" – are crucial for such editions and have expanded 

group efforts. Initiatives such as the LEADERS project, which "aims to enhance remote 

user access to archives by providing the means to present archival source materials within 

their context", ensure that archivists best trained to maintain both the actual and the 

surrogate archival copies assume the responsibilities for doing so and work with scholarly 

editors best trained to organize the data critically. That they can work remotely, as do 

multiple guest co-editors discussed earlier in this chapter, can be imagined, and perhaps 

achieved, only in the digital realm of information organization and exchange. Similarly, 

to develop digital monographs the most basic question – "what lines of critical argument 

are possible only in a digital monograph?" – needs to be posed repeatedly. Certainly, 

there are adept ways of using sound and image, as well as linguistic codes, that are only 

possible in the digital realm, but that is only the beginning. Moving from editor (author 

surrogate) and author to reader (including editor and author), from enacting definitude 

(editing for the static printed page) to enacting fluidity (the dynamic screen), is enabling 

profound innovations in editorial praxes, changes demonstrating how vital are "recent 

moves to reframe objectivity from the epistemic stance necessary to achieve a definitive 

body of knowledge, to a contingent accomplishment of dynamic processes of knowing 

and acting" for enriching our intellectual commons (Suchman, website). Acknowledging 

the fluidity of texts instead of insisting upon single-minded, singularly-oriented texts, 

"learning the meaning of the revision of texts", as well as the revision of our editorial 

practices, creates an environment in which a "new kind of critical thinking based on 

difference, variation, approximation, intention, power, and change" can flourish and work 

for the common good. If we are to shed the inertia bequeathed from the bibliographic 

world, editorial integrity and fidelity created within and upon the "shifting sands of 

democratic life" demand a "new cosmopolitanism" in scholarly editing (Bryant 2002: 

177), adopting the "lesbian rule" of principled accommodation for digital humanities. 


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(ed.), A New Computer-Assisted Literary Criticism? (pp. 295–306). 

23. 

Textual Analysis 

John Burrows 

Preamble 

The object of this paper is to show that computer-assisted textual analysis can be of value 

in many different sorts of literary inquiry, helping to resolve some questions, to carry 

others forward, and to open entirely new ones. The emphasis will not be on the 

straightforward, albeit valuable, business of gathering specimens of chosen phenomena 

for close study – the business of concordances and tagged sets. It will fall rather on the 

form of computational stylistics in which all the most common words (whatever they 

may be) of a large set of texts are subjected to appropriate kinds of statistical analysis. 

Statistical analysis is necessary for the management of words that occur too frequently to 

be studied one by one. But why study such words at all? For those of a sufficiently 

ascetic taste, there is a certain intrinsic interest in considering why Henry Fielding should 

stand out among a host of others in his recourse to the plain man's very and the lawyer's

inferential use of the conjunction for. And if the substance of Aphra Behn's love poetry is 

epitomized in her fondness for warm and soft, her slightly strident tone of voice can be 

heard in all and none and never. On a larger scene, my datasets uphold the belief that 

British writers still make more use of which and should than their American or Australian 

contemporaries. Australian writers, however, show a preference for we/our/us in contexts 

where their British and American counterparts would favor limy/me. A desire to avoid 

any appearance of immodesty or a desire for concealment within the herd? 

But the real value of studying the common words rests on the fact that they constitute the 

underlying fabric of a text, a barely visible web that gives shape to whatever is being 

said. Despite a brave attempt made long ago (Bratley and Ross 1981), we have yet to find 

satisfactory ways of tracing the interconnections of all the different threads, as each of the 

common words occurs and recurs in the full sequence of a text. We are able, nevertheless, 

to show that texts where certain of these threads are unusually prominent differ greatly 

from texts where other threads are given more use. An appropriate analogy, perhaps, is 

with the contrast between handwoven rugs where the russet tones predominate and those 

where they give way to the greens and blues. The principal point of interest is neither a 

single stitch, a single thread, nor even a single color but the overall effect. Such effects 

are best seen, moreover, when different pieces are put side by side. That, at all events, is 

the case I shall present. 

Classification of Texts 

Analyses 

To begin directly with the first of several case studies, let us bring a number of poems 

together, employ a statistical procedure that allows each of them to show its affinity for 

such others as it most resembles, and draw whatever inferences the results admit. For this 

purpose, I have taken forty specimens of seventeenth-century and early eighteenthcentury 

English verse, as listed in Table 23.1. Many are excerpts from longer works but 

each of them exceeds two thousand words in length. While they range in date from 1633 

to about 1733, most come from 1660–1700. While they run across the range of literary 

forms taken by long poems in that era, satire and epic figure prominently among them. 

The first twenty on the list are by poets whose other work contributes to the large, wideranging 

database listed in the Appendix. Seventeen of the second twenty are by poets 

whose work is not included there. Although the remaining three poems (nos. 22–24) are 

of doubtful authorship, they are usually attributed to Andrew Marvell, a member of the 

database. If these three are counted, notionally, as Marvell's, twenty of the forty poems 

form small authorial sets. The other twenty are independent pieces. 

The actual data for analysis consist of word-counts, from each of the forty specimens, of 

150 common words. The words themselves are the 150 most common in the 

aforementioned database of seventeenth-century verse. Such a list is more robust than 

one derived from a small, selective set of texts like the one we shall examine. The texts 

were prepared according to the same protocols as the main database. They were all 

modernized so as to remove the statistically misleading effects of seventeenth-century

spelling. Contracted forms like don't and I've were expanded to allow full counts of the 

parent words. A few common words like so and that were tagged in such a way as to 

distinguish their main grammatical functions. With this preparatory work complete, the 

texts were subjected to our sorting and counting programs. The raw counts for each 

common word in each text were normalized as percentages of the total number of words 

in that text. The object is to avoid the tyranny of the larger numbers with texts of uneven 

length. 

The chief analytical procedures employed for comparing texts on the basis of the relative 

frequencies of many common words all depend upon the logical principle of concomitant 

variation. The counts for each word are treated as variables on which each of the chosen 

specimens (the texts under examination) lies nearest to such others as it most resembles 

and furthest from those it least resembles. To take examples that will easily be 

appreciated, texts (like plays) where / and you occur unusually often are likely to differ in 

many ways from texts (like prose treatises) where the and a rise well above the norm. 

Texts of the former sort usually run high in the simpler auxiliary verbs and in many 

colloquial, speech-oriented forms of expression. Texts of the latter sort tend to be 

oriented towards the relationships among things and ideas rather than persons and often 

show high frequencies in the most common prepositions. Differences in level of 

discourse (as in the wide-ranging contrasts between Latinate forms like ascend and 

Germanic equivalents like go up); in genre (as between the past-tense verbs of 

retrospective narrative and the present tense of most disquisitory writing); and in 

historical provenance (as in the shift from thou to you) – all these and many more are 

reflected in systematic relationships among the frequencies of the very common words. 

23.1 Table Forty long poems of the late seventeenth century 

*1 2586 Butler, Samuel Hudibras, Canto III 

#2 2208 

Upon the Imperfection … of Human Learning, Parts 

I & II. 1–72 

*3 3373 Cotton, Charles A Voyage to Ireland in Burlesque, Cantos 2 and 3 

#4 3004 Cowley, Abraham The Plagues of Egypt, 1–262 

#5 6812 Davideis, Book II 

*6 7824 Dryden, John Absalom and Achitophel 

*7 19896 The Hind and the Panther 

*8 7817 D'Urfey, Thomas The Malecontent 

#9 6020 Gould, Robert To the Society of the Beaux Esprits 

#10 4019 The Play-House. A Satyr, Part II 

#11 4057 A Satyr against Man, Part I 

#12 4492 Presbytery Rough-drawn. A Satyr 

*13 27154 Milton, John Paradise Lost, lines 201–500 of each Book

*14 15694 Paradise Regained 

*15 12885 Samson Agonistes 

*16 2210 Oldham, John Satyr II 

*17 3378 The Eighth Satyr of Monsieur Boileau Imitated 

*18 3381 Swift, Jonathan On Poetry: A Rhapsody 

*19 3206 Verses in the Death of Dr. Swift, D. S. P. D. 

*20 2606 Waller, Edmund Instructions to a Painter 

#21 3547 Addison, Joseph The Campaign 

*22 2867 Anon. The Second Advice to a Painter 

*23 3638 The Third Advice to a Painter 

*24 7693 Last Instructions to a Painter 

*25 11111 Billingsley, Nicholas The World's Infancy 

#26 6986 Blackmore, Richard King Arthur, Book I 

*27 3892 Caryll, John Naboth's Vineyard 

#28 3617 Chamberlayne, Willi Pharonnida, Book I, Canto 1 

#29 2743 Chudleigh, Mary 

On the Death of his Highness the Duke of 

Gloucester 

#30 5167 Davenant, William Gondibert, Book I, Cantos 1 and 2 

#31 3892 Duke, Richard Paris to Helena 

#32 5935 Fletcher, Phineas The Purple Island, Cantos I and II 

#33 8797 Heyrick, Thomas The New Atlantis, Parts I and II 

#34 3731 Parnell, Thomas Homer's Battle of the Frogs and Mice, Books I-III 

*35 3103 Pordage, Samuel The Medal Revers'd 

*36 3250 Thompson, Thomas Midsummer Moon 

*37 2287 Tutchin, John A Search after Honesty 

*38 4374 Vaughan, Henry Juvenal's Tenth Satire Translated 

*39 2156 Wase, Christopher Divination 

*40 3321 Wild, Robert Iter Boreale 

Texts are complege except where specified. Texts marked* are from original or standard 

modern editions. Those marked # are from the Chadwyck-Healey Archive of English 

Poetry, to which my university subscribes. The texts were used only for the extraction of 

word-counts. 

When any large group of texts is analyzed, such manifestations of concomitant variation 

as these yield complex but intelligible patterns of affinity and disaffinity. They are much 

enriched, moreover, by the many exceptions that arise. Some of these are associated with 

authorial idiosyncrasies. The possible combinations from genre to genre, era to era, 

author to author, text to text, are almost unlimited. Effects like these, we may suppose, 

impinge upon the minds of good readers as part of their overall response. But they are not

easy for a reader to articulate. Statistical analysis, on the other hand, offers clear but 

comparatively unsubtle ways of representing them. 

The main analytical procedure to be employed in this chapter is that of cluster analysis, 

chosen because it offers rather a harsh test of the questions to be considered and also 

because the "family trees" in which the results are displayed speak plainly for themselves. 

The procedure is put to excellent use in a recent study of prose fiction. (See Hoover 

2002.) The principal disadvantage of cluster analysis is that the underlying word-patterns 

are not made visible and must therefore be approached in other ways. For those who are 

versed in these matters, I should add that the statistical package used for these cluster 

analyses is MINITAB. My many trials suggest that, for such data as we are examining, 

complete linkages, squared Euclidean distances, and standardized variables yield the 

most accurate results. This pattern of preferences avoids any undue smoothing of data 

whose inherent roughness reflects the complexities of the language itself. This pattern, at 

all events, is used throughout. 

Figure 23.1 represents the outcome of a cluster analysis in which our forty texts are 

compared with each other on the basis of the full list of the 150 most common words. It 

should be studied from the base upwards, taking account of the way the clusters form. 

The texts are identified, along the horizontal base of the figure, by the numbers attached 

to them in Table 23.1. The true affinities are not between entries that merely stand beside 

each other before separating, like nos. 2 and 4, but between those that form unions, like 

nos. 4 and 5. The closest affinities of all are between those pairs that unite soonest, like 

nos. 13 and 14, and those trios that do so, like nos. 13, 14, and 15. 

The most obvious feature of Figure 23.1 is that some of its members are so slow to form 

any union at all. The isolation of no. 35, Samuel Pordage's satire The Medal Revers'd, can 

readily be shown to rest on an unusual preponderance of verbs couched in the present 

tense. They reflect the rhetorical stance of an ostensibly philosophic observer of affairs. 

No. 3, Charles Cotton's A Voyage to Ireland in Burlesque, is isolated by its narrative 

mode and its colloquial speech-register. When it does finally form an affinity, it is not 

with the epic and heroic poems to its left but with the loose group, chiefly made up of 

satires, lying to its right. And even though nos. 1 and 2, the highly idiosyncratic satires of 

Samuel Butler, differ markedly from each other, they stand even further apart from all the 

rest. 

Considered more broadly, Figure 23.1 shows a rough differentiation between the earlierand 

later-born poets, with the work of the latter lying towards the right. Near the righthand 

extremity, for example, the poems of Gould, Swift, Duke, and Lady Mary 

Chudleigh make comfortable enough chronological neighbors while Butler, Cowley, 

Billingsley, and Waller are well placed at the left. But this incipient pattern is severely 

damaged by the genre-differentiation already noted. On a. chronological basis, 

Blackmore (no. 26), Addison (no. 21) and Parnell (no. 34) have no place on the left while 

Vaughan (no. 38) and Phineas Fletcher (no. 32) lie much too far to the right. The location 

of Milton's three poems (nos. 13–15) is anomalous in both chronology and genre.

Of the seventeen poems that make up authorial sets, only four fail to reveal their true 

authorial affinities. The three that are thought to be Marvell's form another little set. (In 

the case of Robert Gould, whose quartet forms two pairs, the success of the cluster 

analysis is incomplete.) Since the members of each authorial pair are locating their proper 

partners from a field of thirty-nine, the odds against the chance achievement of such a 

success rate are immense. Moreover, as readers who know these forty poems will be 

aware, the three anonymous painter-satires (nos. 22–24) are the only set where the effects 

of genre and authorship might be thought to work entirely in unison. The two Dryden 

poems, for instance, have little in common but their authorship; and Samson Agonistes 

differs markedly in genre from Milton's epics. 

Figure 23.2 is specifically designed to show why the painter-satires form a trio. It is 

constructed in exactly the same fashion as Figure 23.1 save that two extra specimens 

have been added. They are two of Andrew Marvell's best-known poems, Upon Appleton 

House (no. 41) and The First Anniversary of the Government under 0. C. (no. 42). At 

4,845 and 3,131 words respectively, they are of appropriate length for the comparison. 

And they differ markedly in genre from each other and from the painter-satires. In Figure 

23.2, it will be seen that they form an additional authorial pair and that this pair unites 

with the trio of painter-satires. As in Figure 23.1, furthermore, the target of the paintersatires, 

Edmund Waller's Instructions to a Painter stands well away from them. Marvell's 

likely authorship of these three painter-satires is a matter of some importance in literary 

history. It is pursued more thoroughly in a new article to be submitted to The Yearbook of 

English Studies. For our present purpose, however, Figure 23.2 makes a more general 

point. With the addition of the two Marvell pieces and the uniting of the Gould quartet, 

Figure 23.2 surpasses the strong result achieved in Figure 23.1. The 150 most common 

words of our main dataset can clearly be said, therefore, to offer a useful basis for testing 

the authorship of long poems of the late seventeenth century. The possibility that this 

outcome might be a statistical artifact is easily set at rest: other methods of analyzing 

these data yield equally accurate results. 

If the word list is progressively truncated from the bottom, much shorter poems can be 

tested for authorship. Although the rate of success diminishes as the texts grow shorter, it 

still far exceeds the rate of chance success. A study of this topic (Burrows 2002a) has 

recently been published. But a selective word list can be constructed in other ways. Is it 

possible to achieve similar levels of success more economically, working with only a 

chosen subset of our 150 most common words? After making many attempts, varying in 

rigor and subtlety but without conspicuous success, as reported elsewhere (Burrows 

2003: 21–3), I chose a plainer path. In Table 23.2, the 150 most common words are 

dispersed in three subsets. The 54 words of "domain 1" comprise the principal referential 

markers, namely the definite and indefinite articles and the personal pronouns, to which 

are added the infinitive particle to and those common verb-forms whose inflections differ 

in association with the articles and pronouns. The 69 words of "domain 2" comprise the 

common connectives (including conjunctions, prepositions, and relative pronouns) and 

the common intensifiers, whether adjectives or adverbs. The descriptive adjectives, 

however, are put aside with the nouns among the 27 words excluded as too lexical, too 

subject-oriented, to be valid markers of more formal differences. A close student of Table

23.2 will detect at least two cases, art and own, where I have sought the less bad 

compromise. The former is included in "domain 1" on the ground that, in seventeenthcentury 

verse, its role as an auxiliary verb is more potent than its role as an abstract noun. 

The latter is excluded on the ground that it is used less often as a verb than as an 

adjective. Such is the wealth of our data that a few arguable choices have little impact on 

the analyses. 

23.2 Table Experimental classification of the 150 most common words of the 

main dataset 

"Domain 1":54 words "Domain 2":69 words Exclusions: 27 words 

1 the 79 do 2 and 71 there 64 love 

4 a 80 should 3 of 72 some 70 great 

5 to (i) 82 let 6 in (p) 73 too 92 own 

7 his 83 make 8 with 74 how 100 first 

10 is 84 could 9 to (p) 76 one 101 men 

12 he 86 must 11 but 77 never 103 man 

14 I 87 an 13 all 81 though 106 heaven 

15 it 90 us 16 as 85 those 108 good 

17 their 93 thee 19 not 88 where 111 well 

18 her 94 made 23 for (p) 89 still 114 long 

20 be 95 has 24 by (p) 91 here 115 old 

21 you 96 see 27 from 97 these 116 day 

22 they 122 art 28 that (rp) 98 before 121 heart 

25 my 124 give 29 or 99 thus 123 wit 

26 we 128 know 33 this 102 every 126 world 

30 our 129 find 34 when 104 whose 130 fate 

31 thy 131 its 37 at 105 out 132 eyes 

32 was 137 been 38 which (rp) 107 much 133 life 

35 are 146 come 39 no (aj) 109 while 134 vain 

36 your 40 what 110 so (c) 135 power 

43 will (v) 41 so (ad) 112 each 139 God 

45 can 42 that (d) 113 only 140 soul 

46 have 44 on (p) 117 once 141 new 

47 she 49 more 118 through 142 fair 

48 thou 50 if 119 up (ap) 147 time 

51 did 53 now 120 both 148 name 

52 would 54 who (rp) 125 till 150 last

"Domain 1":54 words "Domain 2":69 words Exclusions: 27 words 

57 had 55 that (c) 127 ever 

60 him 56 yet 136 down 

66 may 58 then 138 whom 

67 shall 59 such 143 upon (p) 

68 me 61 nor 144 whilst 

69 were 62 for (c) 145 since 

75 does 63 like (p) 149 over 

78 them 65 than 

Abbreviations: 

(p) = preposition 

(i) = infinitive 

(c) = conjunction 

(rp) = relative pronoun 

(v) = verb 

(ap) = adverbial particle 

(ad) = adverb of degree 

(aj) = adjective 

(d) = demonstrative 

Figure 23.3 is a cluster analysis of our original set of forty poems using only the 69 words 

of "domain 2." As a test of authorship, it offers much the same result as the full list of 

150 words used earlier. The members of the Oldham and Swift pairs still stand apart from 

their fellows. The Gould poems now fall into a trio and a singleton instead of the two 

pairs of Figure 23.1 or the quartet of Figure 23.2. The other authorial sets hold firm. On 

closer study, however, it can be seen that Figure 23.3 surpasses both its predecessors 

because the various groups form much more quickly. For testing the authorship of this 

large group of poems, "domain 2", therefore, is more economical of data and more 

efficient in operation than the full list of 150 words. 

It goes almost without saying that, if these 69 words can match or outmatch the full set, 

the omitted words must shed little light on authorship. Figure 20.4 is the counterpart of 

Figure 23.3, taking the 54 words of "domain 1" as its basis. Two of the three Milton 

poems, two of the three painter-satires, and two of the four Gould poems still unite with 

their respective fellows. But where we had four or five errors out of twenty, we now have 

fourteen. Since this pattern is certainly not author-driven, the question is whether other 

inferences can be drawn. A close study of the way the poems cluster offers rough but 

suggestive distinctions between predominantly monologic, dialogic, narrative, and 

reflective forms of rhetoric. Since we are working exclusively with referential markers 

and inflected verbs, this seems a plausible result. But the genre-differences in the poetry 

of the Restoration era are too subtle and too impure to allow any firm conclusions to be 

drawn from this one analysis.

To pursue the hypothesis that "domain 1" may indeed be genre-driven, I have accordingly 

added a further 26 specimens, as listed in Table 23.3 – Some 16 of them (nos. 41–56) are 

selections from plays of the period. Most of the plays are represented by a single Act but 

there are cases where other considerations call for a choice of scattered scenes. (Both 

Rochester and Waller, for example, contributed scenes to plays by other dramatists.) 

Among the plays, the first ten selections are of verse drama. The next four are of prose 

drama and the last two, both from Southerne's The Fatal Marriage, include both verse 

and prose. Of the dramatists included, Dryden, Rochester, Stapylton, Congreve, and 

Southerne offer authorial pairs within their respective subsets. Sedley and Shadwell make 

it possible to test dramatic authorship across the differences between verse and prose. 

While these 16 specimens of drama are too few to be representative, they may suffice for 

an instructive comparison with the poetry and the personal letters of their day. 

23.3 Table Twenty-six additional texts 

Sixteen excerpts from plays of the late seventeenth century 

Verse plays 

*41 2918 ?Dorset, Charles Sackville, Earl of Pompey the Great, Act II 

*42 7133 Dryden, John The Siege of Granada, Act I.i, Act V.i-ii 

*43 4955 Tyrannic Love, Acts I-II 

*44 7870 Rochester, John Wilmot, Earl of Valentinian, replacement scenes 

*45 2106 The Walls of China, a contributed scene 

*46 3177 Sedley, Sir Charles Antony and Cleopatra, Act I.ii, Act III.i 

*47 2304 Shadwell, Thomas Psyche, two scenes 

#48 7817 Stapylton, Sir Robert Hero and Leander, Act II 

#49 3764 The Slighted Maid, Act III 

*50 4980 Waller, Edmund The Maid's Tragedy, replacement scenes 

Prose plays 

#51 5612 Congreve, William Love for Love, Act IV 

#52 5334 The Way of the World, Act IV 

#53 5303 Sedley, Sir Charles Bellamira, Act III 

*54 6144 Shadwell, Thomas The Libertine, Act III 

Prose and verse 

*55 3732 Southerne, Thomas The Fatal Marriage, Act III 

#56 4082 The Fatal Marriage, Act IV 

Ten sets of letters from the late seventeenth century 

*57 9615 Conway, Anne Finch, Lady Letters to her husband 

*58 

5058 King, William, Archbishop of 

Dublin 

Letters to Jonathan Swift

*59 5545 Montagu, Lady Mary Wortley 

Letters to Edward Montagu (before 

marriage) 

*60 5969 More, Henry Letters to Anne, Lady Conway 

*61 8113 Osborne, Dorothy 

Letters to William Temple (before 

marriage) 

*62 7193 Rochester, John Wilmot, Earl of Letters 

*63 6251 Savile, Henry Letters to Rochester 

*64 8948 Swift, Jonathan 

Letters to Esther Vanhomrigh 

("Vanessa") 

*65 8730 Swift, Jonathan Letters to Archbishop King 

*66 5418 Vanhomrigh, Esther ("Vanessa") Letters to Jonathan Swift 

Texts are complete except where specified. Texts marked* are from original or standard 



word-counts. 

The other ten specimens (nos. 57–66) are selections from the personal letters of some 

well-known figures of the period. All the letters within each set except for Rochester's are 

directed to a particular recipient. Two sets of Swift's letters are included to display the 

possible diversity of an epistolary repertoire. 

Whereas only tentative conclusions could be drawn from Figure 23.4, Figure 23.5 uses 

the 54 words of "domain 1" to quite striking effect. Some 90 percent of our 66 specimens 

form genre-based clusters. All ten sets of personal letters form a cluster of their own, at 

the extreme right. Thirty-eight of the forty poems lie to the left of any other specimens. 

Twenty-nine of them form one large cluster at the left with another seven clustering to 

their immediate right. The rest are located among the verse-plays and are marked as 

errors. Two verse plays are marked as errors because they cross into the remaining 

cluster, where they join the prose plays and the two plays in which prose and verse are 

mixed. 

From another, simpler perspective, Figure 23.5 is composed of three large clusters. To 

the left is a set made up entirely of poems. In the middle is a set uniting some poems of a 

dialogic cast with verse drama, obviously their close kin. To the right is a set of sixteen 

prose specimens and two stray pieces of verse drama (nos. 48 and 49). These last, both 

extracts from little-known plays by Sir Robert Stapylton, are couched in what has aptly 

been described (Sutherland 1969: 43) as "an easy and colloquial blank verse." Why do 

they cross the border between verse and prose? The best explanation, I believe, is that 

Stapylton favors a dialogue of brief, deictic interchange and only rarely offers a large, 

poetic set-speech. In an analysis based principally upon pronouns and auxiliary verbs, 

this tendency carries his plays towards the prose drama of the period.

That explanation is supported by Figure 23.6, where the full list of 150 words is 

employed in an analysis of the same 66 specimens. The richer word list, less strongly 

influenced by I/thou, places Stapylton correctly in the cluster composed entirely of verse 

drama. No. 45, Rochester's highly poetic contribution to Sir Robert Howard's verse drama 

The Walls of China, is now misplaced among the 37 poems that make up the main 

cluster. Three poems (nos. 31, 35, and 3), which have previously shown little affinity for 

the others, are now misplaced among the plays. Every other entry is correctly placed in a 

tripartite distribution of the texts according to their genre. 

The foregoing series of analyses shows many points of interest. One of the more 

unexpected offers special food for thought. When we compared Figure 23.1 and Figure 

23.3, we saw that the selective word list, though scarcely less accurate than the full list, 

was more economical and more efficient. In the corresponding case, Figure 23.5 does 

offer a somewhat less accurate separation of literary genres than Figure 23.6. Yet Figure 

23.5 is much more economical than Figure 23.6 in its use of data and its clusters form 

more speedily. If the 69 words of "domain 2" and the 54 words of "domain 1" compare so 

favorably with the full list of 150 words as indicators of authorship and genre 

respectively, we do well to seek an explanation. 

Rationale 

As a first step, the case for excluding the most common lexical words from such analyses 

because they are too subject-oriented is well understood and widely practiced. Even in 

the upper reaches of the frequency hierarchy, the lexical words favored by different 

authors are sometimes idiosyncratic, sometimes those of a coterie. The Christian 

vocabulary of seventeenth-century devotional verse stands out in contrast, for example, to 

the obscene vocabulary that marks the clandestine verse of Rochester and his circle: some 

words of both kinds rank very high in appropriate texts. We do not need a computer to 

determine that the obscene farce, Sodom, is unlikely to be the work of Henry Vaughan. 

And even if it were, a quite uncharacteristic set of lexical words would act only as a 

disguise. Since such problems cannot validly be addressed by picking and choosing 

among the lexical words, it is best to treat them all alike and to recognize what one is 

doing. And, though the boundary between lexical and function words is inexact, the 

upper end of the frequency range includes very few hard choices. On this basis, 27 words 

can be excluded from our list of 150. 

A good case can also be made for excluding the personal pronouns because they are too 

volatile in frequency to serve as reliable measures. If that is done, the question of the 

inflected verbs arises with some force. It would obviously be logical to exclude them too. 

But by now the increasing sacrifice of information must occasion serious concern. These 

three excisions would take out 81 words from the 150 of the list we have been using. A 

desirable rigor would be obtained at a disturbing cost. 

The results already offered illustrate what follows when the 27 most common lexical 

words are set aside while the common function-words are all retained but are separated 

into two "domains." Figure 23.7 represents an attempt to understand the difference

etween these notional domains. Anybody who sets about a piece of writing may be 

thought of as beginning at the standpoint marked at the extreme left of the figure. Even at 

that point, such a person is too much a creature of habit and background to begin with a 

clean slate. Even at that point, such a person will also have some notion of a subject, an 

intended audience, and an attitude to that audience. And, although the writer retains a 

good deal of latitude as the work proceeds, these broad constraints begin to strengthen 

from the moment the very first words are written: "Of Man's first disobedience … "; "The 

glories of our blood and state… "; "It is a truth universally acknowledged… "; "She 

waited, Kate Croy… "; "My father had a small estate in Nottinghamshire; I was the third 

of five sons… "; "When I consider how my light is spent… "; "I wandered lonely as a 

cloud… "; "Shall I compare thee to a summer's day… "; "Believe't, I will.//Thy worst. I 

fart at thee." 

The authorial I of a text is ever-present though not always visible. That I, of course, 

cannot simply be identified with the writer in propria persona. But four of the many roles 

open to that I govern the categories set out in the upper part of Figure 23.7. In category 

A, an I not easily distinguished from the author engages in some form of direct address to 

a real or putative reader. Such work need not be disingenuous. Samuel Johnson offers a 

celebrated warning about the deceits of epistolary correspondence and when Lady Mary 

Pierrepont tells her future husband, Edward Wortley Montagu, "I am going to write you a 

plain long letter" (Halsband 1970: 41), only the warning signs are plain. (He did not heed 

them.) The I of category A usually occurs freely and carries a select group of verbal 

inflections along with it. The thou/you who is addressed may occur just as freely but may 

also remain inexplicit. T. S. Eliot distinguishes the pulpit-rhetoric of Donne from that of 

Lancelot Andrewes on pertinent lines: whereas the former is always in his pulpit, 

addressing his congregation, the latter "is wholly in his subject, unaware of anything 

else" (Eliot 1932: 341). While the comparative frequencies of the relevant pronouns, 

articles, and verbs in their sermons are not what engages a reader, they show the force of 

Eliot's distinction. 

In category B, both the authorial I and the you of the addressees are absorbed into the we 

of a real or notional group. They is assigned to outsiders, whether feared, pitied, or 

admired. When these plural forms predominate, the singular pronouns and verbs all drop 

away in frequency. Winston Churchill's "We will fight them on the beaches … We will 

never surrender" takes the hortatory form. The elegiac form is taken by Lawrence 

Binyon's Armistice hymn, For the Fallen: "They shall grow not old / As we that are left 

grow old." 

In category C, the authorial I is displaced by the deictic I of whichever character, 

dramatic or fictional, is speaking at a given time. In plays, accordingly, Thou/you is 

reserved for the characters then addressed. All of the personal pronouns are free to occur 

but I/thou/you usually predominate. The present tense verb-forms are usually in the 

ascendant. In the retrospective narrative "histories" that either constitute or are 

interpolated in many novels, especially those of the eighteenth and nineteenth centuries, I 

is customarily the highly visible property of the present speaker. (Whereas Lemuel 

Gulliver always holds the center of his stage, Henry Fielding's Man of the Hill and Mrs

Fitzpatrick or his Miss Mathews, Captain Booth, and Mrs Bennet take turns at being /.) 

Thou/you may either be reserved for fellow characters or extended to include us "Gentle 

Readers." Both he and she are likely to occur almost as freely as /, being devoted to those 

others who took part in the events now recounted. Except in those retrospective narratives 

where the historic present tense is adopted as a rhetorical strategy, most verbs are 

couched in the past tense. 

In the many and varied forms of writing composing category D, neither I nor you need 

ever occur, most other pronouns may be rare, and the articles are likely to be the chief 

referential markers. Among the third-person pronouns, it and they are likely to 

predominate. And though historical writings are often more personally oriented than most 

treatises and disquisitions, they do not often find much use for the feminine pronouns. It 

might be interesting to ascertain whether a flurry of feminine pronouns could be found in 

any part of Gibbon's great history except the chapter treating the notable exploits of the 

Empress Theodora. She is certainly one of the only two women named in the extensive 

titles of his 71 chapters. Modern feminist historians are busy redressing the imbalances of 

the past, and altering the incidence of pronouns as they do so. 

With Henry Fielding's novels among my examples, Gentle Reader, I could not easily 

forget that many of the literary forms I have referred to, and the novel in particular, can 

draw on the resources of more than one of my categories. When this occurs, of course, 

the pattern of pronouns, articles, and auxiliary verbs is bound to be modified. My short 

list of distinguishable literary forms is only a scanty representation of the more obvious 

classes. And I am well aware that, in broadening its field of reference in recent years, the 

word genre has lost its former rigor. Its over-extension can be seen when it is applied, for 

example, to personal letters, a whole family of loosely related literary forms. 

With all due allowance for these reservations, Figure 23.5 still indicates that some major 

genre-divisions can indeed be differentiated by the statistical analysis of a select word list 

confined to pronouns, inflected verbs, articles, and the infinitive particle. This last acts as 

a marker of complex verbal compounds, often couched in the first person, in which the 

speaker stands back a little from the action encompassed in the main verb and expresses a 

self-conscious attitude to it. "I hoped/feared/wished/chose to…" and many other forms 

like them typify a usage which is remarkably common in some writers, Butler and 

Rochester among them, and which offers us an easy transition from domain 1 to domain 

2. 

From the standpoint at the left of Figure 23.7, as a writer moves forward with the 

emerging text, the choices made in domain 1 interweave with those of domain 2. Here, 

however, the more open choices of domain 1 are outweighed though not extinguished by 

the constraints of upbringing and habit. Predisposition or idiosyncrasy? The syntactical 

forms by which a writer's ideas are connected and the forms of emphasis a writer chooses 

all offer a new battleground for ancient debates about the nexus between idiosyncratic 

and socially influenced facets of our behavior. If social factors were all that mattered, it 

would be hard to explain why men of such different backgrounds as Defoe and Henry 

Fielding should far outrank their contemporaries in the use of so commonplace a word as

very. If idiosyncrasy were all that mattered, it would be hard to explain why, nearly three 

centuries later, that same word should act as a statistically significant differentia between 

a large set of Australian writers and their British and American fellows. Contrasts in 

socially imposed roles may well explain why not, but, and never are statistically 

significant differentiae between large sets of female and male writers of the eighteenth 

and nineteenth, but not the twentieth centuries. They do not explain why Dryden and 

Gould should stand out among their contemporaries in their use of before and there 

respectively. 

Whether they originate in idiosyncrasy or in upbringing, such authorial habits are strong 

enough and consistent enough to allow the 69 words of domain 2 to act as effective 

authorial discriminators among the forty poems of Figure 23.3 – Although cluster 

analysis rests upon combinations of many word-frequencies, it is possible to pick out 

some of the more striking. Rochester is much given to who and whose and ever, Marvell 

to yet and where and only. Dryden and Congreve make uncommonly frequent use of thus. 

Of while and whilst, Marvell and Dryden lean to the former whereas Aphra Behn prefers 

the latter. Whereas both Dryden and Milton prefer on to upon, Samuel Butler goes the 

other way. As these last examples suggest, the words to which particular writers have 

more recourse than most are matched, as discriminators, by words that they eschew. 

Provided the texts called into comparison are not too dissimilar in kind, such preferences 

among the common connectives and modifiers that constitute our domain 2 (or the 

corresponding "domain 2" of any other large database) can go far towards establishing 

the likely authorship of doubtful texts. And the notion of a writer finding a path forward 

by intermingling choices and habits helps to explain why this should be so. As the writer 

advances, the emerging shape of the text makes the basis of the stylistic corollaries and 

consequences indicated at the extreme right of Figure 23.7. Choices, constraints, further 

constraints following from each choice – and always some room for accident, whether 

inept or inspired. 

But Figure 23.8 is an abrupt reminder that a writer's habits are not set in stone. It 

represents the outcome of a cluster analysis of the 66 texts of Table 23.1 and Table 23.3, 

using the 69 words of domain 2. Some 38 of the 66 texts are eligible as members of little 

authorial subsets: only 17 of them find their partners. Those 17 almost exactly match the 

successful unions of Figure 23.3 and include, in addition, the two specimens of Dryden's 

verse drama (nos. 42 and 43). Samson Agonistes (no. 15) has moved away from the other 

Milton entries, but the previously misplaced Gould entry (no. 9) has rejoined the other 

three. Dryden's verse drama apart, the eligible specimens from among the additional texts 

not only fail to achieve a single cross-genre union but even fail to find their partners 

within the same genre. One interesting failure lies in Swift's two sets of letters (nos. 64– 

5), whose strikingly different recipients may explain why he can adopt such different 

styles. The two Acts from the same tragedy by Thomas Southerne (nos. 55–6) are 

distinguished from each other by their different proportions of subplot and main plot, 

prose and verse. The two specimens of Congreve's prose drama (nos. 51–2) are 

distinguished from each other by this dramatist's keen ear for different stylistic registers. 

The aristocrats of The Way of the World (no. 52) converse in a manner more akin to that

of their peers in Sedley's Bellamira (no. 53) than that of the (not ill-spoken) cits and 

squires in Love for Love (no. 51). 

But the strongest general contrast in Figure 23.8 is between prose and verse. Three 

specimens of verse drama by Rochester (no. 44), Sedley (no. 46), and – as it is thought – 

by Dorset (no. 41) lie among the large cluster of poems at the left. Another by Rochester 

(no. 45) lies among the poems at the right. In the middle of the whole group, ranging 

across from no. 52 to no. 65, the sixteen prose specimens form a slightly disordered but 

unbroken sequence. Even in domain 2, so it appears, strong differences of genre can 

smother authorial resemblance. 

There is matter here for a searching, full-scale inquiry. Even a cursory study of the wordcounts, 

however, is suggestive. The following examples are grounded on comparisons 

among the word-counts for four groups of texts: the 40 poems of Table 23.1; the 25 

authorial sets that make up our main database of seventeenth-century verse; a large 

database of first-person narratives by 55 writers of prose fiction born between 1660 and 

1860, the subject of the next main section of this chapter; and the ten sets of personal 

letters of Table 23.3. From an examination of these four large groups, I pick out the more 

striking contrasts between verse and prose and offer some small inferences. 

Among the connectives, verse scores higher than prose for or and nor, the twin staples of 

many an antithesis, and for the preposition like, the prelude to simile. Verse scores higher 

for four of the five main relative pronouns, the exception being which. The embedded 

clauses customarily introduced by which (at least in British writing) may be less 

congenial to the flow of verse rhythms than the corresponding appended clauses. A 

number of monosyllabic connectives and semi-connectives may serve the metrical needs 

of many poets while offering the strong rhetorical pointing of Restoration verse 

argument. They include what, yet, now, such, too, still, while, whilst, and thus. Verse also 

runs to high scores in some absolute forms like all and no. 

The prose set employed in these comparisons is too heterogeneous to yield examples of 

much interest. But it is certainly worth noting that some of our poets abandon some of 

their most pronounced verse habits when they turn to prose. Congreve and Swift stand 

out among the poets in their frequent recourse to thus and never respectively. In 

Congreve's prose drama and Swift's letters, their scores fall right away. Other poets, like 

Katherine Phillips ("Orinda"), maintain much the same stylistic repertoire through thick 

and thin. On/upon is a strong verse/prose discriminator, possibly because on has greater 

metrical versatility. Of the few poets much given to upon, Samuel Butler is celebrated not 

for a tin ear but for his mastery of offbeat rhymes and rhythms. In general, however, it 

seems that metrical necessities, rhetorical patterning, and a comparative dearth of 

circumstantial detail are among the factors that induce writers to assume rather different 

habits of connection and emphasis when they turn from verse to prose. 

While the separation of domain 1 and domain 2 is obviously worth maintaining and 

deserves further investigation, it is not a Berlin wall but only an analytical convenience. 

The vigorous traffic in both directions represents an intermingling of stylistic forces. As a

valuable sidelight in a study of doubtful authorship (Forsyth et al. 1999: 395–6), the 

writers make the point that an unduly ambitious analysis can collapse under its own 

weight. While something of that kind occurs in Figure 23.8, the outcome is not 

unintelligible. It is rather that some real authorial affinities are obscured when such 

extreme differences of genre supervene. 

Classification of Authorial Groups 

Genre and authorship are not the only powerful stylistic differentiae. A suitable choice of 

specimens allows other effects to be observed. Let us turn, for this purpose, to another 

large database. In its current state, it runs to 1.8 million words and consists entirely of 

first-person, retrospective narratives from works of prose fiction by 100 authors. The 

earliest born of the authors is Daniel Defoe (1660) and the latest is Louise Erdrich (1954). 

The later sets comprise American and Australian as well as British writings. Some 48 of 

the authors are female. The authorial sets range in length from 4,000 to almost 76,000 

words and each includes at least three narratives. The small sets are included, perforce, in 

order to represent some authors of particular interest like Louisa Atkinson, who is 

accepted as the earliest native-born Australian writer. With a threshold of 4,000 words, 

however, the differences of size among the sets show no apparent effects. (The e-texts 

were created by keyboard from standard editions and from the admirable facsimiles of 

early British fiction published by the Garland Press, New York. Permission was obtained 

where necessary and the texts were used only for the extraction of word-counts.) 

Earlier versions of this database have been used for a study of change over time (Burrows 

1992) and a pilot study of national differences in the English of literary narrative 

(Burrows 1996). The former takes its place beside Cluett (1976) and other, more 

linguistically oriented work like Biber and Finegan (1989). The latter, unfortunately, still 

stands alone. 

On the present occasion, developing work I presented at ACH/ALLC 1993 but have 

never published, the database will be used to examine possible differences between male 

and female writers. To this end, for reasons that will emerge, the database is divided 

between 55 authors born before 1860 and 45 born afterwards. The former subset includes 

26 female writers, the latter 22. As Table 23.4 and Table 23.5 indicate, the female writers 

are placed first in each subset. (Most of the information offered in those tables speaks for 

itself. It should be noted, however, that, where a writer is given two nationalities, his or 

her "less influential" country of abode is shown in parentheses.) 

Distribution tests like Student's /-test and the Mann-Whitney test are among the most 

appropriate procedures for assessing possible differences, over a range of many variables, 

between any two large sets of specimens. In both tests, the underlying assumption – the 

null hypothesis – is that the sets do not represent distinct populations. For each variable in 

turn, therefore, the results show the likelihood that the assumption is false. This 

likelihood, expressed as a degree of probability, takes the form of a decimal fraction like 

0.05 or 0.005. The smaller this fraction, the more likely it is that the assumption is false. 

When the tests are conducted with many variables, they must be expected to yield some

chance successes: around one result in twenty should be above 0.05; around one in a 

hundred should be above 0.01, and so on. With our 150 variables, therefore, we need 

many more than seven results from levels below 0.05, or many more than two from 

below 0.01 before we can begin to accept that there are systematic differences between 

our male and female writers, that they do indeed represent different populations. (We 

must also stiffen our stated requirement to allow for the fact that the "two-tailed" form of 

these tests, as used here, doubles the probability of a chance success.) To make the 

requirement still more rigorous, I have chosen, finally, to ignore results that do not satisfy 

both of the distribution tests at levels below 0.05. 

As Table 23.6 indicates, there is no need to quibble about borderline cases. In the upper 

part of the table, labeled (a), the results for our 55 earlier-born authors include no fewer 

than 32 out of 150 where the probabilities fall below 0.05. Almost all of them, in fact, fall 

far below that threshold. In the lower part of the table, labeled (b), the results for our 45 

later-born authors lie well within the realm of chance, with only six words yielding 

probabilities from below the 0.05 threshold. Taken together, the two sets of results 

indicate that, in our earlier but not our later set, the male and female authors represent 

different populations. Whereas men and women used to write very differently from each 

other, they have long since ceased to do so. 

In Table 23.6, the columns headed "/-score" and "DF" represent a different way of 

expressing the same probabilities from those shown in the column headed "/-prob." (The 

discriminatory power of /-scores is established by consulting an appropriate manual of 

statistical tables.) My reason for including the /-scores here is that they distinguish 

between positives and negatives. The positive scores are always those of the first set 

entered – in this case, the set of female authors. 

The contrasting scores for such referential markers as l/me/sbe/ber and, on the other 

hand, the/a point to a straightforward opposition between more and less personally 

oriented rhetorical stances among the two sets of earlier-born writers. The absence of any 

masculine pronouns makes this opposition a little more complex; and the opposed scores 

(albeit weak) for mother and man add to that effect. Female authors, it seems, had more 

to say of females: both male and female authors had much to say of males. For the 

substance of what they said, of course, one turns, with pleasure, to the texts themselves. 

23.4 Table Fifty-five authors born before 1860 (female authors listed first) 

1 2 3 4 5 6 7 8 

Atkinson, Austen, Braddon, Brontë;, Brontë, Brontë, Cambridge, Edgeworth, 

Louisa Jane Mary Anne Charlotte Emily Ada Maria 

1775– 

1834– 72 

1817 

A 

UK 

1837–1915 1820–49 

UK UK 

1816–55 

UK 

1818–48 UK 1844–1926 1767–1849 

UK(A) UK

4005 8889 8269 22640 15644 15743 12266 29330 

9 10 11 12 13 14 15 16 

"Eliot, Fielding, Freeman, Gaskell, Hamilton, Haywood, Hearne, Jewett, 

George" Sarah Mary W. Elizabeth Elizabeth Eliza Mary Sarah O. 

1819–80 1710–68 1852–1930 1810–65 1758– 1693?–1756 fl. 1718 1849–1909 

UK UK US UK 1816 UK UK UK US 

22686 75982 6084 51989 16922 28633 12815 5985 

17 18 19 20 21 22 23 24 

Lennox, Manley, 

Praed, Rosa 

Charlotte Mary 

Radcliffe, 

Ann 

Robinson, 

Scott, Sarah 

Mary 

Shelley, 

Mary 

Smith, 

Charlotte 

1720– 1663– 

1804 1724 

(US) UK UK 

1851–1935 1764–1823 1758– 

1797–1851 1749–1806 

1723–95 UK 

(A) UK UK 1800 UK UK UK 

29037 15727 9147 12066 10891 15316 35806 28695 

25 26 27 28 29 30 31 32 

Stowe, 

Spence, 

Harriet 

Catherine 

B. 

Bage, 

Robert 

Becke, 

Louis 

Bierce, "Boldrewood, Brockden 

Ambrose Rolf" Brown, C. 

Clarke, 

Marcus 

1825– 

1910 

(UK) A 

1811–96 1728–1801 1855–1913 1842– 1826–1915 

US UK A 1914? US UK(A) 

1771–1810 1846–81 

US UK(A) 

11538 10965 13969 9641 7745 17722 15923 13342 

33 34 35 36 37 38 39 40 

Cleland, Collins, Cooper, Defoe, Dickens, Favenc, Fielding, Furphy, 

John Wilkie James F. Daniel Charles Ernest Henry Joseph 

1709–89 1824–89 1789–1851 1660–1731 1812–70 1845–1908 1707–54 1843–1912 

UK UK US UK UK UK(A) UK A 

12995 61539 5760 35317 8519 4762 75532 15532 

41 42 43 44 45 46 47 48 

Godwin, Graves, Hardy, 

William Richard Thomas 

Hawthorne, Holcroft, 

James, Henry 

Nath'l Thomas 

Johnson, 

Samuel 

Maturin, C. 

R. 

1715– 

1756– 

1804 

1836 UK 

UK 

1840–1928 1804–64 

UK US 

1745– 1843–1916 

1809 UK US 

1709–84 

UK 

1782–1824 

UK 

28814 23685 12867 18154 19628 33244 16943 16581 

49 50 51 52 53 54 55 56 

Poe, 

Melville, 

Edgar 

Herman 

A. 

Richardson, Scott, 

Samuel Walter 

Smollett, Stockton, 

Tobias Frank R. 

"Mark 

Twain" 

1819–91 c1809– 1689–1761 1771–1832 1721–71 1834–1902 1835–1910 

US 49 US UK UK UK US US

21084 10930 18615 29156 31746 13321 10142 

23.5 Table Forty–five authors born after 1860 (female authors listed first) 

1 2 3 4 5 6 7 8 

Anderson, Bedford, Bowen, Carter, Cather, Erdrich, Franklin, Gardam, 

Jessica Jean Elizabeth Angela Willa Louise Miles Jane 

1920?–99 1946- 

A (UK(A) 

1899– 1940–92 1873– 

1973 UK UK 1947 US 1954-US 

1879– 

1954 

A(UK) 

1928-UK 

12825 6463 11071 15642 12051 19565 16712 10470 

9 10 11 12 13 14 15 16 

Grenville, Hazzard, Hospital, Jolley, Jong, Langley, Lurie, Mansfield, 

Kate Shirley Janette T. Elizabeth Erica Eve Alison Katherine 

1950-A 

1940?- 

1931-A(US) A(US/ 

Can.) 

1923- 

UK(A) 

1942-US 1908– 

1974 A 

1888– 

1926-US 1923 

NZ(UK) 

14482 10359 5700 12533 13545 13345 14287 10827 

17 18 19 20 21 22 23 24 

Murdoch, 

Park, Ruth 

Iris 

Stead, Tennant, Wharton, Wright, 

Christina Kylie Edith Judith 

Barth, 

John 

Bellow, 

Saul 

1919–99 

UK 

1923?- 

NZ(A) 

1902–83 

A(UK) 

1912–88 1862– 1915– 

A 1937 US 2000 A 

1930-US 1915-US 

21437 6755 10442 7112 19269 10012 12836 20342 

25 26 27 28 29 30 31 32 

Boothby, Boyd, Carey, Cary, Cowan, Doctorow, Faulkner, Fowles, 

Guy William Peter Joyce Peter E. L. William John 

1867– 

1905 (A) 

UK 

1952-UK 1943-A 

1888– 

1914-A 

1957 UK 

1931-US 

1897– 

1962 US 1926-UK 

13182 9871 24708 9936 13221 18701 21471 30394 

33 34 35 36 37 38 39 40 

Greene, Hemingway, Johnston, Joyce, Lawson, Lodge, Malouf, McEwan, 

Graham Ernest George James Henry David David Ian 

1904–91 

A 

1899–1961 

US 

1912–70 

A 

1882– 1867– 

1941 UK 1922 A 

1935-UK 1934-A 1948-UK 

9184 14202 24729 6531 16056 8465 12261 18919 

41 42 43 44 45

Palmer, 

Vance 

1885– 

1959 A 

Porter, Hal Sargeson, 

Frank 

1911–84 A 1903–82 

NZ 

Waugh, 

Evelyn 

1903–66 

UK 

Wells, H. 

G. 

1866– 

1946 UK 

13256 14274 10869 11380 50612 

Texts are complege except where specified. Texts marked* are from original or standard 



word-counts. 

23.6 Table One hundred authors: significant differentiations among the 150 most 

common words 

RK. Word t-score DF t-prob. 

MWprob. 

(a) 55 authors born before 1860: 26 women vs. 29 men 

1 1 the −5.18 52 0.0000 0.0000 

2 3 I 3.40 52 0.0013 0.0038 

3 4 of −3.50 51 0.0010 0.0020 

4 5a −2.79 52 0.0073 0.0088 

5 6 was 2.69 49 0.0097 0.0112 

6 7 to (inf.) 3.33 52 0.0016 0.0023 

7 8 in (p.) −3.44 49 0.0012 0.0036 

8 14 her 3.03 52 0.0038 0.0021 

9 15 me 3.55 48 0.0009 0.0019 

10 18 not 2.91 44 0.0057 0.0068 

11 20 she 4.69 52 0.0000 0.0000 

12 21 but 3.30 43 0.0020 0.0020 

13 35 which (rp) −2.38 52 0.0210 0.0358 

14 36 would 2.89 52 0.0057 0.0065 

15 38 this −2.48 52 0.0160 0.0244 

16 41 could 2.67 50 0.0100 0.0149 

17 45 or −2.55 52 0.0140 0.0187 

18 46 one −2.09 52 0.0420 0.0330 

19 52 did 3.04 37 0.0043 0.0029 

20 54 what 3.37 39 0.0017 0.0007 

21 73 never 2.88 35 0.0068 0.0224

RK. Word t-score DF t-prob. 

MWprob. 

22 75 man −2.10 52 0.0410 0.0278 

23 82 thought 3.03 49 0.0039 0.0018 

24 83 do 3.35 32 0.0021 0.0033 

25 99 two −3.36 33 0.0020 0.0006 

26 101 how 3.76 46 0.0005 0.0012 

27 108 know 4.57 29 0.0001 0.0002 

28 115 mother 2.04 44 0.0470 0.0065 

29 122 told 2.58 41 0.0140 0.0123 

30 141 think 3.36 32 0.0021 0.0038 

31 142 go 2.87 47 0.0062 0.0045 

32 149 these −3.75 46 0.0005 0.0002 

(b) 45 authors born after 1860: 22 women vs. 23 men 

1 6 was −2.37 39 0.0230 0.0377 

2 59 so (av. deg.) 3.48 41 0.0012 0.0015 

3 62 time −2.52 41 0.0160 0.0188 

4 72 like (p) 2.61 35 0.0130 0.0114 

5 75 man −3.26 31 0.0027 0.0017 

6 81 before −2.59 42 0.0130 0.0121 

Abbreviations: 

Rk. = rank ex 150 

DF = degrees of freedom 

t-prob and MW-prob. = probahility as assessed on Student's t-test and Mann- 

Whitney test respectively 

inf. = infinitive particle 

p = preposition 

rp = relative pronoun 

av. deg. = adverb of degree 

Among the clausal connectives, but, what, and bow, words often introducing an 

exclamatory note, are favored by the female writers. The auxiliary verbs did and do are 

often used for emphasis. These all stand, perhaps, with not, never, could, and would in 

signifying that the present indicative is not quite as these writers might wish and that a 

certain insistence is thought necessary if their voices are to be heard. The infinitive 

particle to (the marker of compound verbs) and the verbs thought, think, know, and told 

are also favored by the female writers. The point to observe here is that these words have 

less to do with mental activities as such than with overt reference to those activities: "I 

thought that…", "…, I think,…", "…, you know,…" and "as I told you/him/her" are 

typical specimens. Their presence posits a distance between speaker and deed.

Among the smaller group of words favored by the male writers, the association of the/a 

with of I in and this/these and even, perhaps, with one/two suggests an emphasis on 

things and ideas rather than persons. But, as markers of a firmly marshaled, somewhat 

artificial syntax, which/or may reveal more than any of the phenomena so far mentioned 

about the most remarkable aspect of Table 23.6. 

If a body of statistically significant differences between male and female writers can 

disappear completely over a generation or two, as these results suggest, the explanation 

should not be sought in biological differences between the sexes. But if differences in 

"gender", expressed in the little manifestations of male and female social roles touched 

on above, were the whole explanation, it is unlikely that the alteration would be so nearly 

absolute. Irrespective of one's personal standpoint, it is hard to claim that the social roles 

of men and women in English-speaking societies can no longer be distinguished. 

Figure 23.9, the last of my cluster analyses, throws a clear light on these questions. The 

55 entries for the authors born before 1860 fall, chiefly by gender, into two large groups. 

Six of the 29 men are located among the women. Five of the 26 women are located 

among the men. Of these five women, all but one are known to have been educated as if 

they were boys. Of these six men, all but one are known to have grown up without 

benefit of the classical education offered in British grammar schools. (The other two are 

Charlotte Lennox, who was certainly well enough educated to publish her translations 

from the French, and Thomas Alexander Brown – "Rolf Boldrewood" – who was born in 

Britain but came young to Australia.) The idea that a British classical education exerted a 

powerful influence on the way boys wrote while most girls continued to write something 

more like spoken English can be endorsed by making a similar analysis of an even earlier 

subgroup – those of our British writers who were born before 1800. The division between 

male and female is even more pronounced, but Richardson, Defoe, and Charlotte Lennox 

continue as exceptions. It seems clear, in short, that we began to write more like each 

other when we began to go to school together. And the very notion that a general 

difference between males and females was evident is transmuted into the more plausible 

idea that our education deeply affects the way we use our language. 

Reflections 

In its traditional forms, textual analysis has to do with separating, distinguishing, and the 

like and it usually treats of single works. This sort of literary analysis often rests upon 

seemingly intuitive insights and discriminations, processes that may seem remote from 

the gathering and combining and classifying on which I have concentrated and in which 

computational stylistics is usually engaged. But those insights and discriminations are not 

ultimately intuitive because they draw, albeit covertly, upon data gathered in a lifetime's 

reading, stored away in a subconscious memory bank, and put to use, as Samuel Johnson 

reminds us, through processes of comparison and classification, whether tacit or overt. 

When I. A. Richards opened Principles of Literary Criticism (1924) by saying that "A 

book is a machine to think with", he foreshadowed an attack upon what he regarded as 

the undue aestheticizing of literary criticism. So far as it bears upon books themselves,

his remark gains new force from recent changes in the technology of texts. Although my 

own chief premise may also offend some old-fashioned litterateurs, that is not my motive. 

In proposing that literary criticism and analysis are always grounded in the inductive 

processes of classification and differentiation, I wish to emphasize the links between the 

old stylistics and the new. The close readers and analysts of former years and their 

present counterparts, who draw upon computational methods for their textual analyses, 

should actually be close allies. 

Traditional and computational forms of textual analysis do have their distinct strengths. 

The parallels between the two plots of King Lear or the subtleties discerned by Coleridge 

in the opening lines of Hamlet lie as far beyond the reach of a computer as the ironies 

offered by Jane Austen or the ambiguities detected by William Empson. But computers 

obviously surpass our unassisted powers in managing large textual corpora, singling out 

unique forms or gathering all the instances of common ones. 

Within the realm of English studies, statistical work on single authors has usually been 

focused on comparisons of different texts. There are a few such studies at book length, 

treating respectively of Swift, Jane Austen, and Milton (Milic 1967; Burrows 1987; 

Corns 1990). For exemplary studies of authorial development at article length, see 

Brainerd (1980), Craig (1999), and Forsyth (1999). Among those studies of single texts in 

which literary and statistical analysis are associated, there is notable work on Blake's The 

Four Zoas (Ide 1989) and Joyce's Ulysses (McKenna and Antonia 2001). In both 

instances, the argument relies upon internal comparisons between different facets of these 

large and complex texts. 

But the vast textual archives now available offer rich opportunities for the study of single 

texts in a manner that has never before been feasible. (For a brief illustration of this 

approach, see Burrows 2002b: 693–6.) The database of late seventeenth-century verse 

used at the beginning of the present chapter yields well-founded scores for as many 

common words as anyone might wish. Norms derived from the database make it easy to 

study the vocabulary of any poet of the day. We are now able, however, to take a further 

step. The corresponding scores for any poem of adequate length can be compared not 

only with those same norms but also with the author's overall scores. It is therefore 

possible to establish that, in certain demonstrable respects, Absalom and Acbitopbel or 

Upon Appleton House is characteristic either of its author or of some specified group – 

male poets, say, or satirists – but that, in others, it departs from his usual practice or from 

theirs. At this point, I suggest, the evidence flowing from statistical comparison is more 

nearly complementary to the evidence offered by the traditional literary analyst than it 

has ever been. With due allowance for the felt needs and all the mordant joys of a 

continuing sibling rivalry, dare we hope to go forward hand in hand? 

Appendix 

In our main dataset, from which the list of the 150 most common words derives, the 

present corpus of 540,244 words ranges widely across the work of the following 25 

poets: Aphra Behn (1640–89) 21,705 words; Alexander Brome (1620–66) 29,539;

Samuel Butler (1612–80) 30,932; William Congreve (1670–1729) 30,917; Charles 

Cotton (1630–87) 12,625; Abraham Cowley (1618–67) 19,272; Sir John Denham (1615– 

69) 30,092; Charles Sackville, Earl of Dorset (1638–1706) 9,586; John Dryden (1631– 

1700) 18,238; Thomas D'Urfey (1653–1723), 18,757; Robert Gould (1660?-1709?) 

29,110; Andrew Marvell (1621–78) 23,282; John Milton (1608–74) 18,924; John 

Oldham (1653–83) 32,462; Katherine Phillips (1631–64) 29,004; Matthew Prior (1664– 

1721) 32,000; Alexander Radcliffe (floruit 1669–96) 11,889; John Wilmot, Earl of 

Rochester (1648–80) 12,725; Sir Charles Sedley (1639?-1701) 10,304; Elkanah Settle 

(1648–1724) 24,080; Thomas Shadwell (l642?-92) 14,540; Jonathan Swift (1667–1745) 

30,974; Nahum Tate (1652–1715) 20,333; Edmund Waller (1606–87) 16,443; Anne 

Wharton (1659–85) 12,511. Most of the corpus was prepared by John Burrows and 

Harold Love, assisted by Alexis Antonia and Meredith Sherlock. The Marvell subset was 

contributed by Christopher Wortham, assisted by Joanna Thompson. 


NB. For a broader framework than the present argument requires, see the reference list in 

Burrows (2003). 

Biber, Douglas and Edward Finegan (1989). Drift and the Evolution of English Style: A 

History of Three Genres. Language 65: 487–517. 

Brainerd, B. (1980). The Chronology of Shakespeare's Plays: A Statistical Study. 


Bratley, Paul and Donald Ross, Jr (1981). Syllabic Spectra. ALLC Journal 2: 41–50. 

Burrows, J. F. (1987). Computation into Criticism: A Study of Jane Austen's Novels and 

an Experiment in Method. Oxford: Clarendon Press. 

Burrows, J. F. (1992). Computers and the Study of Literature. In C. S. Butler (ed.), 

Computers and Written Texts (pp. 167–204). Oxford: Blackwell. 

Burrows, J. F. (1996). Tiptoeing into the Infinite: Testing for Evidence of National 

Differences in the Language of English Narrative. In N. Ide and S. Hockey (eds.), 

Research in Humanities Computing 4 (pp. 1–33). Oxford: Clarendon Press. 

Burrows, J. F. (2002a). "Delta": a Measure of Stylistic Difference and a Guide to Likely 

Authorship. Literary and Linguistic Computing 17: 267–86. 

Burrows, J. F. (2002b). The Englishing of Juvenal: Computational Stylistics and 

Translated Texts. Style 36: 677–94. 

Burrows, J. F. (2003). Questions of Authorship: Attribution and Beyond. Computers and 

the Humanities 37: 1–26.

Cluett, Robert (1976). Prose Style and Critical Reading. New York: Teachers College 

Press. 

Corns, Thomas N. (1990). The Development of Milton's Prose Style. London: Oxford 


Craig, Hugh (1999). Contrast and Change in the Idiolects of Ben Jonson Characters. 


Eliot, T S. (1932). For Lancelot Andrewes. In Selected Essays. London: Faber. 

Forsyth, R. S. (1999). Stylochronometry with Substrings, or: a Poet Young and Old. 


Forsyth, Richard S., David I. Holmes, and Emily K. Tse (1999). Cicero, Sigonio, and 

Burrows: Investigating the Authenticity of the Consolatio. Literary and Linguistic 

Computing 14: 375–400. 

Halsband, Robert, (ed.) (1970). The Selected Letters of Lady Mary Wortley Montagu. 

London: Longman. 

Hoover, David (2002). Frequent Word Sequences and Statistical Stylistics. Literary and 


Ide, Nancy M. (1989). Meaning and Method: Computer-assisted Analysis of Blake. In 

Rosanne G. Potter (ed.), Literary Computing and Literary Criticism. Philadelphia: 

University of Pennsylvania Press. 

McKenna, C. W. F. and Alexis Antonia (2001). The Statistical Analysis of Style: 

Reflections on Form, Meaning, and Ideology in the "Nausicaa" Episode of Ulysses. 


Milic, Louis T. (1967). A Quantitative Approach to the Style of Jonathan Swift. Paris: 

Mouton. 

Richards, I. A. (1924). Principles of Literary Criticism. London: Routledge. 

Sutherland, James (1969). English Literature of the Late Seventeenth Century. Oxford: 

Clarendon Press.

23.1 Figure Forty long Poems: cluster anlaysis based on the 150 most common 

words of the main corpus 

23.2 Figure Forty-two long poems: cluster analysis based on the 150 most common 

words of the main corpus 

23.3 Figure Forty long poems: cluster analysis based on the 69 most common 

words of "domain 2" 

23.4 Figure Forty long poems: cluster analysis based on the 54 most common 

words of "domain 1"

23.5 Figure Sixty-six tents: cluster analysis based on the 54 most common words of 

"domain 1" 

23.6 Figure Sixty-six texts: cluster anlaysis based on the 150 most common words 

of the main dataser 

23.7 Figure A sketch-map of relationships among the common words of English: a 

rationale for concomitance of frequency 

23.8 Figure Sixty-six texts: cluster analysis based on the 69 most common words of 

"domain 2"

23.9 Figure Fifty-five "hostorians" born before 1860: cluster analysis based on 

the thirty-two most common gender-discriminating words of the databse 

24. 

Thematic Research Collections 

Carole L. Palmer 

Introduction 

The analogy of the library as the laboratory of the humanities has always been an 

exaggeration. For most humanities scholars, it has been rare to find the necessary 

materials for a research project amassed in one place, as they are in a laboratory setting. 

Thematic research collections are digital resources that come closer to this ideal. Where 

in the past scholars produced documents from source material held in the collections of 

libraries, archives, and museums, they are now producing specialized scholarly resources 

that constitute research collections. Scholars have recognized that information 

technologies open up new possibilities for re-creating the basic resources of research and 

that computing tools can advance and transform work with those resources (Unsworth 

1996). Thematic research collections are evolving as a new genre of scholarly production 

in response to these opportunities. They are digital aggregations of primary sources and 

related materials that support research on a theme. 

Thematic research collections are being developed in tandem with the continuing 

collection development efforts of libraries, archives, and museums. These institutions 

have long served as storehouses and workrooms for research and study in the humanities 

by collecting and making accessible large bodies of diverse material in many subject 

areas. Thousands of extensive, specialized research collections have been established, but 

they are often far removed from the scholars and students who wish to work with them. 

In recent years, many institutions have begun to digitally reformat selected collections 

and make them more widely available on the Web for use by researchers, students, and 

the general public. 

Humanities scholars are participating in this movement, bringing their subject expertise 

and acumen to the collection development process. In taking a thematic approach to 

aggregating digital research materials, they are producing circumscribed collections, 

customized for intensive study and analysis in a specific research area. In many cases 

these digital resources serve as a place, much like a virtual laboratory, where specialized 

source material, tools, and expertise come together to aid in the process of scholarly work 

and the production of new knowledge.

This chapter is focused primarily on the thematic research collections created by scholars. 

The history and proliferation of the new genre cannot be examined in full within the 

limits of the chapter. Instead, this essay will identify characteristics of the genre and 

clarify its relationship to the collection development activities that have traditionally 

taken place in research libraries. This relationship is central to understanding how our 

stores of research materials will evolve in the digital realm, since thematic research 

collections are derived from and will ultimately contribute to the larger institution-based 

collections. The thematic collections used as examples throughout the chapter have been 

selected to illustrate specific features and trends but do not fully represent the variety of 

collections produced or under development. Although there are numerous collections 

available for purchase or through licensing agreements, all the examples identified here 

were available free on the Web as of October 2002. 

Characteristics of the Genre 

There are no firm parameters for defining thematic research collections (hereafter 

referred to as thematic collections), but there are characteristics that are generally 

attributable to the genre. John Unsworth (2000b) describes thematic collections as being: 

electronic 

heterogeneous datatypes 

extensive but thematically coherent 

structured but open-ended 

designed to support research 

authored or multi-authored 

interdisciplinary 

collections of digital primary resources 

These characteristics are common to the projects developed at the Institute for Advanced 

Technology in the Humanities (IATH), the research and development center at the 

University of Virginia, previously directed by Unsworth. They are also broadly 

applicable to many of the research collections being developed elsewhere by scholars, 

librarians, and collaborative teams. With thematic collections, however, there is 

considerable synergy among these characteristics, and as the genre grows and matures 

additional characteristics are emerging that differentiate it from other types of digital 

resources. 

Table 24.1 reworks Unsworth's list of descriptors, separating content and function aspects 

of the collections and adding emerging features of the genre. The first tier of features

contains basic elements that are generally shared by thematic collections. In terms of 

content, they are all digital in format and thematic in scope. In terms of function, they are 

all intentionally designed to support research. The next tier of features further defines the 

makeup and role of thematic collections, and together they reflect the unique contribution 

this type of digital resource is making to research in the humanities. Unlike the basic 

elements, these characteristics are highly variable. They are not represented in all 

thematic collections, and the degree to which any one is present in a given collection is 

varied. Collections differ in the range and depth of content and the types of functions 

provided. The basic elements and all the content features, outlined below, are closely 

aligned with Unsworth's description. The emergent function features are explicated more 

fully in sections that follow. 

Thematic collections are digital in format. While the sources may also exist as printed 

texts, manuscripts, photographs, paintings, film, or other artifacts, the value of a thematic 

collection lies in the effectiveness of the digital medium for supporting research with the 

materials. For example, through advances in information technology, creators of The 

William Blake Archive have been able to produce images that are more accurate in color, 

detail, and scale than commercially printed reproductions, and texts more faithful to the 

author's originals than existing printed editions (Viscomi 2002). 

The contents are thematic or focused on a research theme. For example, a number of the 

IATH collections are constructed around author-based themes, including The Complete 

Writings and Pictures of Dante Gabriel Rossetti: A Hypermedia Research Archive, The 

Dickinson Electronic Archives, and The Walt Whitman Archive. Collections can also be 

developed around a literary or artistic work, such as Uncle Tom's Cabin and American 

Culture. A collection called Hamlet on the Ramparts, designed and maintained by the 

MIT Shakespeare Project, is a good example of a collection based on a narrowly defined 

literary theme. That project aims to bring together texts, artwork, photographs, films, 

sound recordings, and commentary related to a very specific literary entity, Hamlet's first 

encounter with the ghost. A collection theme can be an event, place, phenomenon, or any 

other object of study. Interesting examples outside of literary studies include the Salem 

Witch Trials, Pompeii Forum, and the Waters of Rome projects. Some thematic 

collections are embedded in larger digital resources. One example is the Wilfred Owen 

Multimedia Digital Archive, a core content area in the Virtual Seminars for Teaching 

Literature, a pedagogical resource developed at Oxford University. 

Like traditional library collections in the humanities, thematic collections have been built 

for research support, but the new genre is producing more specialized microcosms of 

materials that are tightly aligned with specific research interests and that aid in specific 

research processes. Some thematic collections have been designed as new virtual 

environments for scholarly work. For example, Digital Dante, a project produced at the 

Institute for Learning Technologies at Columbia University, has been conceived as a 

"place" for study and learning and a "means" of scholarly production.

24.1 Table Features of thematic research collections 

Content Function 

Basic elements 

* Digital 

* Thematic 

Variable characteristics 

Research support 

* Coherent Scholarly contribution 

* Heterogeneous Contextual mass 

* Structured Interdisciplinary platform 

* Open-ended Activity support 

The thematic framework allows for coherent aggregation of content. All the materials 

included assist in research and study on the theme. This coherence is generally anchored 

by a core set of primary sources. The capabilities of networked, digital technology make 

it possible to bring together extensive corpuses of primary materials and to combine those 

with any number of related works. Thus the content is heterogeneous in the mix of 

primary, secondary, and tertiary materials provided, which might include manuscripts, 

letters, critical essays, reviews, biographies, bibliographies, etc., but the materials also 

tend to be multimedia. The digital environment provides the means to integrate many 

different kinds of objects into a collection. In literary studies collections, multiple 

versions of a given text are commonly made available to aid in comparative analysis, 

along with additional types of media such as maps, illustrations, and recorded music. For 

example, Uncle Tom's Cabin and American Culture contains different editions of the 

primary text along with poems, images, films, and songs that show the context and 

history surrounding the primary work (Condron et al. 2001). 

The individual items in a collection are structured to permit search and analysis, with 

most projects in the humanities adopting SGML-based markup formats. Many aspects of 

a source may be coded, including bibliographic information, physical features, and 

substantive content, to produce highly flexible and searchable digital materials. The 

collection as a whole is further organized into interrelated groups of materials for display 

and to assist in retrieval. Libraries, archives, and museums have conventions for 

structuring and representing collections, which include systems and guidelines for 

applying metadata, classification schemes, and descriptors. Some of these methods are 

being applied in thematic collections, but scholars are also designing new approaches and 

developing new standards, such as the TEI (Text Encoding Initiative) markup language 

for tagging scholarly material, that are more attuned with scholarly practices. As a result, 

there is not yet uniformity in the methods used for structuring data in digital collections, 

but there are ongoing efforts to align standards and a growing awareness by collection 

developers of the value of developing interoperable systems.

Collections of all kinds can be open-ended, in that they have the potential to grow and 

change depending on commitment of resources from collectors. Most thematic 

collections are not static. Scholars add to and improve the content, and work on any given 

collection could continue over generations. Moreover, individual items in a collection can 

also evolve because of the inherent flexibility (and vulnerability) of "born digital" and 

transcribed documents. The dynamic nature of collections raises critical questions about 

how they will be maintained and preserved as they evolve over time. 

Scholarly Contribution 

While thematic collections support both research and pedagogy, the scholarly 

contribution that results from the creation and use of the resources is what qualifies them 

as a scholarly genre. When electronic sources are brought together for scholarly purposes 

they become a new, second-generation electronic resource (Unsworth 2000b). Scholars 

are not only constructing environments where more people can do research more 

conveniently, they are also creating new research. Like other scholarship in the 

humanities, research takes place in the production of the resource, and research is 

advanced as a result of it. Thus, scholarship is embedded in the product and its use. And 

like research generated in the fields of engineering, computer science, and information 

science, some of the research contribution lies in the technical design, functionality, and 

innovation that makes new kinds of research possible. 

Authorship is included in Unsworth's description of thematic collections, but the term 

does not fully capture the nature of the work involved in developing thematic collections. 

A collection is not always attributable to one author or even a few co-authors, and the 

process of production may not generate new, original content. Some of the technical 

work involved in creating a collection requires expertise outside that of a typical author in 

the humanities. Literary scholars who are assembling electronic texts and archives of 

multimedia objects have become "literary-encoders" and "literary-librarians" 

(Schreibman 2002). Moreover, as noted above, thematic collections are inherently openended 

and therefore can be added to and altered in dramatic ways over time by new 

participants in the process. After a collection has been established for some time, it may 

not be accurate to continue to assign complete authorship to the originator, and it may 

prove complicated to trace authorial responsibility as it evolves over years or even 

decades. 

In many ways collection work resembles that of an editor, but the activities of curators, 

archivists, and compilers are also applicable. But the concept of author is useful in its 

ability to relate the significance of the purposeful organization of information. As 

Atkinson notes in reference to professional collection developers in research libraries, 

since "every text is to some extent a compilation of previous texts, then the collection is a 

kind of text – and the building of the collection is a kind of authorship" (1998: 19). 

Nonetheless, "creator" seems best for encapsulating the range of work involved in the 

development of thematic collections. The term "creator" has become common in standard 

schemes for describing electronic resources, such as the Dublin Core metadata element 

set, and it accommodates the technical, intellectual, and creative aspects of the digital

collection development process. Academic subject expertise is considered critical in the 

development of quality, scholarly digital resources (Crane and Rydberg-Cox 2000), and 

technical computing knowledge of text, image, audio and video standards, and 

applications is equally important. Thus, the creators of scholarly collections will need to 

be a new kind of scholar, or team, with a distinct mix of expertise in at least three areas – 

the specific subject matter and associated critical and analytical techniques, technical 

computing processes, and principles of content selection and organization. 

Contextual Mass 

The creators of thematic collections are constructing research environments with 

contextual mass, a proposed principle for digital collection development that prioritizes 

the values and work practices of scholarly communities (Palmer 2000). The premise 

behind the principle is that rather than striving for a critical mass of content, digital 

research libraries should be systematically collecting sources and developing tools that 

work together to provide a supportive context for the research process. For libraries, this 

approach to collection development requires analysis of the materials and activities 

involved in the practices of the different research communities served (Brockman et al. 

2001). Researchers are able to more readily construct contextual mass for themselves 

through highly purposeful selection and organization of content directly related to their 

specialized areas of research. 

All collections are built through the process of privileging some materials over others 

(Buckland 1995), and the construction of contextual mass takes place through careful, 

purposeful privileging. Because of the specific scope and aims of thematic collections, 

creators select materials in a highly focused and deliberate manner, creating dense, 

interrelated collections. By contrast, in both physical and digital libraries, materials are 

usually separated for reasons unimportant to the scholar. For example, primary texts may 

be part of an isolated rare book room or special collection, while secondary works are in 

separate book and journal collections, with indexes, bibliographies, and handbooks kept 

in reference areas. Moreover, the historical, literary, and cultural treatments of a topic are 

likely to be further scattered across differentiated classes of subjects. When a person uses 

a research library collection they are interacting with a context that includes physical, 

institutional, and intellectual features (Lee 2000). It is a grand and scattered context 

compared to that of thematic collections, which tend to focus on the physical context of 

core primary sources and the intellectual context represented in a mix of heterogeneous 

but closely associated materials. 

Collections built on a contextual mass model create a system of interrelated sources 

where different types of materials and different subjects work together to support deep 

and multifaceted inquiry in an area of research. Although many of the resources 

referenced in this chapter contain large, complex cores of primary materials, this is not 

necessary to achieve contextual mass. For instance, the Decameron Web project, a 

collection devoted to the literary, historical, and cultural context of Boccaccio's famous 

text, contains an established critical edition with translations and a selection of related 

materials, such as annotations, commentaries, critical essays, maps, and bibliographies.

The pedagogical intent of the site is obvious in its content and layout, but it is 

simultaneously strong as a research context. 

A number of existing thematic collections exemplify the notion of contextual mass in 

their depth and complexity, as well as in their explicit goals. The core of the Rossetti 

archive is intended to be all of Rossetti's texts and pictorial works, and this set of primary 

works is complemented by a corpus of contextual materials that includes other works 

from the period, family letters, biography, and contemporary secondary materials. In the 

Blake archive, "contextual" information is at the heart of the scholarly aims of the 

project. The documentation at the website explains that works of art make sense only in 

context. In this case creating a meaningful context involves presenting the texts with the 

illustrations, illuminated books in relation to other illuminated books, and putting those 

together with other drawings and paintings. All of this work is then presented in the 

context of relevant historical information. 

Collaboration is required in the creation of contextually rich thematic collections. Instead 

of being a patron of existing collections, scholars must partner with libraries, museums, 

and publishers to compile diverse materials that are held in different locations. For 

example, collections from the Boston Public Library, the New York Public Library, the 

Massachusetts Historical Society, the Massachusetts Archives, and the Peabody Essex 

Museum were melded to create the Salem Witch Trials collection. The Dickinson 

Electronic Archives, produced by a collective that includes four general editors who work 

collabora-tively with co-editors, staff, and users, has reproduced works housed in library 

archives all along the northeast corridor of the United States (Smith 1999). 

Interdisciplinary Platform 

Humanities scholars have long been engaged in interdisciplinary inquiry, but the library 

collections they have relied on have been developed around academic structures that tend 

to obscure connections between fields of research. This is partly because of the large 

scale of research libraries, but also because of the inherent difficulties in maintaining 

standard organization and access systems for materials that represent a complex base of 

knowledge. The continuing growth of interdisciplinary research is a recognized challenge 

to collection development and the provision of information services in research libraries, 

and recent studies have identified specific practices of interdisciplinary humanities 

scholars that need to be better supported in the next generation of digital resources 

(Palmer 1996; Palmer and Neumann 2002). Creators of thematic collections are 

beginning to address some of these needs through the conscious development of 

interdisciplinary platforms for research. 

A number of collections have been explicitly designed to be conducive to 

interdisciplinary research and have effectively incorporated the interests of diverse 

intellectual communities. The Thomas MacGreevy Archive aims to promote inquiry into 

the interconnections between literature, culture, history, and politics by blurring the 

boundaries that separate the different fields of study. Monuments and Dust, a thematic 

collection focused on Victorian London, states its interdisciplinary intent in the project

subtitle: "New Technologies and Sociologies of Research." A stated objective of the 

project is to foster international collaboration and intellectual exchange among scholars 

in literature, architecture, painting, journalism, colonialism, modern urban space, and 

mass culture. The premise is that the aggregation of diverse sources – images, texts, 

numerical data, maps, and models – will seed intellectual interaction by making it 

possible to discover new visual, textual, and statistical relationships within the collection 

and between lines of research. 

Activity Support 

The functions of thematic collections are being greatly expanded as creators add tools to 

support research activities. Humanities research processes involve numerous activities, 

and collections are essential to many of them. Scholarly information seeking is one type 

of activity that has been studied empirically for some time, and our understanding of it is 

slowly informing the development of information retrieval systems in the humanities 

(Bates 1994). Other significant research practices, especially those involved in 

interpretation and analysis of sources, have received less attention. 

Reading is of particular importance in the humanities, and the technologies being 

developed for collections are beginning to address the complexities of how and why 

people read texts (Crane et al. 2001). Scanning a text is a different activity from rereading 

it deeply and repeatedly over time, and it is but one stage of a larger pattern of wide 

reading and collecting practiced by many humanities scholars (Brockman et al. 2001). 

Other common scholarly activities, such as the "scholarly primitives" identified by 

Unsworth (2000a), have yet to be adequately supported by the digital resources designed 

for scholars. These include the basic activities of annotating, comparing, referring, 

selecting, linking, and discovering that are continually carried out by scholars as part of 

the complex processes of reading, searching, and writing. Just as materials can be 

structured for scholarly purposes as we transform our bodies of texts into digital format, 

tools can be tailored for specific scholarly tasks. 

Searching collection content is a standard function of databases of all kinds, and among 

thematic collections there is considerable variation in the level of retrieval supported. 

Image searching has been a vital area of development, since many collections contain a 

significant number of facsimile images and other pictorial works. In the Blake archive, 

for instance, access to images is enhanced through extensive description of each image 

and the application of a controlled vocabulary. Likewise, the Rossetti archive project has 

been dedicated to developing methods for formally coding features of images to make 

them amenable to full search and analysis (McGann 1996). Imagesizer, a tool developed 

at IATH and provided to both the Blake and Rossetti archives, allows the user to view 

images in their original size or in other convenient dimensions. Inote, another IATH tool 

configured for the Blake project, allows viewing of illustrations, components of 

illustrations, and image descriptions. It also allows users to create their own personal 

annotations along with saved copies of an image. This, in particular, is a key 

advancement in activity support for collections since it goes beyond searching, viewing, 

and linking to assist scholars with the basic tasks of interpretation and note-taking,

activities that have been largely neglected in digital resource development. Tools to 

support scholarly tasks are also being developed independent of thematic collection 

initiatives. For example, the Version-ing Machine software designed by the Maryland 

Institute for Technology in the Humanities (MITH) blends features of the traditional book 

format with electronic publishing capabilities to enhance scholars' interpretive work with 

multiple texts. 

Hypertext has been a monumental advancement in the functionality of collections, and 

many current projects are working toward extensive interlinking among aggregated 

materials. The Walt Whitman Hypertext Archive intends to link sources to demonstrate 

the numerous and complex revisions Whitman made to his poems. The items associated 

with a poem might include the initial notes and trial lines in a notebook, a published 

version from a periodical, publisher's page proofs, and various printed book versions. The 

Bolles Collection on the History of London, part of the larger Perseus digital library, 

exploits hypertext by presenting full historical texts on London with hyperlinks from 

names of towns, buildings, and people to correlate items, such as photographs and maps 

of places and drawings and biographies of people. 

Mapping and modeling tools are valuable features of a number of place-based thematic 

collections. The Bolles Collection provides electronic timelines for visualizing how time 

is represented within and across documents. Also, historical maps of London have been 

integrated with a current geographic information system (GIS) to allow users to view the 

same location across the maps (Crane et al. 2001). Modeling adds an important layer of 

data to the Monuments and Dust collection. A variety of VRML (Virtual Reality 

Modeling Language) images of the Crystal Palace have been developed from engineering 

plans and other sources of data to create models of the building and small architectural 

features such as drains, trusses, and wall panels, as well as animations of the building's 

lighting and other three-dimensional replicas. 

The Humanities Laboratory 

The thematic collections concentrating on contextual mass and activity support are 

coming closest to creating a laboratory environment where the day-to-day work of 

scholars can be performed. As with scientific laboratories, the most effective places will 

be those that contain the materials that need to be studied and consulted during the course 

of an investigation as well as the instrumentation to carry out the actual work. For 

humanities scholars, a well-equipped laboratory would consist of the sources that would 

be explored, studied, annotated, and gathered in libraries and archives for an area of 

research and the means to perform the reading, analyzing, interpreting, and writing that 

would normally take place in their offices. The most successful of these sites will move 

beyond the thematic focus to provide contextual mass and activity support that is not only 

responsive to what scholars currently do, but also to the questions they would like to ask 

and the activities they would like to be able to undertake. 

In the sciences the virtual laboratory, or collaboratory, concept has been around for some 

time. Traditional laboratories that are physically located encourage interaction and

cooperation within teams, and collaboratories extend that dimension of research to 

distributed groups that may be as small as a work group or as large as an international 

research community. Collaboratories are designed as media-rich networks that link 

people to information, facilities, and other people (Finholt 2002). They are places where 

scientists can obtain resources, do work, interact, share data, results, and other 

information, and collaborate. 

Collaborative processes have not been a significant factor in technology development in 

the humanities, due at least in part to the prevalent notion that humanities scholars work 

alone. This is true to some degree. Reading, searching databases, and browsing 

collections are solitary activities, and most articles and books are written by individuals. 

However, most humanities scholars do work together in other important ways. They 

frequently share citations, ideas, drafts of papers, and converse about research in 

progress, and these interactions are dependent on strong relationships with others in an 

intellectual community. For most scholars, this type of collaborative activity is a 

necessary part of the daily practice of research, and it has been shown to be especially 

vital for interdisciplinary work (Palmer and Neumann 2002). 

An increasing number of thematic projects are encouraging scholars to share their 

research through the submission of content and by providing forums for dialogue among 

creators and the user community. A few initiatives are aimed at enabling collaboration 

among scholars, as with the case of Monuments and Dust, noted above. In other projects, 

the corpus brought together is considered a resource to foster collaboration among 

scholars, students, and lay readers. While it is not a thematic collection per se, Collate is 

a unique resource that provides tools for indexing, annotation, and other types of work 

with digital resources. This international project has been designed to support the 

development of collections of digitized historic and cultural materials, but its other 

primary goal is to demonstrate the viability of the collaboratory in the humanities. 

The Process of Collocation 

All collections, either physical or virtual, are formed through collocation, the process of 

bringing together related information (Taylor 1999). Collocation is a useful term because 

it emphasizes the purpose of collection building and can be applied to the different means 

used to unite materials. The collocation of research materials can take many forms. 

Anthologies are collocations of selected works on a subject. In a traditional archive, 

collocation is based on the originating source – the person or institution – and these 

collections are acquired and maintained as a whole. Collocation is often associated with 

physical location, such as when materials by the same author are placed together on 

shelves in a library. It is not surprising that some thematic collections have adopted the 

metaphor of physical collocation. For example, the Decameron Web describes its 

collection as a "specialized bookshelf or mini-library" generated from Boccaccio's 

masterpiece. The Tibetan and Himalayan Digital Library describes its collections as the 

equivalent of the "stacks" in a traditional library. A library catalogue also provides 

collocation by bringing together like materials through a system of records and 

references.

To see a substantial portion of the works associated with a particular author or topic, it 

has been necessary for scholars to consult many catalogues and indexes and travel to 

different libraries, copying and collecting what they can along the way. In the case of 

fragile items, handling is limited and photocopying or microfilming may be prohibited. 

With thematic collections, scholars are now exercising the power of virtual collocation. 

By pulling together materials that are part of various works and located in repositories at 

different sites, they collocate deep, sophisticated collections of sources that can be used at 

a convenient time and place. For example, the directory of the Rossetti archive, which 

lists pictures, poems, prose, illustrated texts, double works, manuscripts, books, 

biography, bibliography, chronology, and contexts, illustrates the diversity and richness 

that can be achieved through the collocation of digital materials. 

The physical proximity of resources becomes trivial when the material is digital and 

made available in a networked information system (Lagoze and Fielding 1998), but the 

intellectual and technical work of selecting and structuring meaningful groupings of 

materials remains critical. This is especially true in the humanities, where research often 

concentrates on the documents and artifacts created by or surrounding an object of study. 

Compared with other fields of study, the sources used are highly heterogeneous and 

wide-ranging, and their value persists over time, rather than dissipating through 

obsolescence. Over the course of a scholar's research project, certain manuscripts or 

artifacts in foreign archives may be essential, but a standard edition or a popular culture 

website can be equally important (Palmer and Neumann 2002). The distributed, dynamic 

research collections that can be created on the Web are attuned with the nature of 

humanities fields, which are "concerned with the construction of knowledge from sources 

of different types, scattered across different subject areas" (Fraser 2000: 274). 

The principles that guide the collocation of research collections in libraries are different 

from scholarly motivations for collocation. Library collections are amassed for 

preservation, dispensing, bibliographic, and symbolic purposes (Buckland 1992). The 

process of collecting is ruled first by the mission of the institution and more specifically 

by the selection criteria developed to support research and teaching in a given subject 

area. In contrast, collections produced by scholars are customized to the research focus of 

a scholarly community or to the specific interests of the creators. Thus, the "principles of 

inclusion" for The William Blake Archive – that designate the illuminated books as the 

foundation and the strategy of adding clusters of materials based on medium, theme, or 

history – are idiosyncratic to that particular project. Schreibman suggests that the Blake 

archive and other early thematic collections, as well as broader collection initiatives such 

as the CELT Project and the Women Writers Project, have been governed by a digital 

library model that collocates "previously published texts based on a theory of collection 

appropriate to the particular archive" (2002: 287). While a loose theory of collecting may 

be guiding creators' selection of content, the criteria being used to determine what is 

appropriate for a collection and the long-term development principles of a project are not 

always clarified for users of thematic collections. 

The potential of digital collocation has been restrained by copyright concerns. It is much 

less complicated to digitize and redistribute sources that do not have copyright

estrictions, and therefore older materials in the public domain have been more widely 

selected for digital collections of all kinds. Increasingly, thematic collection creators are 

working through copyright requirements for published works, as well as adding new, 

born digital sources, to build systematic and principled collections that meet their 

scholarly aims. Again, The William Blake Archive is one of the projects that offer a sound 

model on this front. They have gained access to valuable and important materials for their 

collection by working closely with museums, libraries, and collectors to address their 

copyright concerns. 

Research Collections Landscape 

Scholarly thematic collections are a new addition to the array of existing and emerging 

research collocations. Interestingly, many thematic collections created by scholars refer 

to themselves as archives, but conventional archives differ in important ways, especially 

in terms of their mission and the methods they use to organize materials. The collections 

held in archival repositories document the life and work of institutions or individuals. An 

archive's role is to "preserve records of enduring value that document organizational and 

personal activities accumulated in the course of daily life or work" (Taylor 1999: 8). 

Archival collections are collocated according to provenance – the individual or corporate 

originator – and organized in original working order. As accumulations of materials from 

regular daily life, these collections may contain print and electronic documents and 

artifacts of any kind, including meeting minutes, annual reports, memoranda, deeds, 

manuscripts, photographs, letters, diaries, printed books, and audio recordings. 

Thematic collections are more analogous to the subject collections traditionally 

developed in research libraries than they are to archives. Examples of research library 

subject collections include the Chicano Studies collection at the University of California 

at Berkeley and the Historical Linguistics collection at the Newberry Library in Chicago. 

A standard directory lists 65,818 library and museum subject collections in the United 

States and Canada (Ash 1993), many of which are thematic in scope. For example, the 

entry for William Blake lists 20 collections, two of which are contributors to The William 

Blake Archive project. Subject collections are sometimes developed cooperatively by 

multiple institutions, and these tend to cover broad academic or geographic categories. 

Examples include the Urban Studies collection at the Center for Research Libraries, a 

membership organization devoted to cooperative collection programs, and the East Asian 

collections cooperatively developed at the University of North Carolina and Duke 

University. 

Localized collections that contain rare or valuable items may be kept as part of a special 

collection. Special collections departments develop concentrated subject and theme-based 

collections that include substantial primary materials. They are also the place where 

manuscripts, papers, and other unique, fragile, or valuable items are maintained and 

segregated for restricted access. As research libraries began to make materials accessible 

via the Web, the contents of special collections were often the first to be selected for 

digitization. Research libraries have been eager to share their treasures with a wider 

audience and make them more convenient to view, and offering a digital alternative

decreases handling and the wear and tear on valuable materials. The first digitized special 

collections released by the Library of Congress in 1994 through their American Memory 

project were photographic collections. The initiative has since grown to offer over 100 

online collections, many of which are thematic and multimedia. Most are based on 

existing special collections within the Library of Congress, but some, such as Band Music 

from the Civil War Era and the American Variety Stage, are thematic collections that 

have been collocated for the first time specifically for online presentation. Many other 

institutions have selected notable special collections for digitization. For instance, the 

Academic Affairs Library at the University of North Carolina at Chapel Hill has 

produced Documenting the American South, a digital collection based on their existing 

Southern Historical Collection, one of the largest stores of Southern manuscripts in the 

country. 

As research libraries continue to undertake these projects, substantial bodies of 

previously hidden source material are coming into public view. These digital collections 

make an important contribution to digital research library development and provide 

potential raw materials for the construction of thematic collections and other aggregations 

of digital content. Digital special collections provide an important service for researchers, 

but they generally do not possess the range of scholarly functions – scholarly 

contribution, contextual mass, interdisciplinary platform, and activity support – provided 

by many thematic collections. 

Digital Collections Terminology 

There is little consistency in the terms used to describe digital resources, and the number 

of terms and the overlap between them seem to be increasing. In addition to being a new 

conceptualization of research collection development, the phrase "thematic research 

collection" is itself a new addition to the vocabulary. As discussed above, the term 

"archive" is being widely applied to thematic collections, and the adoption of the word 

has merit for this purpose, since, like traditional archives, scholarly thematic collections 

tend to focus on primary sources and emphasize the importance of the physical object. 

For example, The Dickinson Electronic Archives prioritizes the physical object by 

representing Emily Dickinson's poems, letters, letter-poems, drafts, fragments, and 

manuscripts as facsimile images. Likewise, in the William Blake Archive the physical 

nature of the artifacts is a central thrust. The digital collection represents the integration 

of Blake's illustrations and texts and the variations among different copies of his books, 

features that have not been well represented in printed editions of his work. As with most 

thematic collections, the actual goals of the Blake project reach beyond those of a 

traditional archive, where the central aim would be to preserve the physical record of 

production. Here the notion of the archive has been extended to include the catalogue, 

scholarly edition, database, and tools that work together to fully exploit the advantages of 

the digital medium (Viscomi 2002). 

It has been suggested that electronic archives will increasingly take the form of hypertext 

editions, similar to the Electronic Variorum Edition of Don Quixote being developed at 

the Center for the Study of Digital Libraries at Texas A & M University (Urbina et al.

2002). But, at present, many kinds of resources, including journal article pre-print servers 

and lists of links on a web page, are being referred to as digital or electronic archives. 

The traditional, professional archive still holds an important place in the array of research 

collections, and some of these are being digitally reformatted while retaining their 

original aims and organizational methods. At the same time, colloquial applications of the 

term are increasing and new scholarly idealizations of the concept are evolving. 

The vocabulary of digital resources has been further complicated by the wide usage of the 

term "digital library" for all kinds of digital collections, essentially blurring the 

distinction between collections and libraries. Of the many definitions of digital libraries 

circulating in the literature, several can be readily applied to thematic collections. 

However, a widely accepted conception in the field of library and information science 

clarifies the difference: 

Digital libraries are organizations that provide the resources, including the specialized 

staff, to select, structure, offer intellectual access to, interpret, distribute, preserve the 

integrity of, and ensure the persistence over time of collections of digital works so that 

they are readily and economically available for use by a defined community or set of 

communities. 

(Waters 1998) 

A thematic collection is not a library in the organizational sense; it is a collection that 

may be developed or selected for inclusion in a digital library, or it may exist separately 

from any library or similar institution. A library contains a collection of collections and 

has an institutional commitment to services that ensure access and persistence. Because 

of their size and diverse user population, libraries, including digital libraries, generally 

lack the coherency and the functional features characteristic of thematic collections. 

In the humanities, the Perseus project is considered an exemplar digital library. It serves 

as a good, albeit complex, example of the relationship between collections and libraries. 

The scope of Perseus was originally disciplinary rather than thematic, providing access to 

an immense integrated body of materials in Classics, including primary Greek texts, 

translations, images, and lexical tools (Fraser 2000). As the project has grown, as both a 

scholarly resource and a research initiative, it has added collections outside the realm of 

Classics. The mix of subject collections within Perseus represents an interesting variety 

of collection-building approaches in terms of scope and mode of creation. Three 

geographically oriented collections, California, Upper Midwest, and Chesapeake Bay, 

have been developed in association with the Library of Congress's American Memory 

project. The library also contains an extensive body of primary and secondary materials 

covering the early modern period. The best example of a thematic collection within 

Perseus is the pre-twentieth-century London segment based on the Bolles Collection on 

the History of London. It is a digitized recreation of an existing special collection that is 

homogeneous in theme but heterogeneous in content. As noted previously, it interlinks 

maps of London, relevant texts, and historical and contemporary illustrations of the city.

The Tibetan and Himalayan Digital Library (THDL) is another kind of digital library/ 

thematic collection hybrid. It is being designed to capitalize on internal collocation of an 

underlying base of holdings to create multiple collections with different structures and 

perspectives. For example, the Environment and Cultural Geography collection organizes 

the library's texts, videos, images, maps and other types of materials according to space 

and time attributes. The thematic and special collections are organized by subject 

attributes. The categorization scheme used in the THDL is an interesting case of variant 

applications of digital resource terminology. Its special collections are thematic, with a 

focus, for example, on the life or activities of an individual, while the thematic 

collections integrate diverse sources within broad disciplinary units, such as Art, 

Linguistics, Literature, or Music. Subtheme collections, which are independent projects 

with their own content and goals, are nested within the thematic collections. 

Needless to say, there is much overlap between digital library and thematic collection 

efforts, and variations and hybrids will continue to evolve along with the terminology. 

Digital research libraries will no doubt continue to acquire the collections built by 

scholars, collaborative teams, and institutions, while scholars' projects grow to nest and 

annex digital special collections. An important outcome of this activity is that expert 

collocation of research materials by scholars is adding an important new layer of 

resources to humanities research collections. 

Turn in the Collection Cycle 

In the past, scholars used collections for their research and contributed to collections as 

authors, but their role as collection builders was limited. They developed significant 

personal collections for their own purposes, and they collocated materials by editing and 

publishing collected works. On the other hand, collection development has long been a 

significant part of the professional responsibilities of librarians, archivists, and curators. 

The interaction between the scholarly community and collection professionals has been 

an important influence on the development of research library collections. As the primary 

constituency of research libraries, scholars' questions, requests, and ongoing research and 

teaching activities have guided the collection processes at research institutions. Of 

course, the essential contribution of scholars has been as creators of intellectual works 

that make up a large proportion of research collections. Now scholars have also become 

creators of research collections, and this change will have an important impact on how 

our vast arrays of research materials take shape in the future. 

Where libraries once acquired the documents authored by scholars, they now also need to 

collect the thematic research collections created by scholars. This genre has new qualities 

that cannot be treated in the same way as a printed or electronic single work, and the 

interdisciplinary, multimedia, and open-ended characteristics of the resources, further 

complicate matters. Libraries are not yet systematically collecting the collections 

produced by scholars, in part because of the newness of the genre, but also because this 

type of meta-collecting is an unfamiliar practice. In fact, most research libraries do not 

yet collect and catalogue non-commercial, web-based digital materials of any kind. For 

example, at the time of this writing, WorldCat, a major bibliographic database of library

holdings, indicated that 263 libraries had purchased and catalogued a recent book of 

criticism on William Blake by William Vaughan. In contrast, only 26 libraries had added 

the William Blake Archive collection to their catalogue. As a point of reference, 34 

libraries had catalogued Voice of the Shuttle, a humanities gateway that is widely used 

but less similar to the scholarly creations traditionally collected by libraries than the 

materials in a typical thematic collection. As research libraries begin to regularly acquire 

and catalogue thematic collections, they will be interjecting a new layer of collecting 

activities and causing a shift in the scholarly information transfer cycle. 

The traditional cycle of document transfer as conceptualized before the advent of digital 

documents (King and Bryant 1971) required publishers and libraries to take documents 

through most steps of the process. A large part of the production and distribution of 

scholarly research materials still adheres to this cycle. It begins with use of a document 

for scholarly work, usually in conjunction with many other documents, which leads to 

composition by the scholar. At this point, the scholar becomes an author, in addition to an 

information user. A publisher handles the reproduction or printing and distribution phase. 

Libraries move published documents through the circuit by selecting, acquiring, 

organizing, and storing them, and then by making them accessible, usually on library 

shelves and through representation in catalogues and indexes. Use of the materials is 

enhanced by assistance from reference, instruction, and access services provided by the 

library. The cycle is completed when the material becomes part of the research process 

by being accessed and assimilated by a scholar. 

To some degree, libraries can treat thematic collections like the documents produced by 

scholars – by selecting, acquiring, and organizing them, as has been done by the 26 

libraries that have catalogued the Blake archive. Over time, the distribution of scholarly 

materials in the humanities may be greatly expanded by scholars as they take up selection 

and organization activities. Perhaps even more importantly, scholars are adding valuable 

features to collections as they customize them for their scholarly purposes. While 

research libraries strive to meet the information needs of the communities they serve, 

they are not equipped or charged to fully support the scholarly process. Selection criteria 

for collections in research libraries emphasize how to choose the best items from the 

universe of publications being produced relative to the research communities being 

served. Measurements of satisfaction, circulation, and Web activity are combined with 

librarians' knowledge of their scholarly constituencies, which grows based on what 

scholars ask for and what they reveal to librarians about their interests and projects. Less 

attention has been paid to assessing how to prioritize materials in terms of what scholars 

do, what they value, or what would be the most likely to enhance specific research areas. 

Many research libraries are currently focusing on global approaches to digital collection 

building by producing expansive gateways for all their user communities. At the same 

time, researchers are creating their own repositories and tools, highly customized to the 

scholarly work of their intellectual communities. Research libraries will need to fill the 

gap by developing mid-range collection services that actively collocate thematic 

collections within meaningful aggregations. The profile of a mid-level research collection 

would look quite different from the current digital research library. It would not prioritize

the top tier of scholarly journals, the major indexes, a large general set of reference 

materials, or disciplinary canons. Instead, it would provide access to constellations of 

high-quality thematic research collections that are aligned with the scholarly activities 

conducted at the institution. 

Scholar-created research collections are likely to increase in number as the work of 

producing them becomes more widely accepted as legitimate scholarship. Research 

libraries have yet to grasp how this will impact their practices, and it may be some time 

before there is a confluence of scholar- and institution-generated collections. First there 

will need to be a wider awareness of thematic collections as an important mode of 

scholarly work. Scholars and scientists are producing an abundance of digital products, 

many of which are important, high-quality compilations, and these activities are 

proliferating through support from funding agencies. It will be necessary for research 

libraries to respond to this trend in their collection development programs. Just as 

importantly, as collection building grows as a form of scholarly production, universities 

will need to provide resources to assist in this form of research. At present, the materials 

and expertise required for collection building research tend to be thinly scattered across 

departments, libraries, and computing centers. Resources and support services would be 

best centralized in the library or in auxiliary research units where scholars from all fields 

can turn for assistance in developing content and tools. 

Conclusion 

As scholars gain mastery in digital collocation and produce innovative research 

environments, they are practicing a new kind of collection development. Thematic 

collections are conceived not only as support for scholarship but as contributions to 

scholarship. They provide configurations of research materials that strongly represent the 

relationships between different kinds of sources and different subject areas. Through 

contextual mass, interdisciplinary platform, and activity support, thematic collections add 

density, flexibility, and interactivity to previously scattered and static repositories of 

content. They assist in the production of new research, but they also have the potential to 

substantively improve the scholarly research process. 

In thematic collections, research materials are closely tied to the processes of inquiry, 

making the contours of scholarship more visible as they are inscribed into the collection. 

The questions and methods that propel scholarship become part of the representation, and 

as scholars build the partnerships it takes to construct quality collections, the networks of 

researchers and institutions involved in a research area become more explicit. Thematic 

collections are a substantive contribution to the rebuilding of research resources in the 

digital age, adding richness to our expansive stores of materials and new opportunities for 

humanities scholarship. 

See also Chapter 36: The Past, Present, and Future of Digital Libraries. 


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25. 

Print Scholarship and Digital Resources 

Claire Warwick 

Whosoever loves not picture, is injurious to truth: and all the wisdom of poetry. Picture is 

the invention of heaven: the most ancient, and most akin to nature. It is itself a silent 

work: and always of one and the same habit: yet it doth so enter, and penetrate the inmost 

affection (being done by an excellent artificer) as sometimes it o'ercomes the power of 

speech and oratory. 

Ben Jonson, Explorata or Discoveries, 11. 1882–90 

Introduction 

In the late 1990s there was a great deal of concern about the death of the book. From 

every corner it was possible to hear quoted Victor Hugo's Archbishop, complaining that 

"ceci, tuera cela" (Nunberg 1996). Articles and books were published on the future of the 

book, which was assumed to be going to be a brief one (Finneran 1996). At the same time 

we were being told that we might no longer need to commute to work, or attend or teach 

at real physical universities, and of course if there were no longer any books, we would 

only need virtual libraries from which to access our electronic documents. Just a few 

years later all this seems to be as misguidedly futuristic as those 1970s newspaper articles 

predicting that by the year 2000 we would all eat protein pills instead of food. It is clear, 

then, that far from being killed off, print scholarship is still very much alive and well, and 

that its relationship to electronic resources is a highly complex one. In this chapter I will 

examine this relationship, and argue that we cannot hope to understand the complexity of 

such a relationship without looking at scholarly practices, and the way that such resources 

are used. This in turn means examining how we use information, either computationally 

or in print, in the wider context of scholarly life. We must consider how we read texts, 

and indeed visual objects, to understand why print remains important, and how it may 

relate to the digital objects and surrogates that are its companions. 

Scenes from Academic Life 

To exemplify some of the changes that are taking place I would like to recreate two 

scenes from my academic life, which are illustrative of some of these larger issues.

Thessaloniki, Greece 

I am in the Byzantine museum, being shown some of the treasures of this northern Greek 

city, which prides itself on having been longest under continuous Byzantine rule. It is full 

of icons, mosaics, marble carvings, and richly painted tombs. My guide asks what I think 

of them, and repeatedly I marvel at their beauty. But this, it appears, is not the point. We 

must, I am told, be able to read these images and icons in the way that their creators 

intended us to. I see a tomb with a painted tree sheltering a rotund bird which looks rather 

like a turkey. In fact this is an olive, signifying eternity and peace, and the bird is a 

peacock, symbol of paradise. This is not simply wallpaper for the dead, but a statement of 

belief. The icons themselves glow with colors, whose richness I wonder at, but again they 

must be read. The gold background symbolizes heaven, and eternity, the red of a cloak is 

for love, the green band around the Madonna's head is for hope. The wrinkles painted on 

her face are to symbolize that beauty comes from within, and is not altered by age. My 

guide begins to test me, what do I see in front of me? I struggle to remember the 

unfamiliar visual alphabet that I am being taught, and realize that reliance on the printed 

word, and a lack of such meaningful images, has deprived me of a visual vocabulary; that 

such iconography is part of another tradition of communication, whose roots, at least in 

this part of Greece, are extremely ancient. They have long co-existed with the culture of 

the printed word, but have not entirely been supplanted by it. 

Portsmouth, England 

I am on an advisory board for the Portsmouth record office. Over a period of several 

decades they have painstakingly produced nine edited volumes of printed work 

cataloguing some of the holdings of their archives. They are arranged thematically, 

concerning dockyards, houses in the old town, legal documents. All are handsome 

hardbacks with an individual design. There is, it seems, still a substantial backlog of 

volumes in preparation, but printing has become so costly that they have decided to 

publish electronically, which is why I am here. We spend time discussing volumes 

awaiting release, and all are relieved to find that the next volume should soon be ready 

after a period of thirty years in preparation. 

There is a certain culture shock on both sides. I am amazed to discover how long the 

process of editing and publication takes. Thirty years is long, but an average of a decade 

seems to be quite usual. I reflect that the papers themselves are historic, and still exist in 

the archive, waiting to be discovered, even if not calendared. But from the world I am 

used to, where technology changes so quickly, it is hard to return to a sense of such 

relative lack of change and urgency. 

A fellow panel member suggests that what had been thought of as several discrete print 

volumes, on large-scale maps, title deeds, city plans, and population data, could easily be 

combined, in an electronic environment, with the help of geographic information systems 

(GIS) technology. I in turn argue that the idea of separate volumes need not be retained in 

an electronic publication. There is no need to draw up a publication schedule as has been 

done with the print volumes. We could publish data on different themes concurrently, in

several releases, when it is ready, so that the digital records will grow at the pace of those 

who are editing, and not have to suffer delays. 

These suggestions are welcomed enthusiastically but the series editor reminds us that 

there are human dimensions to this process. Those editing individual volumes may want 

to see their work identified as a discrete entity, to gain credit from funding authorities or 

promotion boards. These collections would also, it seems, be incomplete without an 

introduction, and that, paradoxically, is usually written last: a major intellectual task and 

perhaps the factor, I speculate privately, which may have delayed publication, since it 

involves the synthesis of a vast range of sources and an attempt to indicate their 

intellectual value to a historian. Would it be possible to publish a release of the data 

without such an introduction, even if temporarily? We explore different possibilities for 

ways that data might appear with different views, to accommodate such problems, and 

the intellectual adjustments on both sides are almost visible. The historians and archivists 

contemplate the electronic unbinding of the volume as a controlling entity: those 

interested in computing are reminded that more traditional elements of the intellectual 

culture we are working in cannot be ignored if the project is to keep the good will of the 

scholars who work on it. 

Tools to Think With 

The two examples above serve as illustration of some of the very complex issues that we 

must contend with when considering the relationship between printed and electronic 

resources. As Jerome McGann argues, we have grown used to books as the primary tools 

to think with in the humanities. We may read one text, but are likely to use a variety of 

other books as tools, as we attempt to interpret texts of all kinds (McGann 2001, ch. 2). In 

the remainder of this chapter I shall demonstrate that what links the two examples that I 

have quoted above with McGann's concerns is the question of how we use materials in 

humanities scholarship. I shall argue that whatever format materials are in, computational 

methods must make us reconsider how we read. Since we are so used to the idea of 

reading as an interpretative strategy we risk taking it for granted, and considering it 

mundane when compared to exciting new computational methods. But, as the example of 

the Macedonian museum shows, reading is a much more broad-ranging process than the 

comprehension of a printed text, and this comprehension itself is a complex process 

which requires more detailed analysis. It is also arguable that the visual elements of the 

graphical user interface (GUI) have also made us rediscover the visual aspects of reading 

and comprehension. Both of these processes must be considered in order to try to 

understand the complex linkage between digital resources and print scholarship, because 

if we assume that reading printed text is a simple process, easily replaced by 

computational methods of interpreting digital resources, we risk underestimating the 

richness and complexity of more traditional research in the humanities. 

When the use of digital resources was first becoming widespread, assumptions were 

made that such resources could and indeed should replace the culture of interpreting 

printed resources by reading them. Enthusiasts championed the use of digital resources, 

and decried those who did not use them as ill-informed or neo-Luddite. During the 1990s

efforts were made to educate academics in the use of digital resources. Universities set up 

learning media units to help with the production of resources, and offered some technical 

support to academics, though at least in the British system this remains inadequate. The 

quality and quantity of digital resources available in the humanities also increased. And 

yet print scholarship is far from dead. Academics in the humanities still insisted on 

reading books, and writing articles, even if they also used or created digital resources. As 

I discovered in Portsmouth, cultural factors within academia are slow to change. The 

authority of print publication is still undoubted. Why, after all, is this collection published 

in book form? Books are not only convenient, but carry weight with promotion 

committees, funding councils, and one's peers. Computational techniques, however, 

continue to improve and academic culture changes, even if slowly. What is not likely to 

change in the complex dynamics between the two media is the fundamentals of how 

humanities academics work, and the way that they understand their material. How, then, 

should we explain the survival of reading printed texts? 

We might begin this process by examining early attempts to effect such a culture change. 

The 1993 Computers and the Humanities (CHUM) was very much in proselytizing mode. 

In the keynote article of a special issue on computers and literary criticism, Olsen argued 

that scholars were being wrong-headed. If only they realized what computers really are 

useful for, he suggested, there would be nothing to stop them using computer 

methodology to produce important and far-reaching literary research. This is followed by 

an interesting collection of articles, making stimulating methodological suggestions. All 

of them proceeded from the assumption that critics ought to use, and what is more, 

should want to use digital resources in their research. Suggestions include the use of 

corpora for studying intertextuality (CHUM 1993, Greco) or cultural and social 

phenomena (CHUM 1993, Olsen); scientific or quantitative methodologies (CHUM 1993, 

Goldfield) such as those from cognitive science (CHUM 1993, Henry, and Spolsky), and 

Artificial Intelligence theory (CHUM 1993, Matsuba). All of these might have proved 

fruitful, but no work to be found in subsequent mainstream journals suggests that any 

literary critics took note of them. 

The reason appears to be that the authors of these papers assumed that a lack of 

knowledge on the part of their more traditional colleagues must be causing their apparent 

conservatism. It appears that they did not countenance the idea that none of these 

suggested methods might be fit for what a critic might want to do. As Fortier argues in 

one of the other articles in the volume, the true core activity of literary study is the study 

of the text itself, not theory, nor anything else. He suggests that: "this is not some 

reactionary perversity on the part of an entire profession, but a recognition that once 

literature studies cease to focus on literature, they become something else: sociology, 

anthropology, history of culture, philosophy speculation, or what have you" (CHUM 

1993, Fortier 1993: 376). In other words these writers are offering useful suggestions 

about what their literary colleagues might do, instead of listening to critics like Fortier 

who are quite clear about what it is they want to do. Users have been introduced to all 

sorts of interesting things that can be done with computer analysis or electronic 

resources, but very few of them have been asked what it is that they do, and want to keep 

doing, which is to study texts by reading them.

As a result, there is a danger that humanities computing enthusiasts may be seen by their 

more traditional colleagues as wild-eyed technocrats who play with computers and digital 

resources because they can. We may be seen as playing with technological toys, while 

our colleagues perform difficult interpretative tasks by reading texts without the aid of 

technology. So if reading is still so highly valued and widely practiced, perhaps in order 

to be taken seriously as scholars, we as humanities computing practitioners should take 

the activity of reading seriously as well. As the example of my Portsmouth meeting 

shows, both sides of the debate will tend to make certain assumptions about scholarly 

practice, and it is only when we all understand and value such assumptions that we can 

make progress together. If reading a text is an activity that is not easily abandoned, even 

after academics know about and actively use digital resources, then it is important for us 

to ask what then reading might be, and what kind of materials are being read. I shall 

consider the second part of the question first, because before we can understand 

analytical methods we need to look at the material under analysis. 

Texts and Tradition 

Norman (1999) argues that we must be aware what computer systems are good for and 

where they fit in with human expertise. He thinks that computers are of most use when 

they complement what humans can do best. He attributes the failure or lack of use of 

some apparently good computer systems to the problem that they replicate what humans 

can do, but do it less efficiently. A clearer understanding of what humanities scholars do 

in their scholarship is therefore important. Computer analysis is particularly good at 

returning quantitative data and has most readily been adopted by scholars in fields where 

this kind of analysis is privileged, such as social and economic history, or linguistics. A 

population dataset or linguistic corpus contains data that is ideal for quantitative analysis, 

and other researchers can also use the same data and analysis technique to test the 

veracity of the results. 

However, despite the pioneering work of Corns (1990) and Burrows (1987), literary text 

can be particularly badly suited to this type of quantitative analysis because of the kind of 

questions asked of the data. As Iser (1989) argues, the literary text does not describe 

objective reality. Literary data in particular are so complex that they are not well suited to 

quantitative study, polar opposites of being or not being, right and wrong, presence or 

absence, but rather the analysis of subtle shades of meaning, of what some people 

perceive and others do not. The complexity is demonstrated in the use of figurative 

language. As Van Peer (1989: 303) argues this is an intrinsic feature of its "literariness." 

Thus we cannot realistically try to reduce complex texts to any sort of objective and nonambiguous 

state for fear of destroying what makes them worth reading and studying. 

Computer analysis cannot "recognize" figurative use of language. However, an electronic 

text might be marked up in such a way that figurative uses of words are distinguished 

from literal uses before any electronic analysis is embarked upon. However, there are two 

fundamental problems with this approach. Firstly, it is unlikely that all readers would 

agree on what is and is not figurative, nor on absolute numbers of usages. As I. A. 

Richards' study (1929) was the first to show, readers may produce entirely different

eadings of the same literary text. Secondly, the activity of performing this kind of 

markup would be so labor-intensive that a critic might just as well read the text in the 

first place. Nor could it be said to be any more accurate than manual analysis, because of 

the uncertain nature of the data. In many ways we are still in the position that Corns 

complained of in 1991 when he remarked that: "Such programmes can produce lists and 

tables like a medium producing ectoplasm, and what those lists and tables mean is often 

as mysterious" (1991: 128). Friendlier user interfaces mean that results are easier to 

interpret, but the point he made is still valid. The program can produce data, but humans 

are still vital in its interpretation (CHUM 1993, Lessard and Benard). 

Furthermore, interpreting the results of the analysis is a particularly complex activity. 

One of the most fundamental ideas in the design of automatic information retrieval 

systems is that the researcher must know what he or she is looking for in advance. This 

means that they can design the system to find this feature and that they know when they 

have found it, and how efficient recall is. However, unlike social scientists or linguists, 

humanities researchers often do not know what they are looking for before they approach 

a text, nor may they be immediately certain why it is significant when they find it. If 

computer systems are best used to find certain features, then this is problematic. They can 

only acquire this knowledge by reading that text, and probably many others. Otherwise, 

they are likely to find it difficult to interpret the results of computer analysis, or indeed to 

know what sort of "questions" to ask of the text in the first place. 

Humanities scholars often do not need to analyze huge amounts of text to find material of 

interest to them. They may not need to prove a hypothesis as conclusively as possible, or 

build up statistical models of the occurrence of features to find them of interest, and may 

find the exceptional use of language or style as significant as general patterns (Stone 

1982). They may therefore see traditional detailed reading of a relatively small amount of 

printed text as a more effective method of analysis. 

The availability of a large variety of text types may be more important than the amount of 

material. Historians require a wide variety of materials such as letters, manuscripts, 

archival records, and secondary historical sources like books and articles (Duff and 

Johnson 2002). All of these are to be found in print or manuscript sources, some of which 

are rare and delicate and often found only in specific archives or libraries. Of course 

some materials like these have been digitized, but only a very small proportion. Even 

with the largest and most enthusiastic programs of digitization, given constraints of time, 

the limited budgets of libraries, and even research councils, it seems likely that this will 

remain the case for the foreseeable future. The historical researcher may also be looking 

for an unusual document whose value may not be apparent to others: the kind of 

document which may be ignored in selective digitization strategies. 

Indeed the experience of projects like Portsmouth records society suggests that this has 

already been recognized. If the actual material is so unique that a historian may need to 

see the actual artifact rather than a digitized surrogate, then we may be better advised to 

digitize catalogues, calendars, and finding aids, and allow users to make use of them to 

access the material itself. This also returns us to the question of the visual aspect of

humanities scholarship. Initially the phenomenon of a digitized surrogate increasing the 

demand for the original artifact seemed paradoxical to librarians and archivists. However, 

this acknowledges the need for us to appreciate the visual aspects of the interpretation of 

humanities material. A transcribed manuscript which has been digitized allows us to 

access the information contained in it. It may only be as a result of having seen a digital 

image that the scholar realizes what further potential for interpretation exists, and this, it 

appears, may only be satisfied by the artifact itself. This is such a complex process that 

we do not yet fully understand its significance. 

The nature of the resources that humanities scholars use should begin to explain why 

there will continue to be a complex interaction between print and digital resources. There 

is not always a good fit between the needs of a humanities scholar or the tasks that they 

might want to carry out, and the digital resources available. Print still fulfills many 

functions and this perhaps encourages scholars to produce more, by publishing their 

research in printed form. But surely we might argue that computational methods would 

allow more powerful and subtle ways of analyzing such material. Reading, after all, 

seems such a simple task. 

What is Reading? 

Whatever assumptions we might be tempted to make, the activity of reading even the 

simplest text is a highly complex cognitive task, involving what Crowder and Wagner 

(1992: 4) describe as "three stupendous achievements": the development of spoken 

language, written language, and literacy. Kneepkins and Zwaan (1994: 126) show that: 

In processing text, readers perform several basic operations. For example, they decode 

letters, assign meaning to words, parse the syntactic structure of the sentence, relate 

different words and sentences, construct a theme for the text and may infer the objectives 

of the author. Readers attempt to construct a coherent mental representation of the text. In 

this process they use their linguistic knowledge (knowledge of words, grammar), and 

their world knowledge (knowledge of what is possible in reality, cultural knowledge, 

knowledge of the theme). 

These processes are necessary for even the most basic of texts, and therefore the 

cognitive effort necessary to process a complex text, such as the material commonly used 

by humanities researchers, must be correspondingly greater. Arguably, the most complex 

of all such texts are literary ones, and thus the following section is largely concerned with 

such material. Although empirical studies of the reading of literary text are in their 

comparative infancy (de Beaugrande 1992) research has ensured that the reading process 

is thought of as not simply a matter of recall of knowledge, but as a "complex cognitive 

and affective transaction involving text-based, reader-based, and situational factors" 

(Goetz et al. 1993: 35).

Text-based factors 

Most humanities scholars would agree that their primary task is to determine how 

meaning can be attributed to texts (Dixon et al. 1993). Yet the connection between 

meaning and language is an extremely complex problem. As Snelgrove (1990) 

concluded, when we read a literary text we understand it not only in terms of the meaning 

of specific linguistic features, but also by the creation of large-scale patterns and 

structures based on the interrelation of words and ideas to one another. This pattern 

making means that the relative meaning invested in a word may depend on its position in 

a text and the reaction that it may already have evoked in the reader. Dramatic irony, for 

example, is effective because we know that a character's speech is loaded with a 

significance they do not recognize. Our recognition of this will depend on the mental 

patterns and echoes it evokes. Language may become loaded and suffused with meaning 

specifically by relations, or its use in certain contexts. 

The complexity of the patterns generated by human readers is, however, difficult to 

replicate when computational analysis is used. Text analysis software tends to remove the 

particular phenomenon under investigation from its immediate context except for the few 

words immediately surrounding it. A linguist may collect instances of a particular 

phenomenon and present the results of a concordance sorted alphabetically, irrespective 

of the order in which the words originally occurred in the text, or the author of them. 

However, for a literary critic the patterns created are vital to the experience of reading the 

text, and to the way it becomes meaningful. Thus the fragmented presentation of a 

computer analysis program cannot begin to approach the kind of understanding of 

meaning that we gain by reading a word as part of a narrative structure. 

The way we read literary text also depends on the genre of the work. Comprehension 

depends on what we already assume about the type of text we recognize it to be. Fish 

(1980: 326) found that he could persuade students that a list of names was a poem 

because of their assumptions about the form that poems usually take. Hanauer (1998) has 

found that genre affects the way readers comprehend and recall text, since they are likely 

to read more slowly and remember different types of information in a poem. Readers also 

decode terms and anaphora differently depending on what we know to be the genre of a 

text; for example, we will expect "cabinet" to mean one thing in a newspaper report of a 

political speech, and another in a carpentry magazine (Zwaan 1993: 2). 

Reader-based factors 

The way that we extract meaning from a text also depends on many things which are 

extrinsic to it. The meaning of language changes depending on the associations an 

individual reader may make with other features of the text (Miall 1992), and with other 

texts and ideas. The creation of such webs of association and causation is central to the 

historian's craft. As the eminent historian G. R. Elton put it:

G. M. Young once offered celebrated advice: read in a period until you hear its people 

speak.… The truth is that one must read them, study their creations and think about them 

until one knows what they are going to say next. 

(Elton 1967: 30) 

Interaction between the reader and the text is also affected by factors which are particular 

to the individual reader (Halasz 1992). Iser (1989) argues that narrative texts invite the 

interaction of the reader by indeterminacy in the narrative. Where there are gaps of 

uncertainty, the reader fills them using their own experience. Readers' experience of a 

fictional text will even be affected by the relationship which they build between 

themselves and the narrator (Dixon and Bertolussi 1996a). 

Situational factors 

The reader's response to a text is likely to be affected by situational factors, for example 

their gender, race, education, social class, and so on. This is also liable to change over 

time, so that we may experience a text differently at the age of 56 than at 16. As Potter 

(1991) argues, these factors have yet to be taken into account in empirical readership 

studies of literary text. Even more subtly than this, however, a reader's appreciation of a 

text may be affected by how they feel on a particular day, or if a feature of the text is 

reminiscent of their personal experience. Happy readers notice and recall parts of a text 

which describe happiness, or evoke the emotion in them, and sad ones the opposite 

(Kneepkins and Zwaan 1994: 128). The role of emotional engagement is clearly vital in 

literary reading. Yet it is one which is very difficult to quantify, or describe, and therefore 

is almost impossible for computer analysis to simulate. 

Reading a text is also affected by the frequency of reading and the expertise of the reader, 

as Elton's observation suggests (Dixon and Bertolussi 1996b; Dorfman 1996). Dixon and 

colleagues (1993) found that the same textual feature might be the cause of varied effects 

in different readers and certain effects were only apparent to some readers. Although core 

effects of the text could usually be discerned on first reading, other, more subtle effects 

were only reported on second or subsequent readings, or would only be apparent to some 

of the readers. They also found that the subtlest of literary effects tended to be noticed by 

readers who they called "experienced." They concluded that reading is such a complex 

procedure that all the effects of the text are unlikely to be apparent at once, and that 

reading is clearly a skill that needs to be learnt and practiced. 

Reading the Visual 

It should therefore be becoming clear why print resources have continued to co-exist with 

digital ones. The key activity of the humanities scholar is to read and interpret texts, and 

there is little point in using a computational tool to replicate what human agency does 

best in a much less complex and subtle manner. Reading a printed text is clearly a subtle 

and complex analysis technique. It is therefore not surprising that scholars have made the 

assumption that digital resources and computational techniques that simply replicate the

activity of reading are a pale imitation of an already successful technique. To be of use to 

the humanities scholar, it seems that digital resources must therefore provide a different 

dimension that may change the way that we view our raw materials. 

In some ways we can discern a similar movement in humanities computing to that which 

has taken place in computer science. In the 1980s and 1990s Artificial Intelligence 

seemed to offer the prospect of thinking machines (Jonscher 1999, ch. 5). But the 

technology that has captured the imagination of users has not been a computer system 

that seeks to think for them, but one that provides access to material that can provide raw 

material for human thought processes, that is, the Internet and World Wide Web. The 

popularity of the Web appears to have dated from the development of graphical browsers 

that gave us access not only to textual information, but to images. The effect of this and 

the rise of the graphical user interface has been to re-acquaint us with the power of 

images, not only as ways of organizing information, but as way of communicating it. Just 

as the images in the museum in Thessaloniki reminded me that there are other ways to 

interpret and communicate ideas, so we have had to relearn ways to read an image, 

whether the frame contains a painted or a pixelated icon. 

It is in this area that, I would argue, digital resources can make the greatest contribution 

to humanities scholarship. Digitization projects have revolutionized our access to 

resources such as images of manuscripts (Unsworth 2002). The use of three-dimensional 

CAD modeling has been extensively used in archaeology to help reconstruct the way that 

buildings might have looked (Sheffield University 2003). However, the projects that are 

most innovative are those that use digital resources not for reconstruction or improved 

access, though these are of course enormously valuable, but as tools to think with. If the 

process of reading and interpreting a text is so complex, then it may be that this is best 

left to our brains as processing devices for at least the medium term. It is, however, in the 

realm of the visual that we are seeing some of the most interesting interrelationships of 

print scholarship and digital resources. We need only look at a small sample of some of 

the papers presented at the Association for Literary and Linguistic Computing- 

Association for Computers and the Humanities conference (at ) 

in 2002 to see exciting examples of such 

developments in progress. 

Steve Ramsay argues that we might "remap, reenvision, and re-form" a literary text 

(Ramsay 2002). He refers to McGann and Drucker's experiments with deforming the way 

a text is written on a page (McGann 2001), but has moved beyond this to the use of 

Graph View Software. He has used this program, which was originally designed to create 

graphic representations of numerical data, to help in the analysis of dramatic texts. In a 

previous interview, he had demonstrated this to me, showing a three-dimensional 

graphical mapping of Shakespeare's Antony and Cleopatra. This created wonderful 

abstract looping patterns which might have been at home in a gallery of modern art. But, 

like the Macedonian icons, theses were not simply objects of beauty. Once interpreted, 

they show the way that characters move though the play, being drawn inexorably towards 

Rome. This visual representation had the effect, not of abolishing the human agency of 

the literary critic, but providing, literally, a new vision of the play, perhaps opening up

new vistas to the critical view. The significance of such movement, and what it reveals 

about the play, is for the critic herself to decide, but the program has performed a useful 

form of defamiliarization, which would be difficult to imagine in a print environment. 

We can also see similar types of visual representation of textual information in the 

interactive 3-D model of Dante's Inferno. The effect of this is a similar kind of 

defamiliarization. A very new view of the information in the text is created, but the effect 

of it, at least on this reader, is to make her wish to return to the text itself in printed form, 

and to read it with new eyes. The digital resource has therefore not made reading 

redundant, but helped to suggest new avenues of interpretation. This project is being 

developed at the same research centre, IATH, where McGann is and Ramsay was based. 

This is another intriguing connection between the world of digital resources and more 

traditional forms of scholarship. Computational tools as simple as e-mail have made the 

process of scholarly collaboration over large physical distances much easier than before. 

Yet it is fascinating to note that the physical proximity of scholars such as those at IATH 

facilitates the interchange of ideas and makes it possible for methodologies to be shared 

and for projects and scholars to be a creative influence on each other – a process that we 

can see at work in the visual dynamics of these IATH projects. This is not an isolated 

phenomenon. The fact that Microsoft's campus in Cambridge shares alternate floors of a 

building with the university department of computer science shows that such a 

technologically advanced organization still values informal creative exchanges as a way 

to inspire new projects and support existing ones. The Cambridge University Computer 

Laboratory's online coffee machine was a star turn of the early Web (Stafford-Fraser 

1995). But it was finally switched off in 2001, perhaps proof that Microsoft's decision to 

privilege meetings over a non-virtual coffee shows their recognition that traditional 

methods are still vital in innovation and scholarship. 

Two other IATH projects were also discussed at the conference. The Salem witch trials 

and Boston's Back Bay Fens are two projects which both make use of GIS technology to 

integrate textual material and numerical data with spatial information (Pitti et al. 2002). 

Once again these projects allow the user to visualize the data in different ways. 

Connections might be made between people or places in historical Boston, whose 

physical proximity is much easier for a user to establish in the visual environment of a 

GUI interface than by the examination of printed data. Where a particular user's 

knowledge might be partial, the use of such data lets her literally envision new ones, as a 

result of interrogating a large spatial dataset. A new textual narrative can emerge from the 

physical linkages. 

The case of the Salem witch trials is also intriguing, since the Flash animations actually 

allow the user to watch as the accusations spread over time and space like a medical, 

rather than psychological, epidemic. This idea of mapping this spread is, however, not 

new in terms of historiography. In 1974 Boyer and Nissenbaum had pioneered this 

approach in a ground-breaking book, Salem Possessed. This contains printed maps of the 

area which give us snapshots of the progress of the allegations. We can, therefore, see an 

immediate relationship between scholarship in print and a digital resource which has 

grown out of such theories. What the digital resource adds, though, is the immediacy of

eing able to watch, run and rerun the sequence in a way that a book, while impressive, 

cannot allow us to do. Once again, the theories that lie behind the data are, as Pitti et al. 

(2002) make clear, very much the product of the scholarship that the historians who 

initiated the projects brought to them. Analysis performed on the data will also be done in 

the minds of other scholars. But the visual impact of both of these databases supports 

human information processing, and may suggest new directions for human analysis to 

take. 

As the website for the Valley of the Shadow, one of the two original IATH projects, puts 

it, GIS may be used literally "to put [historical] people back into their houses and 

businesses" (Ayers et al. 2001). Valley of the Shadow is itself doing far more than simply 

this. The project itself seems to be predicated on an understanding of the visual. Even the 

basic navigation of the site is organized around the metaphor of a physical archive, where 

a user navigates the different materials by visiting, or clicking on, the separate rooms of a 

plan of the space. By linking a contemporary map with the data about a particular area, 

GIS allows users to interpret the data in a new way, giving a concrete meaning to 

statistical data or names of people and places in the registers which are also reproduced 

(Thomas 2000). Just as with a literary text, this might cause a historian to return to the 

numerical or textual data for further analysis, and some of this might use computational 

tools to aid the process. 

But its greatest value is in prompting a fresh approach for consideration. The historian's 

brain is still the tool that determines the significance of the findings. This distinction is 

important, since it distinguishes Valley of the Shadow from some earlier computational 

projects in similar areas of American history. For example, in 1974 Fogle and Engerman 

wrote Time on the Cross, in which they sought to explain American slavery with the use 

of vast amounts of quantitative data on plantation slavery, which was then analyzed 

computationally. This, they claimed, would produce a definitive record of the objective 

reality of slavery in America. Critics of the book have argued persuasively that the data 

were handled much too uncritically. Their findings, it has been claimed, were biased, 

because they only used statistical written data, which usually emerged from large 

plantations, and ignored the situation of small slave holders who either did not or could 

not document details of their holdings, either because the holdings were small or because 

the slave holder was illiterate (Ransom and Sutch 1977; Wright 1978). Other historians 

have insisted that anecdotal sources and printed texts must be used to complement the 

findings, to do sufficient justice to the complexity of the area. It could be argued that the 

problems that they encountered were caused by an over-reliance on computational 

analysis of numerical data, and by the implication that this could somehow deliver a 

definitive explanation of slavery in a way that would finally put an end to controversies 

caused by subjective human analysis. A project such as Valley of the Shadow is a 

significant progression onwards, not only in computational techniques but also in 

scholarly method. It does not rely on one style of data, since it links numerical records to 

textual and spatial data. These resources are then offered as tools to aid interpretation, 

which takes place in the historian's brain, rather than in any way seeking to supersede 

this.

The products of the projects are also varied, ranging from students' projects, which take 

the materials and use them to create smaller digital projects for assessment, to more 

traditional articles and conference presentations. Most intriguing, perhaps, is the form of 

hybrid scholarship that Ed Ayers, the project's founder, and William Thomas have 

produced. Ayers had long felt that despite the innovative research that could be 

performed with a digital resource, there had been very little effect on the nature of 

resulting publication. The written article, even if produced in an electronic journal, was 

still essentially untouched by the digital medium, having the same structure as an article 

in a traditional printed journal. Ayers and Thomas (2002) therefore wrote an article which 

takes advantage of the electronic medium, by incorporation some of the GIS data, and the 

hypertextual navigation system which gives a reader multiple points of entry and of 

linkage with other parts of the resource. Readers are given a choice of navigation, a 

visual interface with Flash animations or a more traditional text-based interface. The 

article might even be printed out, but it is difficult to see how it could be fully appreciated 

without the use of its digital interactive elements. This has appeared in American 

Historical Review, a traditional academic journal, announcing its right to be considered 

part of the mainstream of historical research. As such it represents a dialogue between the 

more traditional world of the academic journal and the possibilities presented by digital 

resources, at once maintaining the continuity of scholarly traditions in history, but also 

seeking to push the boundaries of what is considered to be a scholarly publication. The 

analysis presented in the paper emerges from human reading and processing of data, but 

would not have been possible without the use of the digital resource. 

It is not only at IATH, however, that work in this area is taking place, despite their 

leading position in the field. Thomas Corns et al. (2002), from the University of Bangor 

in Wales, has described how one aspect of document visualization can aid human 

analysis. Being able to digitize a rare manuscript has significantly aided his team in 

trying to determine whether it was written by Milton. The simple task of being able to 

cut, paste, and manipulate letter shapes in the digitized text has helped in their 

examination of scribal hands. The judgment is that of the scholars, but based on the 

ability to see a text in a new way, only afforded by digitized resources. This is not a new 

technique, and its success is largely dependent on the questions that are being asked of 

the data by the human investigators. Donaldson (1997) discusses ways in which complex 

analysis of digital images of seventeenth-century type was used to try to decide whether 

Shakespeare had used the word wife or wise in a couplet from The Tempest, usually 

rendered as "So rare a wondered father and a wise / Makes this place paradise" (Act IV, 

Scene, I, 11. 122–3). The digital research proved inconclusive but might have been 

unnecessary, since a Shakespeare scholar might be expected to deduce that the rhyme of 

wise and paradise is much more likely in the context of the end of a character's speech, 

than the word wife, which while tempting for a feminist analysis would not follow the 

expected pattern of sound. All of which indicates that the use of digital resources can 

only be truly meaningful when combined with old-fashioned critical judgment. 

Another project being presented at ALLC-ACH, which is very much concerned with 

facilitating critical judgment through the realm of the visual, is the Versioning Machine 

(Smith 2002). This package, which supports the organization and analysis of text with

multiple variants, is once again a way of helping the user to envision a text in a different 

way, or even in multiple different ways. The ability to display multiple variants 

concurrently, to color-code comments that are read or unread, selectively to show or hide 

markup pertaining to certain witnesses, gives scholars a different way of perceiving the 

text, both in terms of sight and of facilitating the critical process. It is also far less 

restrictive than a printed book in the case where the text of a writer might have multiple 

variants, none of which the critic can say with certainly is the final version. The case of 

Emily Dickinson is a notable one, presented by the MITH team, but it may be that if 

freed by digital resources like the Versioning Machine of the necessity of having to 

decide on a copy text for an edition, the text of many other writers might be seen as much 

more mutable, and less fixed in a final form of texuality. 

Print editions, for example of the seventeenth-century poet Richard Crashaw, have forced 

editors to make difficult decisions about whether the author himself made revisions to 

many of the poems. When, as in this case, evidence is contradictory or inconclusive, it is 

surely better to be able to use digital technology such as the Versioning Machine to give 

users a different way of seeing, and enable them to view the variants without editorial 

intervention. The use of the Versioning Machine will not stop the arguments about which 

version might be preferred, based as they are on literary judgment and the interpretation 

of historical events, but at least we as readers are not presented with the spurious 

certainty that a print edition forces us into. Once again, therefore, such use of computer 

technology is not intended to provide a substitute for critical analysis, and the vast 

processing power of the human brain, rather it gives us a way of reviewing the evidence 

of authorial revisions. It makes concrete and real again the metaphors that those terms 

have lost in the world of print scholarship. 

Conclusion 

Even when we look at a small sample of what is taking place in the field, it is clear that 

some of the most exciting new developments in the humanities computing area seem to 

be looking towards the visual as a way of helping us to reinterpret the textual. It appears 

that we are moving beyond not printed books and print-based scholarship, but the naive 

belief that they can easily be replaced by digital resources. 

As the example of my visit to Portsmouth demonstrated, it is simplistic to believe that we 

can, or should, rush to convince our more traditional colleagues of the inherent value of 

digital resources, without taking into account the culture of long-established print 

scholarship. It is only through negotiations with scholars, and in forging links between 

the digital and the textual traditions that the most interesting research work is likely to 

emerge. 

The materials that humanities scholars use in their work are complex, with shifting 

shades of meaning that are not easily interpreted. We are only beginning to understand 

the subtle and complicated processes of interpretation that these require. However, when 

we consider that the process of reading a text, which may seem so simple, is in fact so 

difficult an operation that computer analysis cannot hope to replicate it at present, we can

egin to understand why for many scholars the reading of such material in print will 

continue to form the core activity of their research. 

Digital resources can, however, make an important contribution to this activity. Far from 

attempting to replace the scholar's mind as the processing device, computer delivery of 

resources can help to support the process. The complexity of visual devices as a way of 

enshrining memory and communicating knowledge is something that the ancient world 

understood very well, as I learnt when I began to read the icons in Thessaloniki. While 

much of this knowledge has been lost in the textual obsession of print culture, the 

graphical interface of the computer screen has helped us reconnect to the world of the 

visual and recognize that we can relearn a long-neglected vocabulary of interpretation. 

Digital resources can provide us with a new way to see, and thus to perceive the 

complexities in the process of interpreting humanities materials. A new way of looking at 

a text can lead to a way of reading it that is unconstrained by the bindings of the printed 

medium, even if it leads us back to the pages of a printed book. 


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26. 

Digital Media and the Analysis of Film 

Robert Kolker 

This history of film studies is a short one, dating from the early to mid-1960s, and 

evolving from a number of historical events. One was the appearance of the films of the 

French New Wave – Jean-Luc Godard, Francois Truffaut, Claude Chabrol, Eric Rohmer, 

as well as older figures, such as Alain Resnais. These directors, along with Sweden's 

Ingmar Bergman and Italy's Federico Fellini and Michelangelo Antonioni, among others, 

demonstrated that the formal, thematic, and economic givens of American Cinema were 

not that at all. Film had a flexible language that could be explored, opened, rethought. 

They proved that the conventions of Hollywood story telling could be pulled and 

stretched, stood on their head. These films, in short, made many people aware that 

cinema was a serious form of expression, an art form involved in the production of 

thought. 

The wonderful paradox here was that many of these European directors learned from 

watching American film. Not knowing English, they read the visual structure of the films 

they saw and discovered a visual energy mostly missed by plot-centric American 

filmgoers and film reviewers. They embraced the American style and countered it 

simultaneously, all the while refusing the pressures of a studio system that, in America, 

saw films as commodities. And, as the writing and the filmmaking of the French New 

Wave reached Britain and then the USA, there was an interesting flow-back effect, 

turning the eyes of burgeoning American film scholars toward their own cinema, largely 

reviled, mostly forgotten. 

In their discovery of American film, the French – concentrating on the visual aspects of 

what they saw, and understanding that film was essentially about the construction of 

images – noticed that when the structures of formal and thematic element cohered, it was 

around the film's director – no matter what other personnel were involved, including the 

screenwriter. As these insights were passed overseas, American film suddenly took on a 

patina of seriousness that only authorship could give it. This, coupled with the excitement 

of the new cinema coming from overseas, and the call by students for a broader college 

curriculum, led to film courses being offered by English professors (like myself), and in 

history and art history departments. Film Studies departments came together more 

slowly. Publishing scholarly film articles and books grew apace. 

What was taught and studied in the early days? The auteur theory, in which the film 

director is seen as the formative creative consciousness of a film, turned out to be in 

practice not mere idolatry, but a means of analysis. If one could identify a filmmaker by

certain stylistic and thematic traits, these could to be understood and analyzed. Or, taking 

a Foucauldian turn, the auteur could be constructed from a group of films, discovered as 

auteurs through their work, the analysis of which yields ways of cinematic seeing that 

were recognizable from film to film. 

By the late 1960s and into the 1970s and 1980s, the fundamentals of auteuristn were 

enriched by a number of other theoretical and historical practices. In fact, film studies 

was among the earliest disciplines to apply feminist and gender theory. Laura Mulvey's 

theory of the formal structures of the gendered gaze in her essay "Visual Pleasure and the 

Narrative Cinema", published in 1975, remains a touchstone not only for film studies, but 

for art and literary analysis as well. Ideological criticism and cultural analysis, Lacanian 

psychoanalytic theory, structuralism, postmodern critique – indeed a variety of theoretical 

permutations – have built film (and, finally, television) studies into a rich and productive 

discipline. 

Throughout this period of growth (if not expansion), one problem remained in both the 

teaching and writing of film: the ability to prove and demonstrate by means of quotation. 

In other words, the literary scholar, the historian, the art historian can literally bring the 

work under study into her own text to prove a point, illustrate an argument, and provide 

the text and context for analysis. The film scholar cannot. 

In teaching, the ability to analyze sequences in front of a class was better served, going 

through a long phase of never quite adequate development. In the beginning, there were 

only 16 mm copies of films to show and analyze. The equivalent of Xerox copies, 16 mm 

film was terrible for general viewing, and selecting a sequence to study involved putting 

pieces of paper in the film reel as it ran and then winding it back to find the passage. One 

could also put the reel on manual rewinds and use a small viewer to find the passage, and 

then remount the whole thing back on the projector. At one point, the department I was 

working in purchased an "analyzer" projector – the kind that was used by football teams 

before videotape. One still needed to find the passage in the reel, but, theoretically, one 

could freeze frames, roll forward or backward, or even step-frame through the sequence. 

In reality, the machine usually spat out sprocket holes and tore up the film. Videotape 

was no better in image quality – worse, in fact, because the manufacturers of video 

believed audiences would not put up with the black bars on the top and bottom of the 

frame for wide-screen and anamorphic films, and so blew up the image to make it fit the 

familiar, almost square rectangle, thereby losing about two-thirds of it in a process nicely 

named "pan and scan." 

Laserdisk and now DVD came close to ameliorating the situation. The image resolution 

is greatly improved. Good video projection creates a sharp, color correct or accurate 

greyscale image in the proper screen ratio. Accessibility to the precise place in the film to 

which we want to aim our students' attention, or have them find the sequence they wish to 

discuss, is easy. We can now "quote" the passages of the film when teaching them. 

But this did not solve the quotation problem for research. The presentation and analysis 

of cinematic works, no matter what methodology was used, could not be given the proof

of the thing itself, the moving image upon which the analysis was based. We could only 

describe what we saw as a means of bolstering and driving our arguments, and, for 

visuals (often on the insistence of publishers), provide studio stills, which are, more often 

than not, publicity stills rather than frame enlargements from the film itself. To be sure, 

there were some painstaking researchers who, with special lenses and the permission of 

archives, made (and still make) their own frame enlargements directly from a 35 mm 

print in order to get exactly the image they need. But the images were still, and we were 

all writing and talking about the images in motion. 

The situation began to change in the early 1990s. The first indication of a new way to do 

film studies occurred at a meeting of the Society for Cinema Studies in 1989, when 

Stephen Mamber of UCLA hooked up a computer, a laserdisk player, and a television 

monitor and controlled the images of the film on the laserdisk through the computer with 

a program he had written. This was followed the next year by a colloquium that Mamber 

and Bob Rosen set up at UCLA, funded by a MacArthur Grant, that advanced Mamber's 

work considerably. He and Rosen had created a database of every shot in Citizen Kane, 

Welles's Macbeth, and a film from China, Girl from Hunan (1986). They could access the 

shots from the computer, through a multimedia program called Toolbook – an excellent 

multimedia package that still exists and is among the best to do the kind of work I'm 

describing. 

The coup came shortly after this, when a colleague at the University of Maryland 

demonstrated image overlay hardware that put a moving image from a tape or disk 

directly into a window on the computer screen. Quickly following this came inexpensive 

video capture hardware and software that allows the capture of small or long sequences 

from a videotape or DVD. The captured and compressed images (the initially captured 

image creates a huge file which must be compressed with software called "codecs" in 

order to make them usable) are like any computer file. They can be manipulated, edited, 

titled, animated. The film scholar and burgeoning computer user finally discovered that a 

fundamental problem faced by the discipline of film studies could now be solved. The 

visual text that eluded us in our scholarly work and even our teaching was now at hand. 

We could tell, explain, theorize, and demonstrate! 

But other problems immediately surfaced. One was obvious: how would this work be 

transmitted? On first thought, the web seemed the best and obvious means of distribution 

for this new kind of film analysis. It wasn't then; it isn't now. To analyze the moving 

image, it has to appear promptly, smoothly, with as near perfect resolution as possible, 

and at a size no smaller than 320 × 240 pixels or larger (to make things confusing, these 

numbers refer to image resolution, but translate on the screen as image size). This is 

impossible on the Web. Even today's streaming video and high-powered computers 

cannot offer the image size, resolution, and smoothness of playback that are required, 

despite the fact that a few successful Web projects have emerged, mostly using very 

small images to minimize download time. Until Internet 2 becomes widely available, CD 

and DVD are the only suitable media for transmission of moving images. (Recordable 

DVD is still in its standardizing phase, with a number of formats competing with each

other.) There is something on the horizon that will allow the Web to control a DVD in the 

user's machine, and we will get to that in a bit. 

The other problem involves the programming necessary to create a project that combines 

text and moving image. There are relatively simple solutions: moving image clips can be 

easily embedded into a Power Point presentation, for example. A Word document will 

also accept embedded moving images – although, of course, such an essay would have to 

be read on a computer. HTML can be used relatively easily to build an offline Web 

project which will permit the size and speed of moving images necessary. But more 

elaborate presentations that will figuratively or literally open up the image and allow the 

user to interact with it, that will break up the image into analyzable chunks, or permit 

operations on the part of the reader to, for example, re-edit a sequence – all require some 

programming skills. This is probably not the place to address the pains and pleasures of 

programming. There is no getting around the fact that one will need to know some basics 

of programming (and the varieties of online users' groups, who will answer questions); 

but ultimately, programming is only half the problem. The other, perhaps most important, 

is designing the project, creating an interface, making the screen inviting, as easy or as 

complex to address as the creator of it wishes it to be. And, preceding the interface, there 

must lie a well-thought out concept of what we want a user to do, to discover, to learn. In 

other words, in addition to the analytic and theoretical skills of a film scholar, there are 

added design and usability skills as well. 

There are a variety of tools available to execute design and usability, each one with its 

particular strengths and weaknesses. Macromedia Director is good for animations to 

accompany the exposition, but it requires a great deal of programming in a non-intuitive 

environment. Toolbook is an excellent, Windows-only program. Its scripting language is 

fairly simple (or fairly complex, depending upon your need) and much of it in plain 

English. Visual Basic is a powerful tool, requiring a great deal of programming 

knowledge. The solution for all of this, of course, is for the film scholar to work closely 

with a student with programming skills, so that the scholar becomes essentially a concept 

and content provider. On the other hand, it is very satisfying to learn the necessary 

program or multimedia package. More than satisfying, by working out the programming, 

one learns how to structure and form ideas by and for the computer – in effect 

understanding the grammar that will express what you want the viewer to see. By 

understanding the form and structure of computational possibilities, you can generate the 

design, interactivity, the images and analyses that integrate concept, execution, and 

reception. 

Many scholars have experimented with various modes of computer representations since 

the 1990s. I've indicated that the work of Stephen Mamber was responsible for getting me 

and others started in using the computer to analyze films. Mamber went on to do what 

still remains the most exciting and complex computer-driven work of cinematic analysis, 

his "Digital Hitchcock" project on Hitchcock's The Birds. The project started in part when 

the Academy of Motion Picture Arts and Sciences allowed him access to the script, 

storyboards, and other material related to the film. Programming from scratch, Mamber 

created a stunning presentation: beginning with the representation of the first frame of

every shot of the film, all of which he managed to put on one screen. In other words, the 

whole film is represented by a still of each of its shots and each still is addressable. When 

clicked, they bring up the entire shot. 

Mamber compares Hitchcock's storyboard illustrations side by side with moving clips 

from the finished screen, to demonstrate how closely Hitchcock hewed to his original 

conceptions. (Hitchcock was fond of saying that, for him, the most boring part of making 

a film was actually making the film, because he had completed it before it went to the 

studio floor.) Mamber's comparison of sketches with shots proves Hitchcock right and 

wrong, because it indicates where he deviates from the sketches to achieve his greatest 

effects. The program allows the clip to be played while the successive storyboard 

sketches appear next to the sequence. 

In this project, Mamber began what has become some of his most exciting work, the 

creation of 3-D mock-ups of filmic spaces. Working on the theory of "imaginary spaces", 

Mamber shows how the images we look at in a film could not possibly exist. They are 

almost the cinematic equivalents of trompe I'oeil, using the two-dimensional surface of 

the screen to represent a fictional space that the filmmaker deems necessary for us to see, 

without regard to the impossible spaces he or she is creating. By making the entire space 

visible by imagining a three-dimensional simulacrum of it (in effect creating a 

simulacrum of a simulacrum), Mamber exposes not only the fictional space, but the 

processes of representation itself. His spatial representations provide an analytical 

understanding of the continuity of movement through space that filmmakers are so keen 

on maintaining, despite the optical trickery used to create them. His work becomes, in 

effect, an exposure of the ideology of the visible. He creates an alternative world of the 

alternative world that the film itself creates. 

Mamber clarifies a film sequence through 3-D rendering, showing how it is constructed 

for our perception. He has made, for example, a painstaking animated reconstruction of 

the opening of Max Ophiils's The Earrings of Madame De…, showing the intricate 

movements of the camera – by putting the camera in the animation – during an amazingly 

long shot. He has done a still rendering of the racetrack and a flythrough of the betting 

hall in Kubrick's The Killing in order to show the spatial analogues of the complex time 

scheme of the film. The line of his investigation is of enormous potential for all narrative 

art because he is essentially discovering ways of visualizing narrative. Film is, obviously, 

the perfect place to start, because its narrative is visual and depends upon spatial 

relationships, as well as the temporal additions of editing. But literary narrative also 

depends upon the building of imaginary spaces, and the kind of visualizations Mamber is 

doing on film could go far toward a mapping of the spaces of the story we are asked by 

fiction to imagine. 1 

Other pioneering work in the field of computer-driven film studies includes Marsha 

Kinder's 1994 companion CD-ROM to her book on Spanish film, Blood Cinema. Here, 

film clips and narration elaborate the elements of Spanish cinema since the 1950s. Lauren 

Rabinowitz's 1995 CD-ROM, The Rebecca Project, is a wonderful example of how CD- 

ROMs are able to combine images, clips, critical essays, and other documents to drill as

deeply and spread as widely information and analysis of Hitchcock's first American film. 

Robert Kapsis's Multimedia Hitchcock, displayed at MOMA, also brings together a 

variety of information, images, clips, and commentary on Hitchcock's work. Similarly, 

Georgia Tech's Griffith in Context (Ellen Strain, Greg Van Hoosier-Carey, and Patrick 

Ledwell), supported by a National Endowment for the Humanities (NEH) grant, takes 

representative sequences from Birth of a Nation and makes them available to the user in a 

variety of ways. The user can view the clips, attempt a re-editing of them, listen to a 

variety of commentary from film scholars, view documents, and learn about the racial 

and cultural context surrounding Griffith's work. MIT's Virtual Screening Room (Henry 

Jenkins, Ben Singer, Ellen Draper, and Janet Murray), also the recipient of an NEH grant, 

is a huge database of moving images, designed to illustrate various elements of film 

construction. Adrian Miles's "Singin' in the Rain: A Hypertextual Reading", appearing in 

the January, 1998, film issue of Postmodern Culture (at 

, is one 

of the most ambitious projects involving image and critical analysis, and one of the few 

successful web-based projects with moving images. Miles uses small and relatively fastloading 

Quicklimes with an intricate hyper-textual analysis – hypervisual might be more 

accurate – that not only explores the Kelly-Donen film, but experiments with the 

possibilities of a film criticism that follows non-linear, reader-driven paths in which the 

images and the text simultaneously elucidate and make themselves more complex. This is 

textual and visual criticism turned into Roland Barthes's writerly text. 

My own work has followed a number of paths. Early attempts emerged out of an essay I 

wrote on Martin Scorsese's Cape Fear (1991). Cape Fear is not great Scorsese, but, 

watching it, I was struck by something very curious I could not quite put my finger on: I 

could see other films behind it, in it, lurking like ghosts. None of these ghosts were the 

original 1961 Cape Fear, an early Psycho imitation. But it was Hitchcock I was seeing, 

especially early 1950s Hitchcock, before he had got into his stride; I was seeing 

Stagefright (1950), Strangers on a Train (1951), and / Confess (1953). The proof of my 

intuition appeared as soon as I looked closely at these three films. And what I had 

intuited were not ghosts, but a kind of palimpsest, images and scenes that lay under 

Scorsese's film. He was quoting – indeed recreating scenes – from these earlier films. 

Writing the essay analyzing these quotes didn't seem sufficient: I wanted a way to show 

them. 

I built a very simple Toolbook program, using a laserdisk and an overlay window, which 

merely had a screen of buttons naming the various scenes from Strangers on a Train and 

Cape Fear which, when pressed, and with the proper side of the appropriate disk in the 

player, displayed the images on the computer screen. The interface was plain, but the first 

step had been taken. The laserdisk/computer interface was extremely confining. There 

needed to be a way to capture the images and have them on the computer hard disk, 

quickly available and easy to insert into a program. Reasonably priced image capture 

boards that allowed easy digitizing and compression of short-duration clips, even on the 

then low-powered desk-top PCs, were already being developed in the early 1990s. This 

was the obvious answer to the problem: making image files as accessible and 

manipulable as any other digital artifact. Here was hardware and software that turned the

moving image into binary code, and once so encoded, almost anything could be done 

with it. 

With the encouragement and assistance of John Unsworth at the Institute of Advanced 

Technology in the Humanities, I wrote I kind of manifesto, "The Moving Image 

Reclaimed", for a 1994 issue of lATH's online journal Postmodern Culture 

(http://muse.jhu.edu/login?uri=/journals/postmodern_culture/v005/5.1kolker.html). The 

essay included various moving image files, including the major comparisons of Cape 

Fear and Strangers on a Train. It also included an idea of how the computer could be 

used literally to enter the image and diagram it to show how it worked. I took part of a 

very long shot in Citizen Kane, where Mrs Kane sells her son to Mr Thatcher for a deed 

to a copper mine – a sequence in which the movement of the camera and the shifting 

positions of the characters tell more interesting things about the story than the dialogue 

does – and animated it. That is, I plucked each frame from the sequence, and, overlaying 

the frame with various colored lines, put together a variation of a rotoscope (an animation 

technique where live action is traced over and turned into a cartoon). The result was a 

visualizing – a map – of how eyeline matches (the way a film allows us to understand 

who is looking at whom, and why) and the changing positions of the various characters 

were staged and composed in order to represent the spaces of Oedipal conflict. 

Unsworth and I decided that MPEG would be the best format for the purpose (this was 

some time before the development of streaming video), though we could not transmit the 

sound. The essay and images were put online. The images were large, and they took a 

great deal of time to download, but the project successfully proved the ability to solve the 

quotation problem. 

While it was a worthwhile experiment, it confirmed that the Web was an imperfect 

transmission vehicle for moving images. The next project was the creation of an 

interactive program that would provide a visual version of an introduction to the basic 

elements of film study. Prototyping in Toolbook, I created a series of modules on editing, 

point-of-view, mise-en-scene, lighting, camera movement, and other issues that seemed 

to me most amenable to using digitized clips. This was not to be a program on how to 

make a film, but, to borrow James Monaco's term (who has himself recently published a 

DVD full of text and moving images), how to read a film. The program uses text and 

moving image, both often broken up into successive segments to show how a sequence is 

developed, and always allowing the user to look at the entire clip. It included interactivity 

that allowed the user to, for example, put together a montage cell from Eisenstein's 

Potemkin or see the results of classical Hollywood three-point lighting by "turning on" 

the key, fill, and backlighting on a figure, or step through a sequence in Vertigo to show 

how camera movement and framing tell a story different from the one a character in the 

film is telling. It contained a glossary, so that by clicking on any hotword in the text, one 

would jump to a definition of the term. 

The point-of-view (POV) module was among the most challenging. POV is a difficult 

concept to analyze even in the traditional modes of film theory (though Edward Branigan 

and others have made important contributions to our understanding). How to show how

point-of-view works toward narrative ends – how the film guides our gaze and provides a 

"voice" for the narrative – required careful choices and execution. I went to a film that 

was about point-of-view, Hitchcock's Rear Window (1954). (It is, incidentally, no 

accident that so many film/computer projects focus on Hitchcock, one of the most 

complex formalists of American film.) By combining stills and moving images, winding 

up finally with a 3-D flythrough that indicates the perceptual spaces of the film and the 

way they turn at a crucial moment, the user is able to gain a visual grip on the process and 

understand how filmmakers make us see the way they want us to see. 

The creation and publication of the project, called Film, Form, and Culture, offers a 

useful example of other problems and successes that arise once you have decided to go 

digital. For one thing, most publishers are reluctant to publish CDs or DVDs alone. They 

are, after all, book publishers, and they still want paper. While continuing to prototype 

the CD-ROM project, I wrote an introductory textbook, which not only introduces terms 

and concepts basic to film studies, but also examines the history of film in the contexts of 

the cultures that produced them. The book was not written as a supplement, but as a 

stand-alone companion to the CD. Elements in the text that are covered on the CD are 

cited at the end of each chapter. And the book itself is illustrated by digital stills – that is, 

stills grabbed by the computer directly from the DVD or VHS of a film, a process which 

allows creating a sequence of shots which, while still, convey some of the movement 

between them, as well as indicating ways in which editing connects various shots. 

The publisher of the work, McGraw-Hill, bid out for a professional CD-authoring 

company to do the distribution CD. An important lesson was learned from this. The film 

scholar, used to dealing politely with editors, who, more often than not, had the right 

ideas on how to improve the language, organization, and accuracy of a manuscript, is 

here confronted with the age-old problem of "creator" (or, in the discourse of CD or Web 

authoring, the "content provider") vs. the producer, who was working within a budget 

provided by the publisher. The CD producer converted my prototype into Macromedia 

Director, in order to create a cross-platform product. We worked closely to choose a 

suitable interface, but some small "creative differences" ensued. The producer wanted a 

uniform interface, so that each screen would look the same. This is common 

multimedia/web-design practice, though it can limit flexibility, especially when one 

wants to break the limits of the screen frame, or present alternative ways of laying out the 

material. There was an urge to cut down on interactivity and slightly diminish an 

immersive experience for the user. They insisted on small anomalies, such as not 

allowing the cursor to change into a hand icon over a link. 

These were small, limiting constraints, and in some cases, convincing was needed on my 

part, to maintain my original conceptions, especially when these involved pedagogical 

necessities. But like all such give and take, the constraints were turned to advantages, 

and, to the producers' great credit, the last module, on music, turned out to be one of the 

best on the CD, and for that module, I provided only concept, assets (images and sounds), 

and text; they developed the appearance of the section based upon some preliminary 

prototyping on my part – which used Sergei Eisenstein's graphic visualization of how 

Prokofiev's score and Eisenstein's images worked in perfect abstract relationship to each

other in Alexander Nevsky (1938). In the second edition of the CD, many problems were 

solved. The cursor changes to a hand over a link. The new material on film noir and on 

sound are well executed. The interface remains, and even more interactivity was and can 

still be added. As with all digital projects, it is a work in progress, always undergoing 

improvement with each new edition and the help of very supportive publishers. 

But one issue, much more immediately pressing than dealing with producers, hangs like a 

cold hand over the project of using digital media in the study of film or any other 

discipline. This is the issue of copyright and intellectual property law (IP). Simply put, IP 

is the legal issue of who owns what, who can use it, and where it can be used. But the 

issue is not simple. The US government is trying to make copyright laws more and more 

inflexible and has made Fair Use (the legally permitted use of small portions of a 

copyrighted work for educational purposes) more difficult to apply, beginning with the 

1990 Digital Millennium Copyright Act. But many of the new laws are undergoing a 

challenge. And, with the major exception of Napster, there have been few major test 

cases to indicate how the various parties – content owner and content user – would fare in 

court. No one wants to go first! 

It might be helpful to briefly outline the major copyright laws and then go into some 

more detail on the major problems in gaining licenses, rights, and permissions for digital 

media, with the full knowledge that such a complex issue can never be completely 

elucidated in a short space. 

• The overwhelming majority of feature films are under copyright. 

• Copyright has (in its original form) limits: 

– Published before 1923, the work is in the Public Domain (PD). 

– Works created after January 1, 1978, are copyrighted for the life of the author (in film, 

often considered the Studio) plus 70 years. 

– Works published from 1923 to 1963: copyright holds for 28 years, renewable up to 67 

years. There are other variations, but these are the most pertinent. 

– The Sonny Bono Copyright Term Extension Act extends copyright on many works for 

as much as 95 years, and gives foreign works this extension even if the works are not 

formally copyrighted. Works published in or before 1922 remain in the Public Domain. 

Bono is under litigation in the Supreme Court. The restrictive 1990 Digital Millennium 

Copyright Act is also undergoing challenges. 

– Legislation has been passed for the "Technology, Education and Copyright 

Harmonization Act" (TEACH), that aims to modify copyright restrictions for Distance 

Education.

– Films in the Public Domain may have "subsidiary rights." That is, while the film may 

have come in to PD, parts of it, the score, for example, may have been separately 

copyrighted. Carol Reed's The Third Man is an example. 

– Licensing clips for a CD – even an educational one – takes a strong stomach, a very 

busy fax machine, and often a lot of money. 

Copyright issues are, perhaps even more than developing one's work, the greatest 

hindrance to scholars making use of digital interventions in film studies. However, they 

are not insuperable. Copyright concerns and the energy required for licensing clips 

should in fact stop no one, but only cause one to act cautiously and judiciously, especially 

if the work is aimed at publication. The Library of Congress (the repository for all 

copyrighted material) has, for example, a three-volume listing of films in the Public 

Domain. The last volume has an appendix that, as of the early 1990s, lists films for which 

extensive research on subsidiary rights was made. Many of these films are available on 

magnetic or digital media. This is the best first source for what is freely available – 

especially if the Bono Act is overthrown. 

After the research for PD titles, the very best way to start the rights and permissions 

process, or at least get an entry to the studios who own copyrighted material, is to contact 

the film's director – assuming he or she is alive. For Film, Form, and Culture, one 

filmmaker gave us complete use of one of his films, and signed a clearance for it. 

Another, Oliver Stone, a great filmmaker and an admirer of film studies, wrote to each of 

his distributors asking them to help me out. Sometimes even this will not prevail. Disney, 

whose Hollywood Films released Nixon, appreciated the personal note, but announced 

their policy of never giving permissions for CDs. However, Warner Bros did license 

thirty seconds of JFK, and the same length of clip from Citizen Kane, for $3,000 apiece. 

For the second edition, I added a module on film noir. I was most interested in using 

images from one of the greatest of the late-1940s noir directors, Anthony Mann. His 

dark, brutal, misanthropic films are currently owned by the famous publisher of children's 

literature, Golden Books. The ways of copyright are strange, indeed. Golden Books 

charged $1,000 for sixty seconds. In the great scheme of licensing clips, this is not a lot 

of money, and publishers who understand the value of having a CD with a book will pay 

such a relatively modest fee. 

For other clips, the Hitchcock Estate, who, when I started the project, still controlled Rear 

Window and Vertigo, worked with Universal pictures, who – unlike the Estate, but at 

their urging – reluctantly allowed me to use clips from those films. My understanding is 

that Universal and other studios have since streamlined both their rights process and their 

attitude. Everything else used was Public Domain. 

Certainly, none of this was easy (though now it may be at least easier, given such 

companies as RightsLine and RightslQ, that can help track who holds various rights to a 

film). In every successful instance of dealing with a studio, the result will be a long and 

intimidating contract, which must be read carefully. The result, however, is worth the

labor. The work of copyright searches and acquisition of licenses allowed Film, Form, 

and Culture, for example, to present students with access to clips from a large variety of 

films and uses them in some detail to show the precision with which they are made and a 

variety of ways in which they can be read and analyzed. 

I would only repeat that IP concerns should not be the initial obstacle to using moving 

images in a pedagogical or scholarly work. There are many ways to get what you need, if 

not always what you want. And Fair Use should still prevail for in-class pedagogical use, 

for digital stills in books, and, since the TEACH Act has been passed, for wider 

educational distribution. The studios have become somewhat more accommodating, a 

response that may have to do with DVDs and the unexpected popularity of their 

"supplementary material." DVDs also offer something of a new frontier in the 

development of digitized media in film studies and, hopefully, a relief for copyright 

concerns. 

DVDs have proven a boon to film scholars and film viewers alike. They have 

compensated for the culture's massive error in visual judgment when it chose VHS over 

Beta (which had a higher image resolution) in the 1980s. The success of DVD has proven 

that image quality does count. They have been of great help to film studies: they are 

cheap for an academic department to buy; they are readily available for students to rent 

and view at home. Their "supplementary material" (directors' and actors' commentaries, 

demonstrations on how scenes are shot, and how computer graphics are integrated into a 

film) have proven enormously popular, even though they are mostly illustrative and 

anecdotal. This success, as I noted, came as something of a surprise to the studios, and 

the outcome is that they may be taking notice of the fact that viewers may, first of all, be 

interested in older titles, and, second, do want to know more about the film they are 

watching. In fact, studios may now be admitting that a film might have educational value! 

Herein lies some hope on the licensing issue. The studios (and this is pure fantasy at the 

moment) may some day, when a scholar tells them that use of their material will help 

further sales of their films by educating new viewers, and without "copying" clips, begin 

to accept the claim and therefore restrain themselves from deriding the person who makes 

such an argument. 

Use of DVDs for analysis is being made possible by some new technology that opens 

interesting possibilities of using commercially available DVDs, controlling them as one 

would a digitized file on the computer, and even creating a set of analytic tools available 

on a CD or on a website to address the DVD in the individual's own computer. Some of 

this work has received funding from the National Endowment for the Humanities. 

Again, programming issues are at stake here, and various choices have to be made, 

depending upon whether the creator of a DVD-based project wants to do a standalone 

(that is, a project that exists only on a computer or CD-ROM, along with the DVD) or a 

web-based project that contains the controls, a database, and an interface online, which 

address the DVD in the user's computer. Indeed, unlike digitized clips, addressing the 

DVD from the Web does not result in image degradation, because the image remains on 

the user's computer.

The basics for building a set of DVD controls are reasonably straightforward, if you have 

some facility at cutting and pasting code. Microsoft offers a complete programmer's 

guide to DVD control, down to the very frame. Full information and very simple code is 

given at . Drill through this 

page and you will find script that you can cut and paste into an HTML file or a Power 

Point slide to see how it works. There should be utilities, or plug-ins, available for the 

Mac and Mac-based Director that would perform similar operations. The Windows 

version makes use of the MSWebDVD ActiveX control (the program that contains the 

window in which the DVD will play as well as the instructions for writing very simple 

code). It is possible that the Javascript version of the code to control the DVD will also 

function on a web-based project on the Mac, and the latest version of Macromedia 

Director includes DVD controls, though I have not tested this. The work I have done so 

far has been in Toolbook and Visual Basic for standalone use. 

Again, the coding issues can be overcome in various ways. But there is yet another issue 

involved. While the use of a commercial DVD in no way bypasses copyright issues, it 

may (and I must emphasize that I, like anyone else, can only speculate) make licensing 

easier to obtain or cause even less of a worry for Fair Use in a classroom or other 

educational settings. After all, one is not digitizing copyrighted material, but using 

something that is already commercially available. The content provider, however, may 

still see this as using their material, in ways other than for home viewing. 

It may, finally, seem as if we have come full circle to the time when a computer 

controlled a laserdisk. There are important differences. DVDs are digital; laserdisks were 

analogue. Laserdisks had to be played either on a monitor, or on an overlay window on 

the computer screen through special hardware. DVDs play directly through the 

computer's graphics display card – as long as there are the proper drivers (which come 

automatically with any computer that has a DVD drive). All this makes control of DVDs 

easier and more flexible than laserdisk. In a relatively simple Visual Basic program, one 

can even capture a still from the DVD directly to disk. I have been able to program a 

DVD so that, with user input, the film is paused, the exact time of the pause is passed to 

the computer, which then uses that time to call up other information I have prepared in a 

database that is relevant to that moment or shot in the film. This can be combined with 

other images and analysis that further explains the shot or the sequence it is part of- and 

finally the film as a whole. In other words, this combination of database and image, along 

with the interactivity that gives the user choice over what part of the film he wishes to 

examine, provides an invaluable method to thoroughly analyze a single work. 

The database, created and connected to the DVD, contains information for every shot in 

the film – from dialogue, discussion of the narrative moment, mise-en-scène, editing 

patterns, and so on. A database is essentially a static set of cubicles, each containing 

various kinds of data, which can be organized and then accessed in a variety of ways. We 

use them continuously: whenever you go online to use the campus library, to shop, or to 

buy an airline ticket, you are manipulating a database. Terabytes of personal information 

(more and more being added each day) are stored by government agencies in databases. It 

is not a stretch to say that the computerized database is the one completely structured

item in an otherwise chaotic world – though this is no guarantee of the veracity of the 

data it contains. It must be ordered correctly and filled with well-thought out data, and at 

the same time accessible, and manipulable, containing data in the form of text, numbers, 

sounds, or images that can be drawn upon in a number of different ways. Using a simple 

scripted language called SQL (Standard Query Language, better known as "Sequel"), the 

database becomes a malleable, flexible, responsive thing, alive to query and incredibly 

responsive if you ask the right questions of it. Database tables are themselves 

combinable, open to cross-queries, with the ability to pull together a large array of 

information. 

Lev Manovich (2001: 226) theorizes that the database is essential to computer aesthetics, 

part or, perhaps more appropriately, the genesis of the various narratives – including 

analytic and theoretical narratives – we can create by computer. "Creating a work in new 

media", he writes, "can be understood as the construction of an interface to a database", 

the means of accessing, combining, temporalizing, and spatializing static bits of data. 

Allow me another example from my own work: before DVDs were available, I had 

started work analyzing Robert Altman's 1993 film Short Cuts, using a five-minute 

digitized clip. My initial interests were to create a search engine so that various key terms 

(say, "zoom", a camera lens movement that Altman turns into an aesthetic and narrative 

statement) would yield the corresponding shots in which the zoom was used. What I 

would like to do now, when the DVD becomes available, is to create a critical narrative 

of the entire film, especially concentrating on its complex narrative structure and the way 

that complexity is made perfectly comprehensible to the viewer through Altman's editing, 

his use of color, the matching of character movement, and, throughout, an abstract, 

intuited pattern of movement across the narrative fields of the film, which, indeed, can 

itself be thought of as a sort of database of narrative events. 

The creation of a database for a three-hour film is no easy task. There are a number of 

decisions to be made in order to make it become dynamic, and all of these must be done 

by the imagination and work of the film scholar. There is little automation here, but an 

extraordinary opportunity to learn about the film. One must choose the various elements 

to describe and analyze the shot – for example, narrative elements, mise-en-scene, 

editing, dialogue, etc. And, indispensably, though not very creatively, the "in" and "out" 

points for each shot – that is, where a shot ends and where the next shot begins must be 

found and entered in the database. The in and out points are where the program will first 

go to find the shot and the relevant information the user wants for that shot. The filling-in 

of the database fields is where the major analytic work is done; we have to make the 

same judgments, the same analytic insights, and apply the same methodologies as we 

would writing an article or a book. The difference is a kind of fragmentation and the 

adoption of a more aphoristic style than the critical writer is used to. At the same time, 

the writer has to be aware of a thread of analysis running through all the database entries 

and make sure that the keywords that the user may want to search on appear wherever 

appropriate.

The ability to search is a major advantage of a database: if the user wants to find any 

place in the film where a word is used, a color combination is present, a narrative element 

can be found, she should be able to enter that word, click a button, and have a list pulled 

from the database. Clicking on any entry in the list will bring up the accompanying shot. 

The search function opens up an entirely new problem in film analysis. We are, in effect, 

giving over some critical work to the user of the program. This opens up difficult critical 

and functional questions, the most striking of which is: what is the reader going to want 

to search for? Do we second-guess when we create the database, or create a list of 

keywords that we know the user may want to search on, and then make certain that they 

appear in the appropriate fields of the database? Do we provide such a list in the program 

itself, perhaps as part of the introductory apparatus, thereby suggesting what the user 

might want to find? When we go to a Web search engine, like Google, we know more or 

less what we are looking for, even though what we get may not precisely fit our query. 

Going to the interface of a film database and guessing what we want to find, is a problem 

of a different order. We have, somehow, to present a critical apparatus within the 

interface that discusses the program's intent and offers some guidance for the user. As 

much interactivity as we provide, we must provide as well a guide to the critical narrative 

we are striving to get the user to put together. 

We have, then, discovered a second major advantage of the use of the computer in film 

studies. The first was the ability to quote from and analyze a given sequence of a film. 

The second is the database, as huge as one wants, and as fine-tuned as one needs, full of 

ideas, information, analysis, and offering the user the ability to connect the data to the 

precisely relevant images within a complete film. 

What we need, finally, to make this complete is a program that searches images 

themselves. We can tag images in the database and search that way. In other words, we 

can describe the image content, color, composition, and narrative relevance, and allow 

the user to choose these and bring up the related images or shots. A new codec, MPEG-7, 

promises the ability to tag the image itself. But all these are still text-based. We have to 

write out a description for, or appended to, the image and then search for it by entering 

the keyword. There is software available to search still images by example: that is, by 

clicking on one image, other images with similar colors or shapes will be called up. 

These are complex applications, not yet easily available to a film scholar. 

Searching moving images is another matter still. The ability, for example, to search a 

database of Short Cuts for zoom shots of specific kinds, based merely on clicking on an 

example of that kind, would open up new avenues for studying a film's textuality, an 

auteur's style, and, most important, begin to enable us to understand the structure of 

cinematic representation itself. Software to search moving images is slowly being 

developed, although a researcher at Kodak told me "not in our lifetime." But that was six 

years ago. 

Note

1 Another aspect of Mamber's investigations, on the theory and ideology of the 

surveillance camera, can be found online at . 


Branigan, Edward (1984). Point of View in the Cinema: A Theory of Narration and 

Subjectivity in Classical Film. Berlin and New York: Mouton. 

Kolker, Robert (1994). The Moving Image Reclaimed. Postmodern Culture 5.1 

(September). At 

http://muse.jhu.edu/login?uri=/journals/postmodern_culture/v005/5.1kolker.html. 

Kolker, Robert (1998). "Algebraic Figures: Recalculating the Hitchock Formula." In 

Play It Again, Sam: Retakes on Remakes, ed. Andrew Horton and Stuart Y. McDougal. 

Berkeley: University of California Press. 

Mamber, Stephen (1998). Simultaneity and Overlap in Stanley Kubrick's The Killing. 

Postmodern Culture (January). At 

http://muse.jhu.edu/login?uri=/journals/postmodern_culture/v008/8.2mamber.html. 

Manovich, Lev (2001). The Language of New Media. Cambridge, MA: MIT Press. 

Miles, Adrian (1998). Singin' in the Rain: A Hypertextual Reading. Postmodern Culture 

(January). At 

http://muse.jhu.edu/login?uri=/journals/postmodern_culture/v008/8.2miles.html. 

Mulvey, Laura (1975). Visual Pleasure and the Narrative Cinema. Screen 16, 3 

(Autumn): 6–18. Reprinted at http://www.bbk.ac.uk/hafvm/staff_research/visuall.html. 

Information on copyright is from Lolly Gasaway, University of North Carolina 

http://www.unc.edu/~unclng/public-d.htm. David Green provided further information on 

the Bono Act and treats it and many other IP issues in Mary Case and David Green, 

"Rights and Permissions in an Electronic Edition", in Electronic Textual Editing, ed. Lou 

Burnard, Katherine O'Brien and John Unsworth (New York: MLA, forthcoming). 

Among many sources for information on IP law and policy are Ninch (the National 

Initiative for a Networked Cultural Heritage), at http://www.ninch.org, the Digital Future 

Coalition, http://www.dfc.org, and the American Library Association, 

http://www.ala.org. 

27. 

Cognitive Stylistics and the Literary Imagination 

Ian Lancashire

Cognitive Stylistics analyzes an author's idiolect, his individual language traits. Although 

cognitive psychology and neuroscience do not know how the human mind works, they 

have detected, through experiments on how people behave (not through personal 

testimony about that behavior), features of mental behavior that are consistent with a 

standard theory or model. That cautious experimentation uses recall and recognition tests, 

and EEG (electroencephalography), PET (positron emission tomography), fMRI 

(functional magnetic resonance imaging), and other scans. With them, scientists are 

painstakingly uncovering steps, and constraints in taking these steps, that characterize 

how we mentally create and utter sentences (no word in English covers both oral and 

written expressing, but perhaps "uttering" will do). The current standard model describes 

two language processes: an unself-conscious creative process, veiled and almost 

unknowable, named by older writers the Muse (Jacoby 1971); and a conscious analytical 

procedure by which all authors assemble and revise sentences mentally. 

These sciences are enhancing our understanding of how authors create both oral and 

written texts. New knowledge about language processing in the brain helps us interpret 

data from traditional computer text analysis, not because the mind necessarily works 

algorithmically, like a computer program, but because quantitative word studies reveal 

auditory networks, and the cognitive model asserts that the brain operates as a massively 

distributed group of such networks. Cognitive Stylistics underscores how every utterance 

is stamped with signs of its originator, and with the date of its inception: these traits are 

not unique, like fingerprints, but, taken together, they amount to sufficiently distinctive 

configurations to be useful in authorship attribution or text analysis. Cognitive Stylistics 

determines what those traces may be, using concordances and frequency lists of repeated 

phenomena and collocations: together, partially, these conceivably map long-term associational 

memories in the author's mind at the time it uttered the text. Such clusters of 

repetitions arise from built-in cognitive constraints on, and opportunities in, how we 

generate language. The length of fixed phrases, and the complexity of clusters of those 

collocations, are important quantitative traits of individuals. 

Experimental cognitive psychology restores the human author to texts and endows 

computer-based stylistics with enhanced explanatory power. The potentially 

distinguishable authorship traits to which it draws attention include the size of the 

author's personal working memory (Just and Carpenter 1992). The cognitive model 

shows that, far from being dead, traces of the author do remain in the work, just as 

Shakespeare vowed it would in his sonnets, long after his own personal death. 

Collocational clusters also change over time as an individual's memory does. As the 

implications of cognitive research become understood, text-analysis systems may change, 

and with them stylistics. Texts in languages whose spelling departs widely from its 

sounding will likely be routinely translated into a phonological alphabet, before 

processing, as researchers recognize the primacy of the auditory in mental language 

processing. Because the standard cognitive model breaks down the old literary distinction 

between what is said and how it is said, interpretation and stylistics will come together. 

First Impressions

A paradox underlies stylistic research. Most of us do not know much about how we make 

and utter text, simply because we are so expert at uttering. The more we make texts, the 

less we have time to be self-conscious about doing so. Committing ourselves completely 

to the action as it unfolds, we no longer attend to how it takes place. 

Composing for anyone whose style is likely to be subject of analysis resembles walking, 

cycling or even, sometimes, driving a car on a highway. We can almost entirely devote 

our minds to other things and yet execute these actions properly. Kosslyn and Koenig say 

the more we know something, the harder it is to declare how we do it: "when one 

becomes an expert in any domain, one often cannot report how one performs the task. 

Much, if not most, of the information in memory cannot be directly accessed and 

communicated" (1992: 373). Recently one of my students, a professional actor, described 

an unnerving experience she had midway through a theatrical run of Henrik Ibsen's 

Enemy of the People. As she stood, waiting to go onstage, she could not remember 

anything of what she was to say or do when performing her part. Yet once she walked 

onstage, she executed everything perfectly just in time. She then understood why many 

actors repeatedly experience dread and sickness just before going on. So ingrained has 

the experience of performing become, over many weeks, that they can no longer summon 

the words or even the actions to conscious memory. Ironically, even if these actors were 

to succumb to amnesia, they still would not lose their roles. These have just become 

inaccessible and so are in effect protected against loss. Amnesiacs may forget who they 

are, but they can all the same speak and write (Squire 1987: 161, 171; Shimamura et al. 

1992). Ironically, the more we do something, the less we can attend to how we do it. This 

well-observed cognitive effect shows how frightening and even disabling can 

neurological processes developed to ensure reliable performance of an essential skill be 

in practice. 

Technology partially conceals this actual neglect from anyone who composes directly 

onto an artificial memory device. Equipped with a pen, a typewriter, or digital editing 

tools, authors see their text unfolding from their minds as they manually encode it in 

alphanumeric symbols on screen or paper. Making sentences visibly explicit as 

composed, writers no longer worry about having to store mentally what they create. 

Externalized, the words, phrases, and sentences of utterances can be easily deleted, 

rearranged, transformed grammatically, and replaced. We fully attend to the analytic task 

of doing these things. Because we externalize subvocal or inner speech immediately in 

visual form, we feel totally conscious of the mental activity of composing. Yet all we 

experience is the storage and the manipulation of symbols manifested outside the mind. 

What happens within the mind remains dark, especially to the expert composer, although 

novices learning how to use a language may assemble, with painful slowness, utterances 

in memory, consciously, before they utter them. The inexpert attend completely to the 

task. They may even be able to describe what steps they take consciously, probing their 

memory, and editing the results mentally in advance of speaking it. Any native speaker 

can follow this method in preparing to utter a sentence in their own tongue. It is so slow 

in natural conversation and composition as to be seldom worth using.

Expert authors in many languages often mention this neglect of attention to how they 

compose. Although they do not theorize their inability to see how they create sentences, 

their cumulative testimony is convincing. Being unable to see themselves create text, they 

characterize the process itself as beyond comprehension. Jean Cocteau explains: "I feel 

myself inhabited by a force or being – very little known to me. it gives the orders; I 

follow" (Plimpton 1989: 106). Elizabeth Hardwick agrees: "I'm not sure I understand the 

process of writing. There is, I'm sure, something strange about imaginative concentration. 

The brain slowly begins to function in a different way, to make mysterious connections" 

(Plimpton 1989: 113). Fay Weldon candidly admits: "Since I am writing largely out of 

my own unconscious much of the time I barely know at the beginning what I am going to 

say" (Winter 1978: 42). Cynthia Ozick imagines language coming out of disembodied 

nothingness. 

I find when I write I am disembodied. I have no being. Sometimes I'm entranced in the 

sense of being in a trance, a condition that speaks, I think, for other writers as well. 

Sometimes I discover that I'm actually clawing the air looking for a handhold. The 

clawing can be for an idea, for a word. It can be reaching for the use of language, it can 

be reaching for the solution to something that's happening on the page, wresting out of 

nothingness what will happen next. But it's all disembodied…. I fear falling short. I 

probably also fear entering that other world; the struggle on the threshold of that 

disembodied state is pure terror. 

(Wachtel 1993: 16) 

John Hersey compares composing to "'something like dreaming' … I don't know how to 

draw the line between the conscious management of what you're doing and this state" 

(Plimpton 1988: 126). Jonathan Raban thinks of himself as taking down spoken text, as if 

in dictation, uttered by an "it" – the pronoun used by Cocteau – rather than emerging 

from his own consciousness. 

All writers are in some sense secretaries to their own books, which emerge by a process 

of dictation. You start the thing off and on the first few pages you're in control, but if the 

book has any real life of its own, it begins to take control, it begins to demand certain 

things of you, which you may or may not live up to, and it imposes shapes and patterns 

on you; it calls forth the quality of experience it needs. Or that's what you hope happens. I 

don't just sit, making conscious decisions externally about how much of my experience I 

am going to use. 

(Wachtel 1993: 120) 

Earlier writers name the voice dictating the text as the Muse, a word for memory. Gore 

Vidal describes this unknown as sounding words aloud in the mind and goes further than 

others in admitting how ignorant he is of where this voice and its language come from. 

I never know what is coming next. The phrase that sounds in the head changes when it 

appears on the page. Then I start probing it with a pen, finding new meanings. Sometimes

I burst out laughing at what is happening as I twist and turn sentences. Strange business, 

all in all. One never gets to the end of it. That's why I go on, I suppose. To see what the 

next sentences I write will be. 

(Plimpton 1989: 63) 

Amy Lowell uses the word "voice" and says, like Ozick, that it comes from something 

disembodied, from no body. 

I do not hear a voice, but I do hear words pronounced, only the pronouncing is toneless. 

The words seem to be pronounced in my head, but with nobody speaking them. 

(Ghiselin 1952: 110) 

These very different authors all tried to watch how sentences came from their minds and 

gave up. They had to attribute their composition to a disembodied voice or dream that 

came out of nothing. In editing on the page or screen, they consciously delete and 

rearrange words within passages, adjust syntactic structure, re-sequence parts, and make 

word-substitutions, just as we all do. They do not locate the tedious and conscious 

sentence-building in short-term memory as the origin of genuine composition. Literary 

theorists agree. Colin Martindale characterizes "primary-process cognition" as "freeassociative 

… autistic … the thought of dreams and reveries", unlike the mind's problemsolving 

capability, "secondary-process cognition" (1990: 56). Mark Turner states that 

"All but the smallest fraction of our thought and our labor in performing acts of language 

and literature is unconscious and automatic" (1991: 39). 

Impressions Sustained 

Experiments in cognitive psychology and neuroscience have supported the concept of a 

disembodied mental voice that "utters" sentences without exhibiting how they were put 

together. 

All verbal utterances, written or oral, are received by the brain, and uttered by it, in an 

auditory-encoded form. Philip Lieberman explains that, during language processing, we 

access "words from the brain's dictionary through their sound pattern" (2000: 6, 62). 

Further, we internally model whatever we expect to hear separately from what we are 

hearing. That is, we can only understand heard speech by modeling silently the 

articulatory actions necessary to produce it. Lieberman explains that 

a listener uses a special process, a "speech mode", to perceive speech. The incoming 

speech signal is hypothetically interpreted by neurally modeling the sequence of 

articulatory gestures that produces the best match against the incoming signal. The 

internal "articula-tory" representation is the linguistic construct. In other words, we 

perceive speech by subvocally modeling speech, without producing any overt articulatory 

movements.

(2000: 48) 

The "McGurk" effect shows that our internal model for the utterance we are decoding, for 

various reasons, may differ significantly from what comes to us as auditory speech. 

The effect is apparent when a subject views a motion picture or video of the face of a 

person saying the sound [ga] while listening to the sound [ba] synchronized to start when 

the lips of the speaker depicted open. The sound that the listener "hears" is neither [ba] 

nor [ga]. The conflicting visually-conveyed labial place-of-articulation cue and the 

auditory velar place of articulation cue yield the percept of the intermediate alveolar [da]. 

The tape-recorded stimulus is immediately heard as a [ba] when the subject doesn't look 

at the visual display. 

(McGurk and MacDonald 1976, cited by Lieberman 2000: 57) 

If sound alone were responsible for what was heard, the McGurk subject would hear [ba] 

at all times. Because it attends to visual clues as well, the mind hears something never 

sounded, [da]. Only if the subject's mind manufactured speech sounds internally, drawing 

on all sensory evidence available, could its model differ from both auditory and visual 

clues individually. 

Authors who describe their inner mental activity when creating and uttering sentences are 

indeed correct when they characterize the "voice" they hear as bodiless. Even when we 

listen to the speech of others, it is not the speech of those others that we hear. It is the 

brain's own construct of those voices. Obviously, different brains might well perceive 

very different sounds, and thus words, from the same speech sounds heard from others. 

The mind becomes a reader of things made by a mental process that manifests itself as a 

bodiless, at times strange, voice. 

Cognitive sciences confirm authors' impressionistic descriptions of the language-making 

process as blank and inaccessible. The inner voice utters sentences that appear to come 

out of nowhere. That we hear a voice suggests we are listening to someone else, not 

ourselves but someone nameless, unrecognizable, and above all as distant from analysis 

as the mind of someone whose words we hear on a radio. We invoke such images for a 

form of memory of how to do something, creating utterances from natural language, 

where we lack the means to identify the remembering process with ourselves. That 

process exemplifies the expert mind failing to attend to what it is doing. During 

composition, we cannot correct this neglect, as we can when driving a car and suddenly 

wake up to a recognition that we have been driving on automatic, unattended, for some 

miles. We cannot will consciousness of how our minds create utterances. Making them 

relies on what is termed procedural or implicit memory, in which what is recalled (that is, 

how to utter something) is remembered only in the act of doing it. When we try to 

recollect something stored implicitly, we execute the stored procedure. The mind has 

never created a readable "manual" of the steps whereby it creates sentences. The only 

exceptions are those halting, deliberate activities in our short-term memory in which, as if 

on paper, we assemble an utterance, but of course this method too, in the end, relies on

the same mysterious voice or Muse, what cognitive sciences associate with implicit 

memory, to get it going. That truism, "How can we know what we are going to utter until 

we have uttered it?", characterizes the uncertainty of waiting for an utterance to be set 

down on the page or screen, to be spoken aloud, and to enter working memory. In all 

these situations, a silent inner voice precipitates the text out of nowhere. 

Twenty-five years ago, Louis Milic distinguished between what writers do unconsciously 

in generating language (their stylistic options) and what they do consciously in "scanning, 

that is, evaluation of what has been generated" (their rhetorical options; 1971: 85). Milic 

anticipated the distinction between implicit or procedural and explicit memory by several 

years (Squire 1987: 160). By insisting on the primary role of empirical, rather than of 

theoretical or impressionistic, evidence in the study of authorship, Milic directed the 

discipline of stylistics to the cognitive sciences. 

Why cannot we recall how we make a sentence? Why is the mind blocked by implicit 

memory in understanding one of the most critical defining features of a human being? 

The answer seems to lie in what we make memories of. Our long-term memory maker, 

located in the hippocampus, can store language, images, sounds, sensations, ideas, and 

feelings, but not neural procedures. Biologically, we appear to have no use for recalling, 

explicitly, activities by the language-processing centers themselves. Our minds, as they 

develop, have no given names for the actors and the events at such centers. Such 

knowledge is not forbidden. It is likely that it is unnecessary to and possibly 

counterproductive for our survival. 

The Cognitive Model 

So far, cognitive sciences have been shown to make two critical contributions to the 

analysis of style: it must be analyzed as auditory, and it emerges from neural procedures 

to which we cannot attend. Much else can be learned, however, by reading the scientific 

literature, both general studies (Kosslyn and Koenig 1992; Eysenck and Keane 1990; and 

Lieberman 2000), and analyses of specific areas like memory (Baddeley 1990; Squire 

1987). Recent scientific results, on which these books are based, appear as articles in 

journals such as Brain, Brain and Language, Cognitive Psychology, The Journal of 

Neuroscience, The Journal of Verbal Learning and Verbal Behavior, Memory and 

Cognition, Nature, Neuropsychologia, Psychological Review, and Science. These papers 

are accessible, but to work with them the intelligent reader must be grounded in the 

cognitive model of language processing. Because it is changing now, and will continue to 

do so, work in cognitive stylistics will need steady reassessment. However, the method of 

cognitive stylistics, which bases text-stylistics on cognitive effects that experimentation 

has found to illuminate the mind's style, will remain. Here follows a brief summary of the 

emerging model. It breaks down into two parts: memory systems, and neural processes. 

Scientists now recognize three basic kinds of human memory: (1) short-term memory, 

now known as working memory; and long-term associative memory, which falls into two 

kinds, (2) implicit or inaccessible, and (3) explicit or accessible. Implicit long-term

memory includes recall of a procedure in the action, and priming. Explicit long-term 

memory includes episodic memory and semantic memory. 

Working memory offers short-term storage of a limited amount of language so that it can 

be consciously worked on. This form of memory cannot be separated from processing 

activities. Alan Baddeley first proposed twenty years ago a very influential model of 

working memory split into three parts: a central executive and two subsystems, a visual 

area and a phonological or articulatory loop. The executive, which manages tasks in the 

two subsystems, has been localized in the dorsolateral prefrontal cortex (Lieberman 2000: 

77). All conscious mental work on language gets done in the articulatory loop. It 

encompasses many regions in the brain, including the well-known language centers, 

Wernicke's and Broca's areas, which handle, respectively, semantic and syntactic 

processing. (When damaged, Wernicke's area leads an individual to utter well-formed 

nonsense, "word salad", and Broca's area to utter a form of agrammatism, characterized 

by sentence fragments, understandable but unformed.) 

Central to the mind's conscious fashioning of language is the subsystem Baddeley calls 

the articulatory loop. So called because we must recirculate or rehearse a piece of 

language in order to keep working on it, this loop has a limited capacity, identified by 

Miller in 1956 as "seven, plus or minus two." Experiments have for decades confirmed 

these limits and show that individuals do not always reach the maximum potential 

capacity. The so-called reading-span test asks individuals to remember the final words of 

a sequence of unrelated sentences. Test results show a range of from 2 to 5.5 final words 

(Just and Carpenter 1992). As early as 1975 experiments showed that we could store in 

working memory, for recall, only as many words as we could utter aloud in two seconds 

(Baddeley et al. 1975). The number of such words declined as the total number of 

syllables increased, in what was termed "the word-length effect." Other experiments elicit 

so-called "effects" in subjects that confirm the auditory nature of working memory of 

language, and its severe capacity limits. The "acoustic similarity effect" shows that the 

ability of an individual to recollect a sequence of unrelated words suffers if they sound 

alike: semantic relations, or lack of them, and dissimilarity in sound have no effect. If 

working memory used the images of words, as text, the acoustic character of a word 

would not affect manipulation in working memory. The "articulatory suppression" effect 

also testifies to the auditory nature of language as consciously worked in memory. 

Individuals having to repeat aloud, continuously, a single sound or term or number (say, a 

function word such as "with") cannot rehearse, subvocally, utterances and so put or keep 

them in working memory. Auditory language immediately, unpreventably, enters it. 

Other experiments reveal that syntactically challenging sentences, such as those with 

clauses embedded within them centrally, reduce language capacity in working memory. 

Recently, Philip Lieberman asserts that we maintain "words in verbal working memory 

by means of a rehearsal mechanism (silent speech) in which words are internally modeled 

by the neural mechanisms that regulate the production of speech or manual signs" (2000: 

6). 

What can be learned about the mind's style from the articulatory loop? Its capacity 

constraints hamstring conscious mental work on making continuous sentences. It is little

wonder that we seldom mentally assemble or attend to editing what we are going to say 

before we utter it. We have perhaps not had artificial storage devices (e.g., paper, 

computers), where it is very easy to edit texts, long enough noticeably to atrophy our 

already limited working memory. However, we have supplemented that memory, for 

language manipulation, with external storage devices. Increasingly, texts will depart from 

the mind's constraints as we assemble sentences that greatly exceed the length and 

complexity of ones that can be attended to by the unassisted mind. This extension has two 

clear effects. First, it produces utterances that the human mind cannot consciously 

assimilate into working memory for analysis. This, like the McGurk effect, will cause the 

mind to work-around the problem and perhaps, in doing so, to remodel the ingested 

utterance in ways that distort it. Second, the very experience of total control over 

utterances that artificial storage devices give makes all the more unbearable our mental 

blindness to the generation of utterances. No one can use software to create sentences, 

outside of playful programs like Joseph Weizenbaum's Eliza and the Postmodernism 

Generator of

ippling effect on all memories linked to it. The strength of that activation, or its 

"weight", may be proportional to the number of times that the linkage between those two 

memories has previously been activated. 

Long-term associative memory does not store, in one place, complete utterances. Our 

mind's procedural memory of how to create or recreate an utterance calls on many 

different parts of the brain simultaneously, that is, concurrently, and they operate until the 

very instant of utterance or subvocal voicing. The mind's thesaurus (concepts), lexicon 

(words), and encyclopedia (images of things) consist of "morphologically decomposed 

representations" (Koenig et al. 1992). Different systems responsible for phonemes, lexis, 

part-of-speech, syntax, letter-shapes, etc., all stored in different locations, work in 

parallel. Current research, for example, locates color words in the ventral temporal lobe, 

action words in the left temporal gyrus, names of people in the temporal pole, and words 

for animals and tools in, respectively, the anterior and posterior inferotemporal area 

(Martin et al. 1995: 102; Lieberman 2000: 63, 65; Ojemann 1991). The concept of a 

typical noun resembles an address list that itemizes the locations of separately stored 

traits or features. Words associated with concepts are kept separately and matched 

together by a mediating area of the brain (Damasio and Damasio 1992) termed a 

"convergence zone." The "combinatorial arrangements that build features into entities, 

and entities into events, i.e. their spatial and temporal coincidences, are recorded in 

separate neural ensembles, called convergence zones … [found in] association cortexes, 

limbic cortexes, and nonlimbic subcortical nuclei such as the basal ganglia… [where they 

form] hierarchical and heterarchical networks" (Damasio et al. 1990: 105). Convergence 

zones are keys to neural networks. 

One type of long-term associative memory is priming. It is a wildcard in the formation of 

our memory store. Sensory experience lays down primes in the mind; we are never aware 

of and we do not attend to them. Kosslyn and Koenig describe how researchers read 

word-pairs to patients who had been anaesthetized for surgery. Later, when these patients 

were asked for the second, associated member of each word-pair, they replied with the 

formerly primed words more than with equally likely associated words (1992: 376). This 

effect is often termed repetition priming. A "prior exposure to a stimulus facilitates later 

processing of that stimulus" (1992: 374). Primes create sometimes unexpected links 

between an experience or an idea that we regard as common, and other things that would 

not ordinarily be associated with it. Even if everyone shared the same fuzzy definition of 

a simple concept, individual experiences impacting on us in the force of primes would 

subtly alter that already fuzzy definition. When we search long-term memory, we are 

intentionally, consciously, launching a prime-like probe. This type of prime always 

places semantic restrictions on retrieval. For instance, priming with the word "present" in 

the hope of raising memories related to the meaning "gift" will not elicit anything related 

to the meaning "now." When "primes are unattended, words related to either meaning 

appear to be facilitated" (Posner and Raichle 1997: 148–51). That is, when someone or 

some experience triggers long-term memory, what surfaces in equally unattended shape 

has all strings attached.

How does the mind use these associative networks to provide an utterance? That process 

remains elusive. Kosslyn and Koening say that the brain forces output of an utterance 

automatically "via a process of constraint satisfaction" (1992: 48, 268–69) in which what 

might be termed the best fit survives. This fit is to some pragmatic goal that meets the 

person's needs, however they may be said to exist. Emotions, desires, and purposes 

inform those needs. If cognition activates many brain sites in parallel, and if our vaguely 

sensed intentions determine what associative networks are selected to supply the 

semantic gist of what we will say, it is little wonder that we cannot describe how the 

Muse works. Working memory – the only mental place where we can consciously attend 

to language – is not big enough to hold this complex cascade of mental events and is also 

inherently unsuited to doing so. Mental processes are not images or sounds. 

So-called experiments "in nature" (that is, patients with brain damage) and devices that 

image brain activity have at least identified some essential components of this sentencemaking 

process. In the classical model of language brain function, Lichtheim (1885) and 

Geschwind (1970) proposed that two regions of the neocortex were responsible: the 

posterior Wernicke's area did semantic processing and sent language data to the frontal 

Broca's area, which clothed it with syntactic form and passed it on to the motor cortex for 

speaking. This model relied on ample medical evidence that patients with damage in 

Wernicke's area displayed faulty or nonsensical semantics and comprehension, and that 

those with damage in Broca's area revealed staccato, fragmented speech with agrammatism. 

No one disputes this evidence from brain damage, but localizing function so simply 

is now impossible. Neural activity during linguistic processing turns out to be massively 

parallel and distributed. Language does not follow one path but many. Also, after damage 

to Broca's and Wernicke's areas, the brain can enlist "alternate neuroanatomical 

structures" for language use (Lieberman 2000: 5) and recover functionality. Lieberman 

and his colleagues have also recently shown that subcortical basal ganglia structures, in 

one of the most evolutionally ancient (reptilian) parts of the brain, help regulate language 

processing. As far as the brain is concerned, the watchword is indeed in the plural, 

location, location, location. 

The Mind's Style 

Louis Milic some decades ago argued that stylistics must abandon impressionism for 

quantitative measures. Since then, researchers who compiled numeric data about style 

and made such measures have been puzzled to explain how they illuminate literary works 

or the authors who made them. Cognitive stylistics asserts that literary texts do not have 

style; individual minds do, in uttering. It contends that individual styles are profoundly 

affected by the neural constraints surrounding mental language processes. Because minds 

can only be indirectly analyzed, stylistics as a discipline must do research at the interface 

of cognitive sciences and corpus linguistics. Cognitive psychology and neuroscience tell 

us what to expect. Corpus linguistics extracts quantitative features of texts that can be 

analyzed in terms of how they match what human sciences predict will be found. 

How, then, do these sciences characterize language uttered by the disembodied sub-vocal 

voice long named the Muse? Keeping in mind that scientists base their models of how the

mind works on experimentally discovered effects, and that cognitive stylistics is at an 

early stage, a clear profile of the mind's style is beginning to emerge. It is: 

• auditory. Language utterances as stored, processed, and retrieved are phonological, not 

sequences of visual symbols, not alphabetic. 

• lexico-syntactic. Grammar and vocabulary cannot be separated: that is, syntactic 

structure imposed by Broca's area, and semantic fields by Wernicke's area, 

simultaneously participate in a unified, parallel, non-sequential process. 

• combinatory. The building blocks of any utterance are networks, what Damasio's 

convergence zones wire together. These blocks are not discrete words. The mind knows 

word-image-concept-sound combinations, not dictionary headwords and their 

explanations. 

• built from two-second-long units. These combinations appear in repeated phrases or 

unfixed collocations that are not more than 5–9 units in length. This follows if, as 

scientists suspect, working memory constraints are associated with a deeper limitation 

existing at the level of neural networks. Indeed, one use of computer text analysis is to 

help determine the size and complexity of long-term-memory networks, how many things 

can converge on a convergence zone. 

• biased to parataxis. Working memory is slowed when it must manipulate centrally 

embedded clauses. The mind, working towards a best fit in generating a sentence, may 

also well opt for simpler syntactical structures, unnested syntactic constructions, that is, 

paratactic sentences that take the form of a list of clauses linked by conjunctions. 

• semantically indeterminate. No conventional thesaurus, encyclopedia, or dictionary can 

adequately document the individual mind's convergence zones, what may underlie 

semantic fields and associative clusters, simply because every individual's long-term 

store is affected by fortuitous associations, primes. The traits associated with concepts 

and words alter subtly as the weights that measure their binding strength change over 

time, affected by everything we directly experience through the senses. This partly 

explains the language effect known as lexical indeterminacy (Pilch 1988). Many words 

cannot be defined precisely enough to avoid misunderstandings. Individuals use words 

differently and only partially overlap with others. 

• time-sensitive. As memory changes (or deteriorates), so do the characteristic traits of its 

utterances. Style is tied always to the health and age of the author's brain. 

Other features of the mind's default style may be known and certainly will be discovered. 

These inbuilt traits are enough to initiate research. 

Tools

Text-analysis algorithms and tools are available today to undertake rudimentary 

computer-based cognitive Stylistics research. 

Analysis is complicated by the need for enriched texts. We cannot use orthographically 

spelled text alone because the brain uniformly recognizes, stores, retrieves, and operates 

in working memory on language as auditory data, "silent speech." Each word must be 

available in orthographic and phonemic forms, at least, and optimally be segmented into 

syllables, and tagged with morphological information. Organizations such as the Speech 

Assessment Methods Phonetic Alphabet (SAMPA, 

) offer rigorous modern British and 

American alphabets. SIL International () has useful databases and 

software for this enrichment. Some automatic phonetic conversion tools recommended by 

the Simplified Spelling Society () use less rigorous 

alphabets. Phonetic conversion in general can only be done well by researchers who 

understand the sounds of a language. In early periods, before printing, spelling may 

represent word-sounds adequately for analysis. 

Researchers have been able to locate repeated words and fixed phrases since the 

invention of the KWIC concordancer in the late 1950s. Software by computational 

linguistics exists to generate the repeating fixed phrases of texts, termed "n-grams" 

(Fletcher 2002). Techniques for generating unfixed repeating phrases, that is, 

collocations, has been slower to develop. Collgen, a free TACT program I developed in 

the early 1990s, works only with word-pairs, word-word collocations, in small corpora. 

Xtract (1993), by Smadja and McKeown, is unavailable, but Word Sketch by Kilgarriff 

and Tugwell looks promising. Every researcher faces the not inconsiderable task of 

defining collocation itself and selecting a statistical measure that ranks repeating 

collocations by their significance. Collins Wordbanks Online 

() offers interactive searching for wordcombinations 

extracted from a 56-million-word Bank of English with two significance 

scores: "mutual information" and t-test. Berry-Rogghe (1973), Choueka et al. (1983), and 

Church and Hands (1990) offer various measures. Budanitsky and Hirst (2001) evaluate 

them. Significance rating conceivably recovers information about the strength of 

associativity among items stored in long-term memory. By processing concordance data, 

representations of such clusters can be built. 

I am not aware of any software that generates repeating clusters of words, fixed phrases, 

and unfixed phrases (collocations), say, to a limit of seven units, plus or minus two, but 

computational algorithms written for large industrial applications in data mining might 

permit repurposing. 

Applications 

Some trial literary analyses tested whether traits in works by two English poets, Geoffrey 

Chaucer and Shakespeare, could be accounted for within the standard cognitive model of 

language processing. My studies used texts with normalized orthography, untranscribed 

phonetically because Middle and Early Modern English pronunciation is not yet

sufficiently well understood. The software, Collgen, was limited to repeated fixed phrases 

and node-collocate pairs. Unfixed groups of three and more collocates were uncollected. 

These limitations aside, published results of the analyses tended to affirm that the styles 

of both authors might be cognitively based, and partly recoverable. 

I studied two passages from Shakespeare's works, Hamlet, III. 1 (the so-called "nunnery 

scene"), and Troilus and Cressida, 1.3.1–29 (Agamemnon's first speech), and two parts 

of Chaucer's Canterbury Tales, the General Prologue and the Manciple's prologue and 

tale, both in the context of the complete Canterbury Tales. The principal repeated 

vocabulary unit of both authors was the word-combination. In The Canterbury Tales, 

Chaucer used 12,000 word-forms but 22,000 repeating fixed phrases. Over two periods, 

1589–94 and 1597–1604, Shakespeare's different fixed phrases at least doubled his word 

types. The vocabulary of both poets consisted, not of single words, but of little networks, 

a fact consistent with associative long-term memory. The sizes of these networks were 

well within what working memory could accommodate. The 464 phrasal repetends 

appearing in both Chaucer's General Prologue and the rest of his Canterbury Tales 

averaged 2.45 words. They fell into 177 networks. Repeating fixed phrases in 

Shakespeare's texts in both periods averaged 2.5 words. Chaucer's largest repeating 

combination in the General Prologue (853 lines) had nine words. Shakespeare's largest in 

Hamlet III.l, under 200 lines long, had five words. A second Shakespeare analysis, of 

Agamemnon's speech in Troilus and Cressida. 1.3.1–29, found 107 phrasal repetends 

(repeating elsewhere in Shakespeare's works) in a passage that has only 159 different 

word-forms. Most combinations are two words in length, and the maximum has four. It is 

possible that the constraints of working memory affected the quantitative profile of the 

verbal networks employed by both men. 

For both authors, I used text-graphs of these phrasal repetends to depict how they 

intersected. In the Chaucer study, three overlapping "say"-"tell"-"speak" graphs drew 

attention to Chaucer's unnoticed marking of the three verbs: they distinguished "between 

speaking (words), telling tales, and saying truths or sooths" (Lancashire 1992a: 349). 

Gary Shawver's doctoral thesis at the University of Toronto, "A Chaucerian Narratology: 

'Storie' and 'Tale' in Chaucer's Narrative Practice" (1999), developed a finely detailed 

theory of Chaucer's narrative by taking this passing observation seriously. Two 

intersecting phrasal repetend graphs on the various word-forms for "true" and "love" in 

Hamlet also revealed a small network including the term "prove." 

Other findings illustrate the variety of useful applications of cognitive Stylistics. 

Chaucer's phrasal repetends in the General Prologue that repeated somewhere in the rest 

of the tales were graphed against those tales. After taking into account their different 

sizes, a distribution showed that the General Prologue shared more repetends with a quite 

unrelated tale, by the Manciple, found always just preceding the last tale, by the Parson. 

One possible interpretation of these results is that Chaucer wrote the two works in the 

same year. The 107 phrasal repetends in Agamemnon's speech in Shakespeare's Troilus 

and Cressida served a different purpose. They explained why Shakespeare used an odd 

sequence of images in lines that critics thought ill-conceived. Passages from earlier 

poems and plays here documented associative linkages that are private.

Conclusions 

Annotating texts for their repeating combinations conceivably finds traces of an author's 

long-term associative memory that make up his idiolect. Combinatorial analysis also 

assists in close reading. It puts into sharp relief the unrepeating words in those passages 

that may mark recent mental acquisitions. (Very long phrasal repetends enter the author's 

text manually, copied from other sources.) Analysis exposes some repetitions, notably 

syntactic strings, that may show that an author primes his mind by looking at the 

unfolding composition on page or screen and so moving the language into the artificial 

memory. The image of just-composed text can lead to re-use of the grammatical 

structures, that is, function words. Entering working memory as an image, then converted 

subvocally for the articulatory loop, writing stimulates variations on itself. Yet cognitive 

Stylistics is a young field. It must undertake hundreds of case studies to buttress these 

hypotheses. There are three special challenges. 

We still have no firm model of repeating word-combinations. Fixed-phrase repetends (ngrams) 

and collocations (order-free collocate groups) are known to vary according to the 

span or the window, measured in words, within which they are measured. The tighter the 

window (e.g., five words), the smaller the length of the repeating repetends. If a window 

becomes as large as a page, it can contain two or more instances of the same fixed phrase. 

In that case, should the repetend be redefined as a cluster of that repeating phrase? We do 

not know how large such repeating clusters are allowed to get. If we increase the size of 

the window to the complete text, we then have only one complex cluster that turns out 

not to be a repetend after all because it never repeats. The window itself must be 

theorized. It could be set at the length of working memory, but mental associational 

networks may be larger. If eight words on either side of one member of a collocate pair 

were set as the span (i.e., the maximum capacity of the articulatory loop in working 

memory), what would we find if, after collecting all repeating collocates for a node word, 

we then treated those collocates themselves as nodes and went on to collect their 

collocates, and so on? At what point does the original node no longer reasonably 

associate with a distant collocate of one of its node-collocates? 

We now have a reasonable vocabulary for describing the repeating fixed phrase and the 

cluster of a node and its collocates. Word-combinations that have reached their greatest 

phrasal length or collocate number are termed "maximals" (Altenberg and Eeg-Olofsson 

1990: 8). Shorter, more frequent substrings or sub-collocate groups, which appear to 

function as kernels or attractors for other units, are called "subordinates"; and substrings 

of those subordinates, substrings that do not occur more frequently than the subordinate 

in which they appear, are termed "fragments" (Lancashire, 1992b). Kjellmer (1991: 112) 

uses the phrase "right-and-left predictive" for nodes that accumulate collocates to the 

right or the left. Sinclair (1991: 121) characterizes the frequency behavior of a node to be 

"upward" if its collocates occur more frequently than it, and "downward" if less 

frequently. (For example, nouns collocate upward with function words, and function 

words collocate downward with nouns.) Repetend features such as these affect our sense 

of the strength of association – often termed semantic distance – between parts of a fixed 

phrase or node-collocates cluster. So far, we calculate strength on the basis of frequencies

(expected and actual) and mutual distance (measured in words), but size and other traits 

must be taken into account. For example, consider two node-collocate pairs, both 

separated by five words, and both sharing the same frequency profiles. (That is, both 

pairs share an actual frequency of co-occurrence that exceeds the expected frequency by 

the same amount.) Do they have different strengths of association if one of the pairs 

consists of two single words, and the other of two four-word fixed phrases? Or consider a 

repetend consisting of a single open-class word (e.g., noun, adjective, non-auxiliary verb, 

etc.), grammatically marked by a function word (e.g., article, preposition, etc.). Can we 

reasonably compare the strength of association there with that governing two open-class 

words that collocate freely and at some distance from one another? (In other words, can 

we treat grammatical and lexical collocations identically?) And how should we locate, 

name, and characterize words that have no significant repeating collocates and partner in 

no repeated phrases? These are words with a null "constructional tendency" (Kjellmer, 

1994: ix). The more we know about combinatory repetends, the more we can bring to 

understanding the mind's style and even maybe our long-term memory. Texts follow 

from an individual's brain functions, but experimental sciences do not know how to use 

texts as evidence. Cognitive stylistics can assist. 

The most common application of stylistics is authorship attribution. It locates markertraits 

in an unattributed work that match selected traits of only one of the candidates. This 

method systematically neglects the overall stylistic profile of any one author. We need 

many more text-based studies of how a single author uses repeating word-combinations 

over time. Humanities researchers must also go beyond texts for their evidence. We can 

enhance our knowledge about composition, and its relationship to brain function, by 

undertaking experimental studies common in psychology and neuroscience. If the 

humanities expect the sciences to attend to text-based analysis, they must begin to 

undertake controlled experiments to analyze the linguistic behavior of living writers as 

they compose. Interviews and tests before, during, and after periods of composition can 

be combined with detailed capture of the author's keystrokes as the creative process takes 

place. The necessary research tools for these experiments are now widely applied to 

human-user interaction research. 

In cognitive Stylistics, the humanities can take a leading role in profoundly important 

research straddling the medical sciences, corpus and computational linguistics, literary 

studies, and a community of living authors. 


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28. 

Multivariant Narratives 

Marie-Laure Ryan 

Media theorists divide the history of writing into four periods delimited by technological 

innovations: the oral age; the chirographic age (manuscript writing); the print age; and 

the digital age. The material support of language passed from unique to freely copiable; 

from restricted to a live audience to widely distributed; and from evanescent to durable,

only to return to a strange combination of evanescence and durability: though digital texts 

can be stored in a wide variety of memory devices, computer systems become rapidly 

obsolete and their archives unreadable. 

We know that the invention of writing and of the printing press had major consequences 

for textuality and narrativity. In oral cultures, as Walter Ong (1982) has shown, narrative 

was used as a mnemonic device for the transmission of knowledge; its memorization was 

facilitated by prosodic features – meter, rhyme, alliteration – as well as by fixed formulae 

and standardized images; and the limitations of memory were compensated by a 

relatively free episodic structure which allowed, within limits, permutation of its units. 

The invention of writing made it possible to shape the flat line of epic plots into the curve 

of dramatic form, a much more condensed narrative structure that allows a tighter 

management of emotional responses. Writing also froze the free order of plots into a 

fixed sequence. The printing press increased the length of narrative, revived the episodic 

pattern of epic poetry to fill the generous frame of the book, rendered mnemonic devices 

obsolete, and led to the birth of the novel, a relatively unconstrained narrative form that 

took plot to unprecedented levels of complexity: framing, embedding, branching, 

digressions, disruptions of temporal sequence, and multiple plot lines. Thanks to the 

spatiality of the page, images found their way into texts, and visual presentation was 

eventually recognized and exploited as an expressive device. After these three stages, is 

there anything left for digital media to develop in the narrative territory? 

This question would not be worth asking if the computer merely served as a medium of 

transmission for print texts (as it does when a digitized version of a Stephen King novel is 

sold online) or as an instrument of production for texts to be experienced in print: most 

novels, after all, come out of word processors. A truly digital text, or narrative, is one that 

cannot be transferred into the print medium without significant loss. It depends on the 

computer as a sustaining environment, and it uses the screen (or any other display device) 

as a stage for performance. 

What, then, are the properties of digital media, and, by extension, of digital texts, that 

bear upon the development of narrative? Several new media theorists (Murray 1997; 

Manovich 2001) have offered their own lists of distinctive features or essential properties 

of digital systems. The list proposed below is a distillation of the features I regard as the 

most relevant to the issues of textuality and narrativity: 

• Algorithm-driven operation. Computers are machines that can run a variety of 

programs, through which they can perform a variety of tasks. The behavior of digital 

objects, such as texts, images, and sound, is therefore regulated by an invisible code, the 

machine-language instructions of the supporting software. 

• Reactive and interactive nature. This property is a direct consequence of the preceding 

one. Computer code is based on conditional statements (if… then) that execute different 

instructions, depending on the state of the system or on external input. I call a system 

reactive when it responds to changes in the environment or to non- intentional user 

actions; it is interactive when the input originates in a deliberate user action. (Interactivity

does not necessarily mean, however, that the system will act in the way intended by the 

user.) 

• Performantial aspect. Another consequence of the first property. A digital text is like a 

music score or theater script: its written inscription is meant to be executed, either by the 

underlying code alone, or through a feedback loop that leads from the user to the 

underlying code to the display, and back to the user. Digital texts thus present the same 

contrast as the classic performing arts between the invariability of the script and the 

variability of its execution. 

• Multiple sensory and semiotic channels, or what we may call multimedia capabilities, if 

we are not afraid of the apparent paradox of talking about multimedia media. Digital 

environments can combine text, sound, still pictures, and animations. 

• Networking capabilities. Digital media connect machines and people across space and 

bring them together in virtual environments. This opens the possibility of multi-user 

systems and live (real-time) as well as delayed (asynchronous) communication. 

• Volatile signs. Computer memory is made of bits whose value can switch back and 

forth between positive and negative. The value of these bits determines the display. This 

means that unlike books or paintings, digital texts can be refreshed and rewritten, without 

having to throw away the material support. This property explains the unparalleled 

fluidity of digital images. 

• Modularity. Because the computer makes it so easy to reproduce data, digital works 

tend to be composed of many autonomous objects. These objects can be used in many 

different contexts and combinations, and undergo various transformations, during the run 

of the work. 

The output of a hidden program, digital narrative is shaped not only by the general 

properties of its "material" medium (i.e., silicon chips), but also by the specific 

affordances of the system through which it is created and executed. An Infocom 

interactive fiction of the 1980s or a Storyspace hypertext narrative of the early 1990s 

differs significantly from a Flash game or Director "movie" produced in this new century. 

The code of authoring programs is a second-order means of expression, and the various 

software supports should, therefore, be considered the submedia of digitality – just as 

clay tablets, papyrus scrolls, and codices are the submedia of manuscript writing. 

To complete the preliminaries of my discussion, let me briefly define what I mean by 

narrative. I endorse a medium-free, semantically based definition, according to which 

narrative is a type of meaning, or mental image generated in response to certain stimuli. 

A narrative text is an artifact designed to bring this meaning to mind. But the cognitive 

construct specific to narrativity can also be formed in response to stimuli not expressly 

designed for this purpose, for instance as an interpretation of life itself. This does not 

make life into "a" narrative, but it means that life may possess narrative potential – what 

we may call "narrativity." A narrative script (as I will call the relevant cognitive

construct) pictures a world situated in time and populated by intelligent agents. The time 

span framed by the representation encompasses a series of different states mediated by 

accidental happenings and deliberate actions. To understand the sequence as narrative 

means to be capable of reconstructing the motivations of the agents and the causal 

connections between events and states. As a mental representation of a temporal 

sequence of events, narrative is not only linear – or multilinear, when it follows several 

parallel or interwoven destinies – but vectorial: a plot must be followed in a specific 

direction, from birth to death, beginning to end. 

In contrast to some of the narrower definitions endorsed by narratologists, the present 

approach does not limit narration to the re-presentation of past events by a narrator, but 

accepts various modalities: narrative scripts can be told (diegetic mode), they can be 

shown (mimetic mode), or they can be enacted, not for the benefit of an audience, as is 

the case in drama, but as a self-rewarding activity. In this last mode, which prevails in 

computer games and participatory environments, the interactor is also the beneficiary of 

the text. Rather than being necessarily pre-encoded in a semiotic body, moreover, 

narrative scripts can be dynamically generated during the performance of a text through a 

simulation of actions and events. In contrast to scholars such as Espen Aarseth, who want 

to maintain a strict distinction between computer games and narrative (a concept 

implicitly narrowed down to its literary manifestations), my definition regards games and 

literary texts (or rather, those games and those literary texts that satisfy the proper 

semantic conditions) as two different narrative modalities. In the discussion below, I will 

therefore consider texts conceived as literature as well as texts regarded as games, but I 

will exclude the rich field of electronic poetry, as well as some innovative digital texts 

that rely too much on aleatory principles to fulfill the strict requirements of narrative 

coherence. 

The development of digital textuality hit the literary scene at a time when "serious" 

literature – in contrast to popular culture – was in the grip of an epistemological and 

aesthetic crisis that challenged the closure, stability, and vectoriality of narrative form. 

The epistemological issue was a critique of the alleged blindness of classical narrative to 

the complexity of the problem of truth: facts are asserted by authoritative narrators as a 

matter of absolute knowledge, and the reader is asked to take the text as the canonical 

version of the world. For the historian Hayden White (1987), narrative is not a reliable 

way to gain knowledge about the past, because it always involves a fabrication. Reality, 

he claims, does not offer itself to perception in the shape of a story. And for the discourse 

analysts Elinor Ochs and Lisa Capps, a truly "authentic" narrative of personal experience 

would not be a stable reconstruction of the past but "the airing and evaluating of 

alternative possible understandings of past events" (2001: 17). The aesthetic issue arose 

from a similar desire to open up the text to multiple variants. Throughout the twentieth 

century, as Umberto Eco has shown in The Open Work (1989), artists in many media 

have been obsessed with the idea of making the text endlessly self-renewable, of 

capturing infinity in its necessarily bounded body, of turning the work of art from a static 

self-identical object into a matrix of virtualities. Modern science has recently come up 

with some models and names for this aesthetic ideal: emergence, complexity, distributed 

intelligence.

Through its permanent inscription and linear reading protocol, its bound spine and 

bounded content, the book was perceived as an obstacle to the dream of the text that 

keeps giving. If the problem came from the limitations of print, the solution might come 

from the affordances of digital technology. Thanks to the properties of reactivity, 

interactivity, volatility, and modularity, every run of a digital text can be turned into a 

performance of different virtualities. Out of a limited number of elements, the computer 

allows the creation of a vast number of versions. Janet Murray (1997: 155–62) calls this 

property the "kaleidoscopic" nature of the medium. 

Twentieth-century writers did not await the advent of the digital age to develop a 

kaleidoscopic form of textuality. They did so through the technique of physically 

"chunking" and rearranging the text. We find the principle at work in the combinatorial 

algorithms developed by the members of the Oulipo literary movement: novels written on 

decks of cards which could be shuffled to produce different texts (Marc Saporta's 

Composition No 1, 1961); or sonnets obtained by selecting each verse from a matrix of 

poems (Raymond Queneau's Cent mille milliards de poemes). In these last two examples 

the combinatorial principle is aleatory, and it does not guarantee the production of 

meaning, especially not of narrative meaning. We can make a picture out of any 

arrangement of pictorial fragments; and perhaps even a "text" in the same way, since nonsense 

can have a certain poetic force, but certainly not a story. Consider for instance what 

would happen if we reshuffled the functions of Vladimir Propp's Morphology of the 

Folktale (1962 [1928]): 

The villain is defeated. One member of a family either lacks something or desires to have 

something. The hero is recognized. The hero is married and ascends to the throne. The 

hero and the villain join in direct combat. A false hero presents unfounded claims. An 

interdiction is addressed to the hero. 

An alternative to random sequencing, also practiced by print authors, is a combination of 

chunking and directed linking. After a given fragment of text the reader is given the 

choice between two or more alternatives. In "A Story As You Like It" by the French 

Oulipo member Raymond Queneau, the reader is asked: (a) "Do you wish to hear the 

story of the three big skinny beanpoles?" (yes – go to b; no – go to c); (b) "Do you want 

to hear the story of the three meddling mediocre bushes?" (yes – go to d; no – go to e). 

The expansion of each node creates a decision tree. Because there is only one way to 

reach a given node, whether terminal or medial, the author has absolute control over the 

information available to readers at every moment of their itinerary. This prevents the 

nonsense of the Propp example. But the ratio of number of paths vs. number of units in a 

tree-based system is not efficient; most works based on this algorithm offer a single-digit 

number of variants (two, for instance, in Julio Cortazar's Hopscotch (1966); about eight, 

in a typical Choose Your Own Adventures children's book). 

The digital medium offers a much more powerful engine for the creation of multi-variant 

narrative than either aleatory combination or unconditional branching because the 

passage from one chunk of text to the next can be meaningfully controlled by code. In a 

hypertextual system, for instance, the operation of clicking on buttons activates the

execution of some machine-language instructions, usually a "goto" to a certain memory 

address and an instruction to display the group of data beginning at this address. But the 

"goto" can be embedded in an "if… then… else" statement that imposes conditions on the 

branching. From the node "hero arrives at castle", for instance, the computer could be 

instructed to display the nodes "marriage to princess" or "king gives hero mission to 

rescue princess", depending on whether or not the reader has already visited "hero defeats 

villain." In a computer game, similarly, the player's actions function as links between 

segments in the sense that they trigger the execution of code, which leads to changes in 

the display and in the global state of the system. What players can do at a given moment 

depends on what they did in the past. The placement of links or the timing of user action 

in a well-designed narrative or informational system cannot be a random act, unless the 

point is to signify randomness itself. The creation of multivariant narratives depends on 

the existence of protocols that maintain linear coherence on the cognitive level – for, as 

the Propp example suggests, you cannot freely permute events in your mental 

representation of a story without consequences for the construction of causal and 

temporal relations. Here I would like to explore some of the system configurations, 

linking strategies, and modes of user participation that enable digital texts to achieve the 

difficult task of combining variability with narrativity. My investigation will focus on 

three aspects of narrative: discourse, which is the presentation of the narrative script 

through a particular medium, point of view, and plot – the narrative script itself. 

Variable Discourse 

Most of the digital texts that implement this type of variability are hypertexts, a form of 

organization described as follows in the documentation for Storyspace, the authoring 

software published by Eastgate: 

The word "hypertext" refers to a specific kind of writing. While customary text appears in 

a single sequence (e.g. page two always follows page one), hypertext presents the reader 

with multiple pathways through a document. These pathways are created by using 

hypertext links, a special kind of hypertext object that permits readers to actuate 

sequences, just like turning a page…. Simply put, hypertexts are branching textual 

objects that allow the reader to decide where to go next. 

(Storyspace FAQ; available from 

By the admission of its developers, Storyspace was designed to handle "large texts" 

(novels and databases rather than poems and short stories) with heavily interlinked nodes. 

One of the convenient features of Storyspace is the automatic building of a graph that 

keeps track of the system of links. This diagram gives the writer an overview of the 

developing network; but it can also serve as a navigational tool for the reader. 

Figure 28.1 shows the organizational map of Stuart Moulthrop's novel Victory Garden 

(1991). This map (inserted as art, rather than generated by the system) represents only the 

upper layer of the textual architecture: each named site on the map stands for a region of

the text with its own finely grained system of links, and it would be necessary to zoom in 

to a larger-scale image to get a full view of the textual architecture. The configuration of 

the map of Victory Garden is what theorists would call a network, or unrestricted graph. 

The presence of circuits – the formal trademark of a network – means that there may be 

many different ways to get to the same node. The system designer can control the reader's 

itinerary on the local level (where to go from a given node) but not on the global level. 

This feature discourages what I call a "narrative" interpretation of the sequence viewed 

by the reader: an interpretation that narrowly associates the order of appearance of lexia 

with a chronological and causal chain of events in the reference world. This type of 

reading would frequently lead to nonsense. For instance, if we first visited a node where a 

character is dead, and then a node where the character is alive, we would have to imagine 

a miraculous resurrection to make a story out of the sequence. It would take superhuman 

intelligence, or a map with very few decision points, for the designer to guarantee that 

every path will form a logically coherent plot. The map of Victory Garden is not a 

projection of branching possible parallel times into a spatial image but just what it claims 

to be: the map of a garden, or labyrinth, or graveyard – in any case a purely spatial 

construct. Despite its allusions to J. L. Borges's Garden of forking Paths, where paths 

represent possible futures rather than routes in space, Moulthrop's Victory Garden does 

not outline multiple destinies for its heroes, but rather traces many pathways into a 

reasonably solid and chronologically organized narrative core. There is no necessary 

global parallelism between the progression of narrative time and the progression of the 

reader on the paths of the garden, though there can be partial relations: for instance a 

stretch of path without branches or with a default continuation that captures a sequence of 

events in chronological order. (Victory Garden is particularly rich in such linear paths: 

they are what enables the reader to get a general idea of the plot.) 

28.1 Figure The map of Victory Garden

A text based on a map as complex as figure 28.1 does not tell a different story for every 

reader, or with every reading session, it rather tells a story in many different ways, 

varying discourse instead of plot. Approaching the text like a jigsaw puzzle, the reader 

rearranges lexia mentally, so that a fragment encountered at Tl in the reading sequence 

may be assigned time slot T22 in the reader's final reconstruction of the plot. Similarly, 

the reader of an epic text that begins in medias res will reconstrue a chronological order 

of events that differs from the order of their presentation. 

With its implication of a stable, determinate, complete, and coherent image to be 

recovered in its entirety, the image of the jigsaw puzzle admittedly fails to capture 

important aspects of the hypertextual reading experience. How does the phenomenon 

resist the metaphor? The reconstruction of narrative meaning is hardly ever complete 

because readers rarely visit all the lexia. Narrative is often only one of the threads in the 

textual web; following the postmodern aesthetics of the collage, most "classic" hypertexts 

interweave narration with metatextual comments, philosophical reflections, and intertextual 

imports. There may be not just one but many stories in the textual network, just as 

a print text may tell a complex story with many subplots, or the text may present different 

versions of the same events without designating one of them as true. (Michael Joyce's 

afternoon (1987) circles, for instance, among several versions of an accident witnessed 

by the narrator.) Even in its print form, narrative discourse rarely allows the linearization 

of all its information. Islands of free-floating events (mostly of the mental type) usually 

co-exist with streams of temporally ordered states of affairs. Whether print or digital, 

literary texts may actively militate against narrativity and its linear organization, though 

many of the texts that sound their demand for a post-narrative mode of signification do so 

from a platform of narrative fragments that maintain the reader's interest. 

The reconstruction of a reasonably consistent narrative world from a scrambled discourse 

would quickly become a tiresome activity if the reader's role were exhausted by the 

metaphor of the jigsaw puzzle. Any picture can be cut up, boxed, and sold as a puzzle. A 

narrow mapping of hypertext onto puzzles would therefore mean that the significance of 

the reader's involvement is independent of the narrative content of the text. From a 

literary point of view, the best hypertexts are those that manage to present the reader's 

activity of moving through the network and reassembling the narrative as a symbolic 

gesture endowed with a meaning specific to the text, a meaning which cannot be 

predicted by reading the medium as a built-in message. The hypertextual mechanism 

does not make a text automatically innovative and significant; it is up to the author to put 

it in the service of a unique textual idea, of a metaphor that gives meaning to the reader's 

activity. In Victory Garden, this activity is framed as an exploration of space. With its 

2,804 links connecting 993 lexia through a variety of paths planned over long stretches 

rather than from node to node (so that the narrative retains considerable linear 

coherence), Victory Garden takes Daedalian architecture to a level of complexity that 

probably no reader has fully appreciated. Moulthrop's metaphor of the Garden of Forking 

Paths was so suggestive of a radically new mode of reading, and described the structure 

favored by Storyspace so well, that it has become something of a theoretical cliche. In

Michael Joyce's afternoon, the often frustrated search of the reader for explanations and 

narrative continuity emulates the frantic calls of the narrator to find out whether or not 

the accident he witnessed involved, and perhaps killed his ex-wife and son (Bolter 1991: 

126). And in Shelley Jackson's Patchwork Girl, the reader is made to stitch together a 

text out of heterogeneous fragments, some recycled from other texts, just as the narratorcharacter 

Mary Shelley assembles a monster (allegory of a multiple, decentered 

subjectivity) by sewing together body parts collected from different women, and just as 

Shelley Jackson constructs a narrative identity for the monster from the stories of these 

women. 

Variable Point of View 

When the idea of interactive television was first introduced to the public, the advantage 

of the new technology was presented as an opportunity for spectators to view live 

broadcasts from a variety of perspectives. The capture of different cameras would be 

shown in small windows on the side of the screen, and by clicking on one of these 

windows the viewer would expand the image to the entire screen. Interactive movies, a 

genre that never really got off the ground, also played with the idea of variable point of 

view. In the exposition of I'm Your Man (1998), a DVD interactive movie playable on 

personal computers, we are given the choice between three options, each bearing a 

character's name. If we choose Leslie, the heroine, we will hear about Leslie's mission to 

give some important computer disk files to an FBI agent at a party. If we choose Richard, 

the villain, we will find out about his sinister plan to pose as a fake agent in order to get 

the documents from Leslie and then to kill her. (This is a very cheesy plot.) If we choose 

Jack, the fool-turned-hero-in-spite-of-himself, we will simply accompany him on his way 

to the party, looking alternatively at Jack and with Jack at the derrieres and decolletes of 

female passers-by. At every decision point we can switch to the corresponding moment 

on one of the other two narrative tracks. The switches have no impact on the plot, or on 

the order of its presentation. Time moves inexorably forward, and by selecting a point of 

view we miss information that will only become available when we stop the movie and 

start from the beginning again. Because of the temporal structure of film, it takes several 

passes through the movie to tie the three strands together into one coherent plot. 

Purely textual environments can vary point of view without running into this problem 

because a written text normally exists simultaneously in all of its parts. (Digital texts may 

change this, by destroying some of the links after the reader has visited them.) In the 

hypertext short story "A Long Wild Smile" by Jeff Parker (found at 

), the reader can move back and forth 

between two narrative strands: one narrated by a woman's fiance and another by her 

lover. Each node has links to the partner narrative, enabling the reader to follow both 

versions in parallel. Some links make very quick incursions into the other perspective, 

highlighting micro-level conflicts. In the following sequence, as Parker explains, the 

underlined words (which also function as links to the other passage) represent the 

interpretation by two different characters of the same string of sound:

(Node 1: Fiance's perspective) When I awake, she's joined him in the kitchen. She stares 

at the fridge while he stares at her. She says, "Did you ever ride ponies?" He says, "No." 

(Node 2: Lover's perspective) "Do you ever write rhyming poetry?" she said, taking a big 

sip of water and passing the cup to me. I drank and passed it back to her. 

This is a subtle authorial way to suggest misunderstanding (most likely the fiance's) 

behind the back of the two narrators. Locked in their own perspectives, the lover and the 

fiance quote what they hear, and they are not aware that the other interpreted the same 

words differently. But this kind of effect could have been achieved in print by 

juxtaposing the two passages and distinguishing them with indentation or with different 

fonts. 

Variations in point of view have been so successfully achieved in "old media" (think of 

William Faulkner's novel The Sound and the Fury, or of Akira Kurosawa's film 

Rashomon) that they seldom carry digital texts all by themselves. The online soap operas 

The Spot and The Lurker Files, which consisted of interwoven first-person narratives that 

represent the perspective of different characters, have now disappeared from the Web. 

Interactive film and TV will have to find other selling points to succeed commercially – 

such as the possibility for the latter to play any document from an archive. The linking 

design of Parker's text pursues a wider variety of literary effects than merely switching 

back and forth between two perspectives. Even a work as crude as I'm Your Man 

combines variable point of view with other types of choices, such as deciding what the 

characters should do next. It is perhaps computer games that show the most efficient use 

of variable point of view in a digital environment. Many games enable players to switch 

from a god's eye perspective, third-person display, through which they see their character 

as a moving object on a map of the playing field, to a first-person, horizontal perspective 

that shows the game world to the players through the eyes of their avatar. One view is for 

the planning of strategy in a suspended time, and the other for the execution of moves in 

the heat of the game-action. 

Variable Plot 

The simplest way to implement variability on the level of plot is to arrange multiple 

narrative developments along the branches of a decision tree. We see this principle at 

work in Choose Your Own Adventures children's stories, in the Queneau text quoted 

above, and in postmodern narratives with two or more endings or with different reading 

paths (Hopscotch). All these examples come from print texts, and all are fairly static. The 

various narrative lines are pre-inscribed within the database, limited in number (it would 

take God-like vision to control the combinatorial explosion of an arborescent structure 

with more than three levels), and the reader activates them through a purely selective type 

of interactivity. As long as this scheme is maintained, the digital medium does not really 

facilitate the creation of multiple plots. It makes travel along the branches a little bit 

easier, since clicking brings text to the reader, while print makes the reader go to the text, 

but the author still has to write the individual stories. But digital media present one 

significant advantage over print. They allow the user to interact with the text not just

selectively but also productively. It is only when the user contributes elements to a 

developing story, allowing plots to be dynamically generated at run-time, that a system's 

narrative productivity can be raised above the level reachable by print media. In this type 

of system the player's actions perform an individualized narrative by responding to the 

affordances of the textual world. Since no two players will take the same moves, no two 

runs of the program will produce the same narrative trace. This productive type of 

interactivity is found mostly in computer games, although it also occurs in chatrooms, 

MOOs (Multi-User Dungeons, Object-Oriented), dialogue systems (the famous ELIZA 

program), virtual reality installations, and projects in Interactive Drama (Ryan 200la). 

Here I will examine its effect on plot in three types of games: interactive fiction, firstperson 

shooters, and simulation games. 

The lowest degree of plot-level variability occurs when the player's actions fill in the 

blank spots in a pre-determined narrative script. This situation occurs in interactive 

fiction (IF), a text-only digital genre that flourished in the early 1980s, when computer 

graphics were too primitive to create an immersive environment. The genre nearly 

disappeared in the 1990s, displaced from the game market by fancy graphic interfaces 

and exiled from the front stage of digital literature by hypertext, a genre which quickly 

became the darling of theorists. But IF survives in small Internet niches, practiced 

without commercial and theoretical pressures by enthusiastic writers and programmers 

who freely share their work with a devoted community. 

In an interactive fiction, we impersonate a character within the story world. We specify 

the actions of this character by typing instructions to the system in a simplified version of 

a natural language on an old-fashioned command-line interface. A parser analyzes the 

input, and the system responds with appropriate, or sometimes comically inappropriate 

actions. Since most IPs are designed as adventure games, the user's character has a task to 

perform, and the plot only reveals itself fully to the user who is able to complete the task 

by solving all the problems. 

The downside of a system that grants free speech to the user is the impossibility of 

incorporating every verbal action into a coherent dialogue. IF systems understand only a 

limited vocabulary – typically 200 verbs, and as many nouns as there are different objects 

in the game-world – and they reject any input that cannot be parsed by their limited 

syntactic and semantic competence. This means that while the user can technically 

perform an unlimited number of actions, only some of these actions yield events in the 

developing story. The following examples, all taken from Spider and Web by Andrew 

Plotkin (Plotkin, website), illustrate various relations between input and plot. In the first 

example, player input counts as a turn in a conversation between the player's character 

and another member of the story world, and it reproduces exactly a verbal event within 

this world (> represents the output of the system): 

Interrogation chamber [You are imprisoned in a chair] 

"Don't be absurd", [the interrogator] says. "… if you'd had enough sense to walk away 

from that door, you wouldn't be here. You don't and you didn't and are; we caught you.

And you're going to start by telling me how you got through that door. Do you 

understand me? Player: Yes. 

>The man nods briefly – a man satisfied with the least important detail. 

In the next example, the player and the system takes turn at producing the story, though 

the player's input is not an act of narration but a command to the system. The system 

responds by updating its knowledge base, by narrating the result of the action, and 

occasionally (though this is not exemplified in the passage below) by retelling the action 

to the player in more elaborate language: 

>You are carrying nothing of importance, except a lockpick. 

Player: Put lockpick on plate. 

>The pick locks itself rigidly to the black plate by the door. Its status dot flashes to 

yellow. 

Player: Wait. 

>Time passes. 

The door slides neatly open. 

In the last example, the player's actions cannot be executed because they cannot be parsed 

by the system. Their event-status within the narrative is uncertain: do they count as futile 

attempts to solve problems, or are they altogether external to the story world? The 

response of the system is clearly external, and since it is situated on the same ontological 

level as the player's attempted action, the whole dialogue may be regarded as taking place 

outside narrative space and time: 

>A plain metal door faces you to the east, near the alley's end. It's firmly shut. 

Player: Open door. 

>You don't see how. 

Player: Scream in despair. 

>That's not a verb I recognize. 

Interactive fiction thus keeps the plot on the projected track by expelling the actions that 

do not fit within the script to a sphere that narratologists would call the extra-diegetic or 

para-textual.

Most interactive fictions trace a narrowly scripted trajectory. Players must visit a number 

of locations, either in free or fixed order, and they must solve a problem to get to the next 

location. In Spider and Web, for instance, player A may try the blaster and the lockpick to 

enter a space before succeeding with the scan scrambler; player B may never solve the 

problem and be caught by the guards in the hallway (a fate which can be averted by 

consulting the solutions posted on the Web); while player C may succeed on the first try. 

The only room for variation resides in the player's unsuccessful attempts. Spider and Web 

creates an original variation on this pattern by presenting the user's failed actions not as 

actual events but as lies told by the player's character to an interrogator who wants to 

know how the character has managed to infiltrate a secret war laboratory. Some 

interactive fictions have two or three endings, but in general, the variety of the player's 

input does not translate into an equal variety on the level of plot. Though they can 

develop very imaginative scripts that spice up the solving of problems with narrative 

interest, IF texts offer little incentive to re-enter their world once the game has been 

beaten. 

But endless replayability is not a reliable sign of high narrative variability. Consider the 

case of the so-called "First Person Shooter" or FPS (Wolfenstein, Doom, Quake, Unreal 

Tournament, etc.), a type of game that is played over and over again by its fans. Here also 

the user is cast as a character situated in both the time and space of the fictional world. 

The actions of the user determine the fate of the puppet character, and by extension, the 

fate of the fictional world. The script of the game simulates a live encounter with 

opponents, who can be manipulated either by the computer itself, or, if the game is 

played in a networked environment, by other human players. Whereas IPs allow users to 

type whatever they want, but filter out a narrow range of acceptable inputs, FPS games 

accept all of the user's actions as part of the game, but they limit these actions to moving 

around the game world, gathering ammunition, selecting weapons, and aiming and firing 

them. If we regard the player's moves as the writing of a "life story" for the character, 

every run of the system produces a new life, and consequently a new narrative trace. This 

narrative is created dramatically by being enacted, rather than diegetically by being 

narrated. Because the speed of the processor and the number of variables involved in the 

execution of the game script make the system's reaction partially unpredictable to the 

player, FPS games are inexhaustible matrices of different lives for the player's character. 

The stories of these lives remain in a virtual state until they are mentally replayed. When 

players tell about their game experience, they do so by producing a standard "diegetic" 

narrative. For instance: "I was out of ammo, and Tyrus and Jason were surrounding me, 

and I thought I was dead, but I made this cool jump, and landed on the other side of the 

wall, and I found a cache of new ammo. I got back to the dungeon and killed them both, 

and we won the game." But while FPS games are played over and over again, players 

rarely "replay" in their minds the story of a game, because these stories only differ from 

each other in who shot whom and who died when. This monotonous diversity is only 

compounded by the fact that all FPSs implement the same narrative archetype – the story 

of the quest – through the same motif: a physical confrontation between hero and villain. 

Though FPSs have a narrative base, like almost all recent computer games, the stories 

they generate are worth experiencing in the first person but rarely worth telling to a third 

party.

For a combination of replayability and narrative diversity, no formula can presently rival 

the type of game known as simulation or as "god game" – the latter name due to the fact 

that the player manipulates the objects of the game world from an external position of 

power. Simulation games create a dynamic model of a complex entity, such as a city 

(Simcity), an empire (Caesar), or a family (The Sims). An emergent process, the global 

evolution of this entity is the product of an algorithm that computes the interrelated 

consequences of the actions of many individual agents and continually feeds this output 

back into its own engine. In The Sims, for instance, a game whose master plot is a pursuit 

of happiness that requires a steady climb up the economic and social ladder, the wellbeing 

of the family of Bob and Betty Newbie is a balancing act through which this couple 

of suburbanites must manage the resources of time to satisfy bodily demands (eating, 

sleeping, using the bathroom), emotional demands (their relation to each other), 

entertainment demands (satisfied by buying the proper commodities), social demands 

(getting along with neighbors), and economic pressures (they must earn money to fill 

their house with pleasure-giving commodities). The game has been criticized for its 

capitalist philosophy, but the comic texts that ridicule some of the objects available for 

purchase can just as easily be read as a satire of consumerism. 

In keeping with its conception of life as a story that is constantly being written by the 

interaction between individuals and their environment, The Sims presents each object 

within the game world as an opportunity for action. A computer, for instance, affords the 

actions of playing games (good to cheer up depressed characters) or of finding a job 

(good to expand buying power). Characters are like objects: they too offer opportunities 

for actions by other characters. By mousing over Betty Newbie, the user playing Bob will 

for instance discover that Bob can kiss her, hug her, tickle her, or talk to her. The 

possibilities of action evolve during the run of the program, and since affordances are 

determined by the global state of the system, as well as by the nature of the objects, the 

user's choices will always produce a coherent narrative development. 

The object of the game is not to win, since life never runs out of problems to be solved, 

but to manage the life of the characters as successfully as possible according to cultural 

standards (make them rich), or to the player's personal idea of dramatic development (for 

instance, create interpersonal conflicts and situations leading to catastrophic events). 

Some players (the novelists) want to produce specific scenarios; others (the virtual 

historians) are more interested in discovering how certain actions will affect the fate of 

the family. It is the same curiosity that makes us wonder "what would my life be like now 

if I hadn't met this stranger in a bar" or that inspires practitioners of virtual history to 

write essays about "what would have happened in US foreign policy if JFK had not been 

murdered." The motivation of both types of players is much more narrative than that 

which drives the players of FPS. In contrast to the lives of FPS players, the narratives 

produced by The Sims can be enjoyed retrospectively as well as during the run of the 

program. The system allows users to retell the story of their game, or to invent their own 

Sim stories, by taking snapshots of the screen and by complementing their pictures with 

text. Many of these cartoon-form stories are posted on the game's website. Whether or 

not they make interesting reading, and whether or not they chronicle actual games, the

urge of players to share them with other players provides ample evidence that computer 

games can produce a genuine narrative interest in their fictional worlds. 

The ludic pleasure of deciphering the logic of the system – what game designers call 

reverse engineering- cannot be separated from the narrative pleasure of watching the 

story unfold. Without playing skills, the player would be unable to create interesting 

stories. On my first attempt to play The Sims, for instance, the cooking range caught fire, 

and because I hadn't bought a phone I could not call the fire department. I watched 

helplessly my whole family die, and my only option was to let the last survivor mourn for 

his loved ones before being himself engulfed by the flames. At this point there was 

nothing to do but start again from scratch. It is only by learning how the system "thinks" 

that players can increase their authorial power over their family. But since the system 

throws in unexpected events, the player's control over the fate of the characters is never 

absolute. For narrative to be pleasurable, appreciators must be able to anticipate to some 

extent the development of the story, but the story must be able to fulfill expectations in a 

surprising way. This time-tested formula holds no less for simulation games than for 

novels and drama. 

For all its narrative productivity, however, the formula of The Sims does not entirely 

satisfy the ambition of Will Wright, the designer of the game. He envisions a networked 

system that detects the most innovative subplots produced by users, updates the program 

with devices that facilitate their creation, and sends the new version to thousands of other 

users to find out if they, too, will attempt these subplots. "So in fact you [will] have the 

players… cross-pollinating their creativity", but the exchange will be mediated by the 

computer (Pearce 2002: 6). What Wright has in mind is a narrative variability of a higher 

power – a variability that affects the storytelling engine. 

Beyond Narrative 

In all the previous examples, variability remained within the bounds of narrative logic. 

Let me conclude my survey with an example that starts well within these bounds, but 

transgresses them during the run of the program, so that the text performs before the 

reader's eyes the dissolution of narrative. The text in question is The Impermanence 

Agent, by Noah Wardrip-Fruin and Brion Moss (2002). It consists of two windows on the 

screen. One of them contains the input narrative: a story inspired by the death of the 

author's grandmother, Nana, illustrated with family photos. The other window contains 

texts from various authors and memorial imagery from multiple cultures. The content of 

both windows scrolls down slowly by itself, then returns to the top, in an infinite loop. A 

reactive, rather than interactive text which takes full advantage of the networking 

capability of the medium, The Impermanence Agent modifies the content of each window 

by gradually integrating materials culled from the user's "scrapbook" – the file in which 

the Internet browser of the user's system stores text and images from the websites most 

recently visited. Whereas the original story reads "As a girl, at recess, Joan played 

punching games with the boys" it may become, "As a girl, at recess, Joan to follow in the 

Bowlers' footsteps played TeacberSource: Health and Fitness with the boys" (Wardrip- 

Fruin and Moss 2002: 14). A "lightweight intelligence model" selects "interesting" or

frequent words from the user's scrapbook, and makes sure that they fit syntactically into 

the text, but the model does not check the output for semantic coherence. The visual 

material undergoes similar blending with pictures from the scrapbook. The authors claim 

that this algorithm customizes narrative discourse to the reader's interests, but their 

definition of narrative is so loose that it accepts any grammatical sequence of words. 

After several loops through the program, all that is left of the original story is bits and 

pieces of an aleatory collage, as the text is invaded by fragments of other texts that may 

point towards, but never fully tell their own stories. My point in presenting this example 

is not to deny artistic potential to this kind of project, but rather to locate the point where 

multivariant textuality leaves narrativity behind and becomes conceptual art. 

The textual phenomena described in this chapter represent two extremes on the cultural 

spectrum. While computer games have taken popular culture by storm, generating a 

billion-dollar industry that rivals Hollywood and Disneyland, hypertext is an arcane 

academic genre read mostly by theorists and prospective authors. What remains to be 

conquered for digital textuality is the territory that lies between the stereotyped narrative 

scripts of popular culture and the militant anti-narrativity of so many experimental texts: 

a territory where narrative form is neither frozen nor ostracized, but recognized as an 

endlessly productive source of knowledge and aesthetic experiences. In the early 1990s, 

when theorists embraced hypertext as the genre that would carry the future of digital 

literature, the concepts of non-linearity and spatiality stood at the top of their list of 

aesthetic preferences. But narrative, as we have seen, is a fundamentally temporal, and 

consequently linear form of meaning. It was the great achievement of the twentiethcentury 

novel to have created complex networks of internal relations, through which the 

present word, passage, motif, or episode activated the energies of other textual elements 

to form patterns of signification that transcended the linear development of the plot. This 

process of irradiation produced what critics have called a spatial form. But the greatest 

spatial novels (for instance Proust's A la recherche du temps perdu) can still be read for 

the plot, because their spatial organization complements, rather than destroys, the 

temporality of their narrative layer. Computer games, especially the much-maligned FPS, 

also present a very efficient use of time and space, if by space one understands the 

concrete geography of the game world rather than an abstract formal pattern. The 

pleasure of FPS consists in equal parts of moving through a three-dimensional 

environment that constantly updates itself to reflect the player's perspective, and of taking 

the right action at the right moment in a world relentlessly subjected to the ticking of the 

clock. For digital texts to establish themselves within the cultural middle ground – the 

narratives of the educated but not professional public – they must do the opposite of what 

the twentieth-century novel achieved, and perhaps learn a lesson from computer games, 

without succumbing to their propensity for repetitive themes and stereotyped storylines: 

naturally spatial, these texts must reconquer the narrative temporality that fuels the 

reader's desire. 


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29. 

Speculative Computing: Aesthetic Provocations in 

Humanities Computing 

Johanna Drucker (and Bethany Nowviskie) 

With roots in computational linguistics, stylometrics, and other quantitative statistical 

methods for analyzing features of textual documents, humanities computing has had very 

little use for analytic tools with foundations in visual epistemology. In this respect 

humanities computing follows the text-based (dare I still say – logocentric?) approach 

typical of traditional humanities. "Digital" humanities are distinguished by the use of 

computational methods, of course, but they also make frequent use of visual means of 

information display (tables, graphs, and other forms of data presentation) that have 

become common in desktop and Web environments. Two significant challenges arise as a 

result of these developments. The first is to meet requirements that humanistic thought 

conform to the logical systematicity required by computational methods. The second is to 

overcome humanists' long-standing resistance (ranging from passively ignorant to 

actively hostile) to visual forms of knowledge production. Either by itself would raise a 

host of issues. But the addition of a third challenge – to engage computing to produce 

useful aesthetic provocations – pushes mathesis and graphesis into even more unfamiliar 

collaborations. Speculative approaches to digital humanities engage subjective and 

intuitive tools, including visual interfaces, as primary means of interpretation in 

computational environments. Most importantly, the speculative approach is premised on 

the idea that a work is constituted in an interpretation enacted by an interpreter. The 

computational processes that serve speculative inquiry must be dynamic and constitutive 

in their operation, not merely procedural and mechanistic. 

To some extent these notions are a radical departure from established practices in digital 

humanities. As the articles in this volume attest, many of the practices in digital 

humanities are becoming standardized. Technical and practical environments have 

become more stable. So have procedures for developing meta-data, for content modeling, 

for document classification and organization, search instruments, and the various 

protocols of ordering, sorting, and accessing information in digital formats. In its 

broadest conceptualization, speculative computing is premised on the conviction that 

logical, systematic knowledge representation, adequate though it may be for many fields

of inquiry, including many aspects of the humanities, is not sufficient for the 

interpretation of imaginative artifacts. 

Intellectual debates, collateral but substantive, have arisen as digital humanists engage 

long-standing critical discussions of the "textual condition" in its material, graphical, 

bibliographical, semantic, and social dimensions. No task of information management is 

without its theoretical subtext, just as no act of instrumental application is without its 

ideological aspects. We know that the "technical" tasks we perform are themselves acts 

of interpretation. Intellectual decisions that enable even such fundamental activities as 

keyword searching are fraught with interpretative baggage. We know this – just as surely 

as we understand that the front page of any search engine (the world according to 

"Yahoo!") is as idiosyncratic as the Chinese Emperor's Encyclopedia famously conjured 

by Borges and commented upon by Foucault. Any "order of things" is always an 

expression of human foibles and quirks, however naturalized it appears at a particular 

cultural moment. We often pretend otherwise in order to enact the necessary day-to-day 

"job" in front of us, bracketing out the (sometimes egregious) assumptions that allow 

computational methods (such as markup or data models) to operate effectively. 

Still, we didn't arrive at digital humanities naively. Though work in digital humanities has 

turned some relativists into pragmatists under pressure of technical exigencies, it has also 

reinvigorated our collective attention to the heart of our intellectual undertaking. As the 

applied knowledge of digital humanities becomes integrated into libraries and archives, 

providing the foundation for collection management and delivery systems, the ecological 

niche occupied by theory is called on to foster new self-reflective activity. We are not 

only able to use digital instruments to extend humanities research, but to reflect on the 

methods and premises that shape our approach to knowledge and our understanding of 

how interpretation is framed. Digital humanities projects are not simply mechanistic 

applications of technical knowledge, but occasions for critical self-consciousness. 

Such assertions beg for substantiation. Can we demonstrate that humanities computing 

isn't "just" or "merely" a technical innovation, but a critical watershed as important as 

deconstruction, cultural studies, feminist thinking? To do so, we have to show that digital 

approaches don't simply provide objects of study in new formats, but shift the critical 

ground on which we conceptualize our activity. The challenge is to structure instruments 

that engage and enable these investigations, not only those that allow theoretically 

glossed discussion of them. From a distance, even a middle distance of practical 

engagement, much of what is currently done in digital humanities has the look of 

automation. Distinguished from augmentation by Douglas Engelbart, one of the 

pioneering figures of graphical interface design, automation suggests mechanistic 

application of technical knowledge according to invariant principles. Once put into 

motion, an automatic system operates, and its success or benefit depends on the original 

design. By contrast, Engelbart suggested that augmentation extends our intellectual and 

cognitive – even imaginative – capabilities through prosthetic means, enhancing the very 

capabilities according to which the operations we program into a computer can be 

conceived. Creating programs that have emergent properties, or that bootstrap their 

capabilities through feedback loops or other recursive structures, is one stream of

esearch work. Creating digital environments that engage human capacities for subjective 

interpretation, interpellating the subjective into computational activity, is another. 

Prevailing approaches to humanities computing tend to lock users into procedural 

strictures. Once determined, a data structure or content model becomes a template 

restricting interpretation. Not in your tag set? Not subject to hierarchical ordering? Too 

bad. Current methods don't allow much flexibility – a little like learning to dance by 

fitting your feet to footsteps molded into concrete. Speculative computing suggests that 

the concrete be replaced by plasticine that remains malleable, receptive to the trace of 

interpretative moves. Computational management of humanities documents requires that 

"content" has be subjected to analysis and then put into conformity with formal 

principles. 

Much of the intellectual charge of digital humanities has come from the confrontation 

between the seemingly ambiguous nature of imaginative artifacts and the requirements 

for formal dis-ambiguation essential for data structures and schema. The requirement that 

a work of fiction or poetry be understood as an "ordered hierarchy of content objects" 

(Allen Renear's oft-cited phrase of principles underlying the Text Encoding Initiative) 

raises issues, as Jerome McGann has pointed out. Productive as these exchanges have 

been, they haven't made the shrug of resignation that accompanies acceptance of such 

procedures and presses them into practice into anything more than a futile protest against 

the Leviathan of standardization. Alternatives are clearly needed, not merely objections. 

The problems are not just with ordered hierarchies, but with the assumption that an 

artifact is a stable, constant object for empirical observation, rather than a work produced 

through interpretation. 

Speculative computing is an experiment designed to explore alternative approaches. On a 

technical level, the challenge is to change the sequence of events through which the 

process of "dis-ambiguation" occurs. Interpretation of subjective activity can be 

formalized concurrent with its production – at least, that is the design principle we have 

used as the basis of Temporal Modeling. 

By creating a constrained visual interface, Temporal Modeling puts subjective 

interpretation within the system, rather than outside it. The subjective, intuitive 

interpretation is captured and then formalized into a structured data scheme, rather than 

the other way around. The interface gives rise to XML exported in a form that can be 

used to design a document type definition (DTD) or to be transformed through use of 

Extensible Stylesheet Language Transformation (XSLT) or other manipulations. A 

description of the technical parameters that realize these conceptual premises is described 

in the case study below. The project is grounded on convictions that subjective 

approaches to knowledge representation can function with an intellectual rigor 

comparable to that usually claimed by more overtly formal systems of thought. This 

experimental approach has potential to expand humanities computing in theoretical scope 

and practical application.

Our path into the "speculative" has been charted by means of aesthetic exploration, 

emphasizing visual means of interpretation. These are informed by the history of 

aesthetics in descriptive and generative approaches, as well as by the anomalous 

principles of 'pataphysics, that invention of the late nineteenth-century French poetphilosopher 

Alfred Jarry. An outline of these aesthetic, literary, and critical traditions, 

and their role in the ongoing development of digital humanities, forms the first part of 

this chapter. This is followed by a discussion of the project that demonstrates the working 

viability of the precepts of speculative computing, Temporal Modeling. I invited Bethany 

Nowviskie to author this final section, since the conceptual and technical development of 

this research project has proceeded largely under her intellectual guidance. 

Aesthetics and Digital Humanities 

The history of aesthetics is populated chiefly by descriptive approaches. These are 

concerned with truth value, the specificity of individual media and activity "proper" to 

their form, the development of taste and knowledge, and the capacity of aesthetics to 

contribute to moral improvement – and, of course, notions of beauty and the aesthetic 

experience. These concerns are useful in assessing the aesthetic capabilities of digital 

media – as well as visual forms of knowledge production – even if only because of the 

peculiar prejudices such traditional approaches have instilled in our common 

understanding. For instance, long-standing tensions between images and text-based forms 

of knowledge production still plague humanist inquiry. A disposition against visual 

epistemology is deeply rooted in conceptions of image and word within their morally and 

theoretically charged history in Western philosophy. A schematic review of such key 

traditional issues provides a useful preface to understanding current concerns, particularly 

as visual tools become integrated into digital contexts as primary instruments of 

humanities activity. 

Fundamental distinctions differentiate descriptive modes from the intellectual traditions 

that inform our project: generative aesthetics, 'pataphysics, speculative thought, and 

quantum poetics. Generative approaches are concerned with the creation of form, rather 

than its assessment on grounds of truth, purity, epistemological, cognitive, or formal 

value. Speculative aesthetics is a rubric hatched for our specific purposes, and 

incorporates emergent and dynamic principles into interface design while also making a 

place for subjectivity within the computational environment. 'Pataphysics inverts the 

scientific method, proceeding from and sustaining exceptions and unique cases, while 

quantum methods insist on conditions of indeterminacy as that which is intervened in any 

interpretative act. Dynamic and productive with respect to the subject-object dialectic of 

perception and cognition, the quantum extensions of speculative aesthetics have 

implications for applied and theoretical dimensions of computational humanities. 

Before plunging into the vertiginous world of speculative and ';pataphysical endeavors, 

some frameworks of traditional aesthetics provide useful points of departure for 

understanding the difficulties of introducing visual means of knowledge representation 

into digital humanities contexts. To reiterate, the themes of descriptive aesthetics that are 

most potently brought to bear on digital images are: truth value, "purity" or capabilities of

a medium, the cognitive values of aesthetics, and the moral improvement aesthetic 

experience supposedly fosters. Debates about beauty I shall leave aside, except in so far 

as they touch on questions of utility, and the commonplace distinction between applied 

and artistic activity. 

The emphasis placed on the distinction between truth-value and imitation in classical 

philosophy persists in contemporary suspicions of digital images. The simulacral 

representations that circulate in cyberspace (including digital displays of information in 

visual form) are so many removes from "truth" that they would be charged with multiple 

counts of aesthetic violation in any Socratic court. Platonic hierarchies, and their negative 

stigmatization of images as mere imitations of illusions, are famously entrenched in 

Western thought. Whether we consider the virtual image to be a thing-in-itself, with 

ontological status as a first-order imitation, or as a mimetic form and thus yet another 

remove from those Ideas whose truth we attempt to ascertain, hardly matters. The fixed 

hierarchy assesses aesthetic judgment against a well-marked scale of authenticity. 

From a theological perspective, images are subject to negative judgment except when 

they serve as instruments of meditation, as material forms whose properties function as a 

first rung on the long ladder towards enlightenment. Such attitudes are characterized by a 

disregard for embodied intelligence and of the positive capacities of sensory perception. 

Denigrating the epistemological capacity of visualization, they assume that art and 

artifice are debased from the outset – as deceptive, indulgent acts of hubris – or worse, 

temptations to sinful sensuality. But if images are necessarily inferior to some "Idea" 

whose pale shadow they represent, digital images are redeemed only when they bring the 

ideal form of data into presentation. The difficulty of such reasoning, however, is that it 

collapses into questions about what form data has in a disembodied condition. 

Aristotle's concern with bow things are made, not just how "truthful" they are, suggested 

that it was necessary to pay attention to the properties particular to each medium. The 

idea of a "proper" character for poetry was opposed to – or at least distinct from – that of 

visual forms. Likewise, sculpture was distinguished from painting, and so on, in an 

approach dependent on the specificity of media and their identifying properties. This 

notion of "propriety" led to differentiation among aesthetic forms on the basis of media, 

providing a philosophical foundation for distinctions that resonate throughout literary and 

visual studies. Investigation of the distinct properties of media was formulated most 

famously in the modern era by Gotthold Lessing (Laocoon, 1766). The value judgments 

that lurk in his distinctions continue to surface in disciplinary and critical activity to the 

present day. Such boundaries are well policed. But "new media" challenge these 

distinctions through the use of meta-technologies and inter-media sensibility. In addition, 

artistic practices forged from conceptual, procedural, and computational realms can't be 

well accommodated by aesthetic structures with "purist" underpinnings. If a data file can 

be input from a typewriter keyboard and output as musical notes then the idea of the 

"purity" of media seems irrelevant. 

Critics trained in or focused on the modern tradition (in its twentieth-century form and 

reaching back into eighteenth-century aesthetics) have difficulty letting go of the

longstanding distinction between textual and visual forms of representation – as well as 

of the hierarchy that places text above image. The disjunct between literary and visual 

modernism, the very premise of an autonomous visuality freed of literary and textual 

referents, continues to position these approaches to knowledge representation within 

separate domains. The consequences are profound. Intellectual training in the humanities 

only rarely includes skills in interpretation of images or media in any but the most 

thematic or iconographic terms. The idea that visual representation has the capacity to 

serve as a primary tool of knowledge production is an almost foreign notion to most 

humanists. Add to this that many latter-day formalists conceive of digital objects as 

"immaterial" and the complicated legacy of hierarchical aesthetics becomes a very real 

obstacle to be overcome. Naivety aside (digital artifacts are highly, complexly material), 

these habits of thought work against conceptualizing a visual approach to digital 

humanities. Nonetheless, visualization tools have long been a part of the analysis of 

statistical methods in digital humanities. So long as these are kept in their place, a 

secondary and subservient "display" of information, their dubious character is at least 

held in check. Other conceptual difficulties arise when visual interfaces are used to 

create, rather than merely present, information. 

In a ideologically grounded aesthetics, forms of creative expression are understood to 

participate in spiritual evolution, moral improvement – or its opposite. 

Whether staged as cultural or individual improvements in character through exposure to 

the "best that has been thought" embodied in the artifacts of high, fine art, the idea 

lingers: the arts, visual, musical, or poetical, somehow contribute to moral improvement. 

Samuel Taylor Coleridge, Matthew Arnold, and Walter Pater all reinforced this sermon 

on moral uplift in the nineteenth century. Even now the humanities and fine arts often 

find themselves justified on these grounds. The links among ideas of progress and the 

application of "digital" technology to humanities continue to be plagued by pernicious 

notions of improvement. 

Hegel wrote a fine script for the progressive history of aesthetic forms. The cultural 

authority of technology is insidiously bound to such teleologies – especially when it 

becomes interlinked with the place granted to instrumental rationality in modern culture. 

The speculative approach, which interjects a long-repressed streak of subjective 

ambiguity, threatens the idea that digital representations present a perfect match of idea 

and form. 

But Hegel's dialectic provides a benefit. It reorients our understanding of aesthetic form, 

pivoting it away from the classical conception of static, fixed ideal. The interaction of 

thesis and antithesis in Hegelian principles provides a dynamic basis for thinking about 

transformation and change – but within a structure of progress towards an Absolute. 

Hegel believed that art was concerned "with the liberation of the mind and spirit from the 

content and forms of finitude" (Hegel 1975) that would compensate for the "bitter labour 

of knowledge" (ibid.). Aesthetic experience, presumably, follows this visionary path. If 

an aesthetic mode could manage to manifest ideal thought, presumably in the form of 

"pure data" – and give it perfect form through technological means, then descriptive

aesthetics would have in its sights a sense of teleological completion. Mind, reason, 

aesthetic expression – all would align. But evidence that humankind has reached a 

pinnacle of spiritual perfection in this barely new millennium is in short supply. In this 

era following post-structuralism, and influenced by a generation of deconstruction and 

post-colonial theory, we can't really still imagine we are making "progress." Still, the idea 

that digital technology provides a high point of human intelligence, or other 

characterizations of its capabilities in superlative terms, persists. 

Eighteenth-century aestheticians placed attention on the nature of subjective experience, 

rather than remaining focused on standards of harmonious perfection for objectified 

forms. In discussions of taste, subjective opinion comes to the fore. Well suited to an era 

of careful cultivation of elite sensibility, this discussion of taste and refinement 

emphasizes the idea of expertise. Connoisseurship is the epitome of knowledge created 

through systematic refining of sensation. Alexander Baumgarten sought in aesthetic 

experience the perfection proper to thought. He conceived that the object of aesthetics 

was "to analyse the faculty of knowledge" (Baumgarten 1735, sections 115–16; 

Beardsley 1966: 157) or "to investigate the kind of perfection proper to perception which 

is a lower level of cognition but autonomous and possessed of its own laws" (ibid.). The 

final phrase resonates profoundly, granting aesthetics a substantive, rather than trivial, 

role. But aesthetic sensibilities – and objects – were distinguished from those of tecbne or 

utility. The class divide of laborer and intellectual aesthete is reinforced in this 

distinction. The legacy of this attitude persists most perniciously, and the idea of the 

aesthetic function of utilitarian objects is as bracketed in digital environments as it is in 

the well-marked domains of applied and "pure" arts. 

In what is arguably the most influential work in modern aesthetics, Immanuel Kant 

elevated the role of aesthetics – but at a price. The Critique of Judgment (1790), known 

as the "third" critique – since it bridged the first and second critiques of Pure Reason 

(knowledge) and Practical Reason (sensation) – contained an outline of aesthetics as the 

understanding of design, order, and form. But this understanding was meant as the 

apperception of "Purposiveness without purpose." In other words, appreciation of design 

outside of utility was the goal of aesthetics. Knowledge seeking must be "free", 

disinterested, without end, aim, he asserted. In his system of three modes of 

consciousness – knowledge, desire, and feeling (Pure Reason, Practical Reason, and 

Judgment) – Kant positioned aesthetics between knowledge and desire, between pure and 

practical reasons. Aesthetic judgment served as a bridge between mind and sense. But 

what about the function of emergent and participatory subjectivity? Subjectivity that 

affects the system of judgment? These are alien notions. For the Enlightenment thinker, 

the objects under observation and the mind of the observer interact from autonomous 

realms of stability. We cannot look to Kant for anticipation of a "quantum" aesthetics in 

which conditions exist in indeterminacy until intervened by a participatory sentience. 

In summary, we can see that traditional aesthetics bequeaths intellectual parameters on 

which we can distinguish degrees of truth, imitation, refinement, taste, and even the 

"special powers of each medium" that are contributing strains to understanding the

knowledge-production aspects of visual aesthetics in digital media. But none provide a 

foundation for a generative approach, let alone a quantum and/or speculative one. 

Why? 

For all their differences these approaches share a common characteristic. They are all 

descriptive systems. They assume that form pre-exists the act of apprehension, that 

aesthetic objects are independent of subjective perception – and vice versa. They assume 

stable, static relations between knowledge and its representation – even if epistemes 

change (e.g., Hegel's dialectical forms evolve, but they do not depend on contingent 

circumstances of apperception in order to come into being). The very foundations of 

digital media, however, are procedural, generative, and iterative in ways that bring these 

issues to the fore. We can transfer the insights gleaned from our understanding of digital 

artifacts onto traditional documents – and we should – just as similar insights could have 

arisen from non-digital practices. The speculative approach is not specific to digital 

practices – nor are generative methods. Both, however, are premised very differently 

from that of formal, rational, empirical, or classical aesthetics. 

Generative Aesthetics 

The Jewish ecstatic traditions of gematria (a method of interpretation of letter patterns in 

sacred texts) and Kabala (with its inducement of trance conditions through repetitive 

cornbinatoric meditation) provide precedents for enacting and understanding generative 

practices. Secular literary and artistic traditions have also drawn on permutational, 

cornbinatoric, and other programmed means of production. Aleatory procedures 

(seemingly at odds with formal constraints of a "program" but incorporated into 

instructions and procedures) have been used to generate imaginative and aesthetic works 

for more than a century, in accord with the enigmatic cautions uttered by Stephane 

Mallarme that the "throw of the dice would never abolish chance." In each of the domains 

just cited, aesthetic production engages with non-rational systems of thought – whether 

mystical, heretical, secular, or irreverent. Among these, twentieth-century developments 

in generative aesthetics have a specific place and relevance for digital humanities. 

"Generative aesthetics" is the phrase used by the German mathematician and visual poet 

Max Sense to designate works created using algorithmic processes and computational 

means for their production. "The system of generative aesthetics aims at a numerical and 

operational description of characteristics of aesthetic structures", Sense wrote in his 

prescription for a generative aesthetics in the early 1960s (Sense 1971: 57). Algorithmically 

generated computations would give rise to datasets in turn expressed in visual or 

other output displays. Sense's formalist bias is evident. He focused on the description of 

formal properties of visual images, looking for a match between their appearance and the 

instructions that bring them into being. They were evidence of the elegance and formal 

beauty of algorithms. They also demonstrated the ability of a "machine" to produce 

aesthetically harmonious images. His rational, mathematical disposition succeeded in 

further distancing subjectivity from art, suggesting that form exists independent of any 

viewer or artist. Sense's systematic means preclude subjectivity. But his essay marks an

important milestone in the history of aesthetics, articulating as it does a procedural 

approach to form giving that is compatible with computational methods. 

Whether such work has any capacity to become emergent at a level beyond the 

programmed processes of its original conception is another question. Procedural 

approaches are limited because they focus on calculation (manipulation of quantifiable 

parameters) rather than symbolic properties of computing (manipulation at the level of 

represented information), thus remaining mechanistic in conception and execution. 

Reconceptualizing the mathematical premises of cornbinatoric and permutational 

processes so they work at the level of symbolic, even semantic and expressive levels, is 

crucial to the extension of generative aesthetics into speculative, pataphysical, or 

quantum approaches. 

Generative aesthetics has a different lineage than that of traditional aesthetics. Here the 

key points of reference would not be Baumgarten and Kant, Hegel and Walter Pater, 

Roger Fry, Clive Bell, or Theodor Adorno – but the generative morphology of the fifthcentury 

bc Sanskrit grammarian Panini, the rational calculus of Leibniz, the visionary 

work of Charles Babbage, George Boole, Alan Turing, Herbert Simon, and Marvin 

Minsky. Other important contributions come from the traditions of self-consciously 

procedural poetics and art such as that of Lautreamont, Duchamp, Cage, Lewitt, Maciunas, 

Stockhausen, and so on. The keyword vocabulary in this approach would not consist 

of beauty, truth, mimesis, taste, and form – but of emergent, autopoietic, generative, 

iterative, algorithmic, speculative, and so on. 

The intellectual tradition of generative aesthetics inspired artists working in conceptual 

and procedural approaches throughout the twentieth century. Earlier precedents can be 

found, but Dada strategies of composition made chance operations a crucial element of 

poetic and visual art. The working methods of Marcel Duchamp provide ample testimony 

to the success of this experiment. Duchamp's exemplary "unfinished" piece, The Large 

Glass, records a sequence of representations created through actions put into play to 

create tangible traces of abstract thought. Duchamp precipitated form from such activity 

into material residue, rather than addressing the formal parameters of artistic form-giving 

according to traditional notions of beauty (proportion, harmony, or truth, for instance). 

He marked a radical departure from even the innovative visual means of earlier avantgarde 

visual traditions (Post-Impressionism, Cubism, Futurism, and so forth). For all their 

conceptual invention, these were still bound up with visual styles. 

In the 1960s and 1970s, many works characteristic of these approaches were instructionbased. 

Composer John Cage made extensive use of chance operations, establishing his 

visual scores as points of departure for improvisational response, rather than as 

prescriptive guidelines for replication of ideal forms of musical works. Fluxus artists such 

as George Brecht, George Maciunas, Robert Whitman, or Alison Knowles drew on some 

of the conceptual parameters invoked by Dada artists a generation earlier. 

The decades of the 1950s and 1960s are peopled with individuals prone to such inspired 

imaginings: Herbert Franke and Melvin Pruitt, Jascia Reichardt, and the heterogeneous

esearch teams at Bell Labs such as Kenneth Knowlton, Leon Harmon, and dozens of 

other artists, worked in robotics, electronics, video, visual and audio signal processing, or 

the use of new technology that engaged combinatoric or permutational methods for 

production of poetry, prose, music, or other works. The legacy of this work remains 

active. Digital art-making exists in all disciplines and genres, often hybridized with 

traditional approaches in ways that integrate procedural methods and material production. 

One of the most sustained and significant projects in this spirit is Harold Cohen's Aaron 

project. As a demonstration of artificial aesthetics, an attempt to encode artistic creativity 

in several levels of instructions, Aaron is a highly developed instance of generative work. 

Aaron was first conceived in 1973, and not surprisingly its first iteration corresponded to 

artificial vision research at the time. The conviction that perceptual processes, if 

sufficiently understood, would provide a basis for computational models predominated in 

research done by such pioneers as David Marr in the 1970s. Only as this work progressed 

did researchers realize that perceptual processing of visual information had to be 

accompanied by higher-order cognitive representations. Merely understanding 

"perception" was inadequate. Cognitive schemata possessed of the capacity for emerging 

complexity must also be factored into the explanation of the way vision worked. 

Aaron reached a temporary impasse when it became clear that the methods of generating 

shape and form within its programs had to be informed by such world-based knowledge 

as the fact that tree trunks were thicker at the bottom than at the top. Vision, cognition, 

and representation were all engaged in a dialogue of percepts and concepts. Programming 

these into Aaron's operation pushed the project towards increasingly sophisticated AI 

research. Aaron did not simulate sensory perceptual processing (with its own complex 

mechanisms of sorting, classifying, actively seeking stimuli as well as responding to 

them), but the cognitive representations of "intellectualized" knowledge about visual 

forms and their production developed for Aaron made a dramatic demonstration of 

generative aesthetics. Aaron was designed to create original expressive artifacts – new 

works of art. Because such projects have come into being as generative machines before 

our very eyes, through well-recorded stages, they have shown us more and more 

precisely just how that constitutive activity of cognitive function can be conceived. 

'Pataphysical Sensibilities and Quantum Methods 

Before returning to speculative computing, and to the case study of this chapter, a note 

about 'pataphysics is in order. I introduced 'pataphysics almost in passing in the 

introduction above, not to diminish the impact of slipping this peculiar gorilla into the 

chorus, but because I want to suggest that it offers an imaginative fillip to speculative 

computing, rather than the other way around. 

An invention of the late nineteenth-century French physicist poet Alfred Jarry, 

'pataphysics is a science of unique solutions, of exceptions. 'Pataphysics celebrates the 

idiosyncratic and particular within the world of phenomena, thus providing a framework 

for an aesthetics of specificity within generative practice. (This contrasts with Sense's

generative approach, which appears content with generalities of conception and formal 

execution.) 

The original founder of 'pataphysics, Alfred Jarry, declared the principles for the new 

science in the fantastic pages of his novel Dr Faustroll, 'Patapbysicien: "Faustroll 

defined the universe as that which is the exception to oneself." In his introduction to Dr 

Faustroll Roger Shattuck described the three basic principles of Jarry's belief system: 

clinamen, syzygy, and ethernity. Shattuck wrote: "Clinamen, an infinitesimal and 

fortuitous swerve in the motion of an atom, formed the basis of Lucretius's theory of 

matter and was invoked by Lord Kelvin when he proposed his 'kinetic theory of matter.' 

To Jarry in 1898 it signified the very principle of creation, of reality as an exception 

rather than the rule." Just as Jarry was proposing this suggestive reconceptualization of 

physics, his contemporary Stephane Mallarme was calling the bluff on the end game to 

metaphysics. Peter Burger suggests that Mallarme's conception of "the absolute" 

coincides with a conception of aesthetic pleasure conceived of as a technological game, 

driven by a non-existent mechanism. The substantive manifestation in poetic form shows 

the workings of the mechanism as it enacts, unfolds. Generative and speculative 

aesthetics are anticipated in the conceptualization of Mallarme's approach. 

What has any of this to do with computing? 

Without 'pataphysical and speculative capabilities, instrumental reason locks computing 

into engineering problem-solving logical sensibility, programs that only work within the 

already defined parameters. The binarism between reason and its opposite, endemic to 

Western thought, founds scientific inquiry into truth on an empirical method. Pledged to 

rational systematic consistency, this binarism finds an unambiguous articulation in Book 

X of Plato's Republic. "The better part of the soul is that which trusts to measure and 

calculation." The poet and visual artist "implant an evil constitution" – indulging the 

"irrational nature" which is "very far removed from the true." Ancient words, they 

prejudice the current condition in which the cultural authority of the computer derives 

from its relation to symbolic logic at the expense of those inventions and sensibilities that 

characterize imaginative thought. By contrast, speculative approaches seek to create 

parameter-shifting, open-ended, inventive capabilities – humanistic and imaginative by 

nature and disposition. Quantum methods extend these principles. Simply stated, 

quantum interpretation notes that all situations are in a condition of indeterminacy 

distributed across a range of probability until they are intervened by observation. The 

goal of 'pataphysical and speculative computing is to keep digital humanities from falling 

into mere technical application of standard practices (either administered/info 

management or engineering/statistical calculations). To do so requires finding ways to 

implement imaginative operations. 

Speculative Computing and the Use of Aesthetic 

Provocation

Visual or graphic design has played almost no part in humanities computing, except for 

the organized display of already structured information. Why should this be necessary? 

Or continue to be true? What are the possibilities of integrating subjective perspectives 

into the process of digital humanities. And though emergent systems for dynamic 

interface are not realizable, they are certainly conceivable. Such perspectives differentiate 

speculative approaches from generative ones. 

The attitude that pervades information design as a field is almost entirely subsumed by 

notions that data pre-exist display, and that the task of visual form-giving is merely to 

turn a cognitive exercise into a perceptual one. While the value of intelligent information 

design in the interpretation of statistical data can't be overestimated, and dismissing the 

importance of this activity would be ridiculous, the limits of this approach also have to be 

pointed out. Why? Because they circumscribe the condition of knowledge in their 

apparent suggestion that information exists independently of visual presentation and just 

waits for the "best" form in which it can be represented. Many of the digital humanists 

I've encountered treat graphic design as a kind of accessorizing exercise, a dressing-up of 

information for public presentation after the real work of analysis has been put into the 

content model, data structure, or processing algorithm. Arguing against this attitude 

requires rethinking of the way embodiment gives rise to information in a primary sense. 

It also requires recognition that embodiment is not a static or objective process, but one 

that is dynamic and subjective. 

Speculative computing is a technical term, fully compatible with the mechanistic reason 

of technological operations. It refers to the anticipation of probable outcomes along 

possible forward branches in the processing of data. Speculation is used to maximize 

efficient performance. By calculating the most likely next steps, it speeds up processing. 

Unused paths are discarded as new possibilities are calculated. Speculation doesn't 

eliminate options, but, as in any instance of gambling, the process weights the likelihood 

of one path over another in advance of its occurrence. Speculation is a mathematical 

operation unrelated to metaphysics or narrative theory, grounded in probability and 

statistical assessments. Logic-based, and quantitative, the process is pure tecbne, applied 

knowledge, highly crafted, and utterly remote from any notion of poiesis or aesthetic 

expression. Metaphorically, speculation invokes notions of possible worlds spiraling 

outward from every node in the processing chain, vivid as the rings of radio signals in the 

old RKO studios film logo. To a narratologist, the process suggests the garden of forking 

paths, a way to read computing as a tale structured by nodes and branches. 

The phrase "speculative computing" resonates with suggestive possibilities, conjuring 

images of unlikely outcomes and surprise events, imaginative leaps across the circuits 

that comprise the electronic synapses of digital technology. The question that hangs in 

that splendid interval is a fundamental one for many areas of computing application: can 

the logic-based procedures of computational method be used to produce an aesthetic 

provocation? We know, of course, that the logic of computing methods does not in any 

way preclude their being used for illogical ends – or for the processing of information 

that is unsystematic, silly, trivial, or in any other way outside the bounds of logical 

function. Very few fully logical or formally systematic forms of knowledge exist in

human thought beyond those few branches of mathematics or calculation grounded in 

unambiguous procedures. Can speculation engage these formalized models of human 

imagination at the level of computational processing? To include an intuitive site for 

processing subjective interpretation into formal means rather than circumscribing it from 

the outset? If so, what might those outcomes look like and suggest to the humanities 

scholar engaged with the use of digital tools? Does the computer have the capacity to 

generate a provocative aesthetic artifact? 

Speculative computing extends the recognition that interpretation takes place from inside 

a system, rather than from outside. Speculative approaches make it possible for subjective 

interpretation to have a role in shaping the processes, not just the structures, of digital 

humanities. When this occurs, outcomes go beyond descriptive, generative, or predictive 

approaches to become speculative. New knowledge can be created. 

These are big claims. Can they be substantiated? 

Temporal Modeling 1 

Temporal Modeling is a time machine for humanities computing. Not only does it take 

time and the temporal relations inherent in humanities data as its computational and 

aesthetic domain, enabling the production and manipulation of elaborate, subjectively 

inflected timelines, but it also allows its users to intervene in and alter the conventional 

interpretative sequence of visual thinking in digital humanities. 

The Temporal Modeling environment, under ongoing development at SpecLab 

(University of Virginia), embodies a reversal of the increasingly familiar practice of 

generating visualizations algorithmically from marked or structured data, data that have 

already been modeled and made to conform to a logical system. The aesthetic 

provocations Johanna Drucker describes are most typically understood to exist at the 

edges or termini of humanities computing projects. These are the graphs and charts we 

generate from large bodies of data according to strict, pre-defined procedures for 

knowledge representation, and which often enchant us with their ability to reveal hidden 

patterns and augment our understanding of encoded material. They are, however, 

fundamentally static and (as they depend on structured data and defined constraints) 

predictable, and we are hard-pressed to argue that they instantiate any truly new 

perspective on the data they reflect. Why, given the fresh possibilities for graphesis the 

computer affords, should we be content with an after-the-fact analysis of algorithmically 

produced representations alone? Temporal Modeling suggests a new ordering of aesthetic 

provocation, algorithmic process, and hermen-eutic understanding in the work of digital 

humanities, a methodological reversal which makes visualization a procedure rather than 

a product and integrates interpretation into digitization in a concrete way. 

How concrete? The Temporal Modeling tools incorporate an intuitive kind of sketching – 

within a loosely constrained but highly defined visual environment – into the earliest 

phases of content modeling, thereby letting visualization drive the intellectual work of 

data organization and interpretation in the context of temporal relations. Aesthetic

provocation becomes dynamic, part of a complex dialogue in which the user is required 

to respond to visualizations in kind. Response in kind, that is, in the visual language of 

the Temporal Modeling toolset, opens up new ways of thinking about digital objects, 

about the relation of image to information, and about the subjective position of any 

interpreter within a seemingly logical or analytic system. Our chief innovation is the 

translation of user gestures and image-orderings that arise from this iterative dialogue 

into an accurate and expressive XML schema, which can be exported to other systems, 

transformed using XSLT, and even employed as a document type definition (DTD) in 

conventional data-markup practices. The sketching or composition environment in which 

this rich data capture takes place (the Temporal Modeling PlaySpace) is closely wedded 

to a sister-environment, the DisplaySpace. There, we provide a set of filters and 

interactive tools for the manipulation and display of more familiar, algorithmically 

generated visualizations, derivative from PlaySpace schemata or the already-encoded 

data structures of established humanities computing projects. Like the PlaySpace, though, 

the Temporal Modeling DisplaySpace emphasizes the flux and subjectivity common to 

both our human perception of time and our facility for interpretation in the humanities. 

We have not rejected display in favor of the playful engagement our composition 

environment fosters; instead, we hope to show that a new, procedural understanding of 

graphic knowledge enhances and even transfigures visualization in the older modes. 

Our work in building the PlaySpace, with which we began the project in the Summer of 

2001 and which now nears completion, has required a constant articulation of its 

distinction from the DisplaySpace – the implementation of which forms the next phase of 

Temporal Modeling. What quality of appearance or use distinguishes a display tool from 

an editing tool? At their heart, the mechanisms and processes of the PlaySpace are bound 

up in: the positioning of temporal objects (such as events, intervals, and points in time) 

on the axis of a timeline; the labeling of those objects using text, color, size, and quality; 

the relation of objects to specific temporal granularities (the standards by which we mark 

hours, seasons, aeons); and, in complex interaction, the relation of objects to each other. 

Each of these interpretative actions – the specification of objects and orderings, their 

explication and interrelation – additionally involves a practice we designate inflection. 

Inflection is the graphic manifestation of subjective and interpretative positioning toward 

a temporal object or (in a sometimes startling display of warping and adjustment) to a 

region of time. This positioning can be on the part of the prime interpreter, the user of the 

PlaySpace, or inflections can be employed to represent and theorize external 

subjectivities: the implied interpretative standpoint of a character in a work of fiction, for 

instance, or of an historical figure, movement, or Zeitgeist. The energies of the PlaySpace 

are all bound up in enabling understanding through iterative visual construction in an 

editing environment that implies infinite visual breadth and depth. In contrast, the 

DisplaySpace channels energy into iterative visual reflection by providing a responsive, 

richly-layered surface in which subjectivity and inflection in temporal relations are not 

fashioned but may be reconfigured. 

I want to focus here on some specific qualities and tools of Temporal Modeling, 

especially as they relate to the embeddedness of subjectivity, uncertainty, and 

interpretation in every act of representation, which we take as a special province of

speculative computing. Our very process of design self-consciously embodies this 

orientation toward information and software engineering. We make every effort to work 

from imagery as much as from ontology, coupling our research efforts in the philosophy 

and data-driven classification of temporal relations with the intuitive and experimental 

work of artists of whom we asked questions such as: "What does a slow day look like?" 

or "How might you paint anticipation or regret?" As our underlying architecture became 

more stable and we began to assemble a preliminary notation system for temporal objects 

and inflections, we made a practice of asking of each sketch we floated, "What does this 

imply?" and "What relationships might it express?" No visual impulse was dismissed out 

of hand; instead, we retained each evocative image, frequently finding use for it later, 

when our iterative process of development had revealed more about its implications in 

context. 

In this way, the necessity of a special feature of the Temporal Modeling Project was 

impressed on us: a capacity for expansion and adjustment. The objects, actions, and 

relations defined by our schemata and programming are not married inextricably with 

certain graphics and on-screen animations or display modes. Just as we have provided 

tools for captioning and coloring (and the ability to regularize custom-made systems with 

legends and labels), we have also made possible the upload and substitution of user-made 

standard vector graphics (SVG) for the generic notation systems we've devised. This is 

more than mere window-dressing. Our intense methodological emphasis on the 

importance of visual understanding allows the substitution of a single set of graphics 

(representing inflections for, say, mood or foreshadowing) to alter radically the 

statements made possible by Temporal Modeling's loose grammar. Users are invited to 

intervene in the interpretative processes enabled by our tool almost at its root level. 

A similar sensibility governs the output of a session in the PlaySpace environment. 

PlaySpace visualizations consist of objects and inflections in relation to each other and 

(optionally) to the metric of one or more temporal axes. The editing process involves the 

placement and manipulation of these graphics on a series of user-generated, transparent 

layers, which enable groupings and operations on groups (such as zooms, granularity 

adjustments, panning and positioning, simple overlays, and changes in intensity or alphavalue) 

meant to enhance experimentation and iterative information design inside the 

visual field. When the user is satisfied that a particular on-screen configuration represents 

an understanding of his data worth preserving, he may elect to save his work as a model. 

This means that the PlaySpace will remember both the positioning of graphic notations 

on-screen and the underlying data model (in the form of an XML schema) that these 

positions express. This data model can then be exported and used elsewhere or even 

edited outside the PlaySpace and uploaded again for visual application. Most interesting 

is the way in which transparent editing layers function in the definition of PlaySpace 

models. The process of saving a model requires that the user identify those layers 

belonging to a particular, nameable interpretation of his material. This means that a single 

PlaySpace session (which can support the creation of as many layers as hardware 

limitations make feasible) might embed dozens of different interpretative models: some 

of which are radical departures from a norm; some of which differ from each other by a 

small, yet significant, margin; and some of which are old friends, imported into the

PlaySpace from past sessions, from collections of instructional models representing 

conventional understandings of history or fiction, or from the efforts of colleagues 

working in collaboration on research problems in time and temporal relations. A model is 

an interpretative expression of a particular dataset. More importantly, it is what the 

interpreter says it is at any given point in time. We find the flexibility inherent in this 

mode of operation akin to the intuitive and analytical work of the traditional humanities 

at its best. 

Our policies of welcoming (and anticipating) the upload of user-designed graphic 

notation and of enforcing the formalization of interpretative models in the loosest terms 

possible are examples of Temporal Modeling's encouragement of hermeneutic practice in 

computational contexts. In some sense, this practice is still external to the visual 

environment we have built, even as it forms an integral part of the methodology 

Temporal Modeling is designed to reinforce. I wish to close here with a description of a 

new and exciting tool for encoding interpretation and subjectivity within the designed 

Temporal Modeling environment: a mechanism we call the nowslider. 

"Nowsliding" is a neologism for a practice all of us do constantly – on which, in fact, our 

understanding of ourselves and our lives depends. Nowsliding is the subjective 

positioning of the self along a temporal axis and in relation to the points, events, 

intervals, and inflections through which we classify experience and make time 

meaningful. You nowslide when you picture your world at the present moment and, some 

ticks of the clock later, again at another ever-evolving present. You nowslide, too, when 

you imagine and project the future or interpret and recall the past. Our toolset allows a 

graphic literaliza-tion of this subjective positioning and temporal imagining, in the shape 

of configurable, evolving timelines whose content and form at any given "moment" are 

dependent on the position of a sliding icon, representative of the subjective viewpoint. 

Multiple independent or interdependent points of view are possible within the context of 

a single set of data, and the visual quality of nowsliding may be specified in the 

construction of a particular model. 

At present, two display modes for the nowslider are in development. The first is a 

catastrophic mode, in which new axial iterations (or imagined past- and future-lines) 

spring in a tree structure from well-defined instances on a primary temporal axis. In this 

way, PlaySpace users can express the human tendency to re-evaluate the past or make 

predictions about the future in the face of sudden, perspective-altering events. New 

subjective positions on the primary axis of time (and new happenings) can provoke more 

iterations, which do not supplant past imaginings or interpretations, but rather co-exist 

with them, attached as they are to a different temporal locus. In this way, timelines are 

made to bristle with possibility, while still preserving a distinct chronology and single 

path. Our nowsliders also function in a continuous mode – distinct from catastrophism – 

in which past and future iterations fade in and out, change in position or quality, appear 

or disappear, all within the primary axis of the subjective viewpoint. No new lines are 

spawned; instead, this mode presents time as a continuum of interpretation, in which past 

and present are in constant flux and their shape and very content are dependent on the 

interpretative pressure of the now.

Our taking of temporal subjectivity and the shaping force of interpretation as the content 

and overarching theme of the PlaySpace and DisplaySpace environments we have built is 

meant to reinforce the goal of the Temporal Modeling tools and, by extension, of 

speculative computing. Our goal is to place the hermeneut inside a visual and algorithmic 

system, where his or her very presence alters an otherwise mechanistic process at the 

quantum level. Humanists are already skilled at the abstract classification and encoding 

that data modeling requires. We understand algorithmic work and can appreciate the 

transformative and revelatory power of visual and structural deformance. We at least 

think we know what to do with a picture or a graph. What we haven't yet tried in a 

rigorous and systematic way is the injection of the subjective positioning any act of 

interpretation both requires and embodies into a computational, self-consciously visual 

environment. If speculative computing has a contribution to make to the methods and 

outcomes of digital humanities, this is it. 

See also Chapter 16: Marking Texts of Many Dimensions; Chapter 13: How the 

Computer Works 

Note 

1 This section was co-authored with Bethany Nowviskie. 


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30. 

Robotic Poetics 

William Winder 

Robots 

There is a fundamental link between language and robots. Whatever material constitution 

they may have – arms and legs, cogs and wheels, and engines – the crucial ingredients, 

the ones that separate robots from wrenches, are their instructions. Instructions are 

perhaps inscribed in some concrete medium, but they are quintessentially abstract, 

existing more in the netherworld of code than in concrete form. That code netherworld is 

where humans and robots inevitably meet. It is where humans are most robotic and where 

robots are most human. 

Such was Turing's reasoning that led him to choose the test of dialogue to detect human 

intelligence. It is no coincidence that a computer scientist of Turing's stature might come 

to the same conclusion as such foundational thinkers as C. S. Peirce and F. de Saussure. 

Each in his own way took the axiomatic position that there is no thought without signs, 

and Peirce, at least, comes to the conclusion that a person is in fact fundamentally a sign 

– and so too, though inversely, for robots, which are best described as personified signs. 

Robots are instructions that take human form. 

Mechanized writing has been at the center of philosophical debate as far back as the 

invention of writing itself, but the humanists' version of the Turing test is relatively 

recent. It is, of course, not the conversational game between man and machine Turing 

described, but rather a literary version of the Turing test, where the goal is to simulate the 

author (see Kurzweil 2002, for a practical example). There is no doubt that mechanical 

processes underlie literary texts (Zholkovsky 1984: 53) and they have been used 

sporadically throughout history as an explicit device of creation (Swift's literary engine, 

the Surrealists' cadavre exquis, Bryon Gyson and Burroughs's cut-ups). Yet only recently 

have mechanical processes become the principal focus of a literary movement, in the 

form of the (largely French) group OULIPO (ouvoir de littérature potentielle – Workshop 

for Potential Literature), which by a plagiat par anticipation (anticipatory plagiarism) 

took computational constraints as the source of their creations. 

Let us call robotic poetics (RP hereafter) the study of creative writing done by robots: 

Definition 1: RP is the study of robotic authors and the automatic generation of creative 

texts.

RP has as many facets as automatic writing has dimensions (see Fournel 1986). 

Generated text has a robotic author, itself created by a human programmer. There is a 

poetics of creating that author (here creativity lies in the writing of instructions); a poetics 

of generating a specific text (how those instructions play out to make a given text); and a 

poetics of reading generated literature (how the reader will read a text knowing, or not, 

that it is automatically generated). We simply do not read a generated text the same way 

we read a one-off hand-crafted text. We read in the shadow of textual variants, those 

sibling texts that could have been generated just as easily: generated texts come in 

swarms. 

We will consider here RP as the field that covers all these facets of robotic creation, 

without narrowing the field to a single aspect. We will talk about robotic authors (the 

program that generates), robotic texts (generated texts), text engineers (those who 

program), and so on. We will perhaps focus narrowly here on creative natural language 

texts, but that should not be taken as RP's natural limit. What is central to RP is not text 

(which in any case should be taken as multimedia text), but rather printing, the central 

metaphor for any action a robot might take. Literally any medium can be printed on or 

imprinted. Thus CAD (computer-aided design) and CNC (computer numeric control) are 

used in factories to "print" machine parts. What is central is the way the texts of 

instructions are automatically transmuted into something other, sometimes other texts, 

sometimes things. 

Textual generation systems have many applications in the humanities. Lessard and 

Levison offer a good example of the range of applications. Their generation system, Vinci 

(see below), has been used for foreign-language exercise generation, simulation of 

oulipian constraints, teaching linguistics, punning and humor, word and story generation, 

among others. (See their site for documentation, download, and an extensive publication 

list.) 

Two red threads that RP follows are human creativity and combinatory practices. These 

two dimensions are oddly opposed, yet intertwined. A combinatory can be systematic or 

random, but its value is inevitably placed second to what is creative. A roll of the dice is 

not a creative act, since the outcome is within a foreseeable range of events. It is a 

distinctly human ability to creatively define the possible, to envisage possibilities outside 

the foreseeable. And yet we too are combinatory creatures. As Saussure and Peirce 

underline, because we think and live through signs, we must be signs (argues Peirce). 

What are signs? Nothing if not combinatory conspiratorialness. 

Let us formulate a second possible definition of the field that covers the combinatorial 

dimension of robots: 

Definition 2:, RP is humanities combinatorial studies. 

RP is an interdisciplinary, interstitial field – a field that takes on a certain consistency in 

the overlap of more clearly defined disciplines such as cognitive science and 

computational linguistics (Anis 1995). That interdiscplinarity comes naturally through

computers, which are by nature interdisciplinary machines: anything can be digitized; 

everything – so it would seem – can be virtualized (see chapter 28, this volume) and thus 

converge in the computer. 

Potential and Real Texts 

Poetics is traditionally conceived as the science of message construction, a systematic 

study of texts that describes why and how they make sense or have a particular effect. 

Even in this traditional vein, poetics describes text generation, the ergonomics of texts 

and the aesthetics of combinations. RP pushes the notion one step further in that it studies 

what creates the mechanics of creativity. What is central in RP is the virtualized (or 

potentia-lized) dimension of a work of art; understanding virtual texts tells us much about 

real texts and their creative core. A robotic author is precisely the site of a corpus of 

virtual texts. 

Pierre Lévy (1998: 141–2) contrasts the virtual with the potential, the actual, and the real 

(see Figure 30.1). The north/south planes oppose what is inert (substances) and what is 

dynamic (events); the east/west, what is unrealized (potential, virtual) and realized (real, 

actual). The movement from north to south is described as "jectification" – transforming 

what is fixed into a dynamic interaction between subjectified objects (money and 

language, for example, exist only through our collective subjectivity) and objectified 

subjects (the self is constructed by concrete choices of technological solution to problems 

– the subject can choose to be a blond or a brunette). The movement in the opposite 

direction, south to north, is towards the fixation of predefined structures. 

A real text is a text instance that derives its primary value from its simple existence. It has 

a secondary value in that it can also be "reused" through potentialization to enhance a 

potential text, i.e. a range of possible texts. Transducer bots (my neologism; see below), 

which scramble real texts to form new texts, are a clear example of potentialization of 

real texts. 

A potential text represents a set of real texts, each of which is created through the reader's 

choice. Queneau's Cent mille milliards de poemes (One hundred trillion poems) is the 

classic example of this kind of text. For each of the 14 lines of a sonnet, Queneau offers 

10 substitute or variant lines. Since each of the 14 lines has 10 possible realizations and 

any combination of these choices is "poetically grammatical", the number of possible 

poems is 10 14 , or one hundred trillion. 

This (14 by 10) combinatorial poem is printed in book form. The book looks as if it has 

10 pages with 1 poem per page. However, the pages of this book are cut horizontally 

between the verses so that each verse can be turned separately. By turning partial pages in 

this way, verses on the pages below can be successively revealed and each distinct 

combinatory sonnet can be brought to the fore (see Figure 30.2).

According to Queneau's calculations one would have to read 24 hours a day for 200 

millennia to read all the combinations; this is clearly a work that no one could ever 

possibly read. 

Queneau's text shows us how, in Lévy's terms, the potential is transformed into the real. 

On the other hand a transducer bot represents the potentialization of a real text. 

Transducers take as input any text and produce another text, using various transcoding 

procedures. One example is the Shannonizer, which more or less randomly rearranges 

input text and intersperses it with canned expressions. The Shannonizer will transform 

this text (edited by a robotic Dr Seuss): 

Windows XP delivers a host of great features, including improved reliability and security, 

along with great experiences in real-time communications, mobile support, home 

networking, digital photos, digital video, and digital music. As a result of these higher 

standards, you may see some compatibility issues with your computer systems and 

software programs. In most cases, however, you can expect those problems to be at an 

absolute minimum. Here's why. 

(Windows XP "Compatibility", Internet advertisement) 

into this text: 

Windows XP delivers as edited by Dr Seuss 

Windows XP delivers a host of great features, sir. Thank goodness I'm a result of sour 

gooseberry jam! Just look! No, digital photos, home networking, you may see some 

compatibility issues with great experiences in their paddles in real-time communications, 

we serve hot dogs, along with your computer systems and the dark. Take a star, funny 

things are everywhere. In most cases, however, along with great experiences in real-time 

communications, not one little bed all night. 

(The Shannonizer transforms a Windows XP advertisement) 

The Shannonizer potentializes in that it recreates new texts from old. A more 

sophisticated potentializer is Kurzweil's poetry generator ("Cyberart Technologies"), 

which "recycles" the poetry of any given corpus. (As Balpe remarks, poetry is the easiest 

genre to simulate – See Balpe and Magné 1991: 129.) 

Games, Dialogue, and Texts 

A potential text represents a field of possibilities defined by the text engineer. A given 

realization of the text is simply a selection, a roll of the dice, by the user. Potential texts 

are textual kaleidoscopes and the basis of such ancient textual games as Tarot, I Ching, or 

horoscope predictions.

There is a great range of potential texts, and many possible classifications (for example, 

Aarseth 1997: 58ff). The following represents a very simple classification with some 

prototypical examples: 

1 Combinatory texts or textual kaleidoscopes, such as hypertext, which generate 

variants or allow the reader to select variants: mail merge in word processors or 

Queneau's sonnets. 

2 MUDs (multi-user domains), which generate and manage a virtual universe for the 

interaction of several users. (See chapter 28, this volume.) 

3 Transducer bots, which rearrange text: the Shannonizer or Kurzweil's poetry engine. 

4 Generators, such as Vinci (Lessard and Levison 2002) or KPML (Reiter 2002). 

5 Chatbots or Eliza variants, such as ALICE, which have Turing-style dialogues with 

the user. 

Such texts are at the same time text, game, and dialogue. Queneau's text requires that the 

reader select a poem by turning to the appropriate verses. The choice is not determined in 

advance, though the field of possibilities is. Games are in general dialogues between two 

or more players, though in some games, such as solitaire, the second player is reduced to 

the mechanical combination that resists – the shuffle that gives the distribution of cards 

that the solitaire player must "beat." 

"Playtexts" (Motte 1995) have peculiar properties, though all can be represented as a 

sequence of signs, just as chess games have a notation for each move of a game. Lewis 

Carroll will thus reproduce the chess problem that informed Alice in Wonderland in the 

preface to the text itself. Chess offers the example of a now highly computerized game in 

which the robotic player routinely outstrips its designers (Aarseth 1997: 27). Whether 

authorship will be automated to the same degree remains a question. It is nevertheless 

useful at this point to consider more closely the general structure of games. 

A chess game is a combinatory, but its potential nature is distributed in many layers of 

increasing complexity. A chess piece has no pertinent parts, but a board with its 

distribution of pieces does. Even so, we can just as easily consider each possible board an 

unanalyzed "piece" of a larger construct, the sequence of board configurations that make 

up a given game. We will recognize three very broad, relative layers of play: pre-play, 

play, and post-play. 

Pre-play (syntactic level) concerns the mastering of the fundamental rules of the game 

which define the playing field. A player who could in some way visualize all the possible 

board configurations would completely master this level. Even so, he or she would not 

necessarily win a single game, even against someone who could not visualize the entire 

field of possibilities.

Play (semantic level) concerns the relative value of a move and its fundamental effect or 

consequences. The players' vying is a kind of dialogue at a higher combinatorial level 

than pre-play, though it presupposes pre-play vying in the sense that each player is more 

or less expert at seeing potential moves. Play adds a layer of meaning since the choice of 

a complete game must be shared with the opponent. At the same time, play includes an 

unlimited number of possible sub-games. For example, in chess, the opening play is of 

itself a sub-game, as is the end game. Yet all the sub-games blend with the general goal 

of winning. 

Post-play (pragmatic level) concerns aesthetic sensibility; it is not the simple winning or 

losing of the game, but rather the way one wins or loses that becomes the game. Post-play 

can make the game mean just about anything: for example, out of deference, one player 

might seek to lose, perhaps to encourage the other player. At the post-play level the game 

opens on to any number of further games. 

Abstractly, the pre-play level in chess is always the same. However, a real player must 

devise a set of rules for seeing pre-play and play potential, and those heuristic rules can 

change and indeed should change as the player improves. At the moment, it would seem 

that programmers of chess machines, which do great things through brute search 

algorithms, have not yet addressed this further level of chess prowess, that rule-changing 

creativity that allows players to improve or, while playing chess, to play more than just 

chess. 

Reading a playtext involves vying at all these levels. Potential texts are like solitaire: they 

are only worthy adversaries at the pre-play level, or perhaps at the play level. The game 

stops with a win or loss for the computer. In human interaction, the game can be 

transformed into just about any game the players wish; it becomes mostly post-play, or 

higher. That facile movement to higher levels of play is the ultimate focus of RP, both for 

understanding what robotic texts mean and how to build them. 

Virtual and Actualized Texts 

Many features not clearly associated with traditional print literature come to the forefront 

in the robotic text. All texts are minimally interactive, but a robotic text has concrete 

structures that are designed to maintain a more human-like feedback loop with the reader. 

Many special functions, while possible with a paper text, become central to the ethos of a 

robotic text. Clearly, time is one parameter that can be used in a novel way. Thus Fournel 

contrasts the computer implementation of Queneau's sonnets with the printed book: 

The printed collection [of Queneau's poems] is prettily conceived, but the manipulation 

of the strips on which each verse is printed is sometimes tedious. The computer, though, 

makes a selection in the corpus in function of the length of the "reader's" name and the 

time which he takes to type it into the terminal, then prints the sonnet, which bears the 

double signature of Queneau and his reader. 

(Fournel 1986: 140)

This subjectification, as Lévy calls it, characterizes the move towards the virtual text, (see 

Figure 30.1). Yet moving from the potential to the virtual is not simply a question of 

degree. Both virtual and potential texts are alike in that both are equally unreadable (as 

Queneau remarks above). Virtual texts have however a kind of open-ended creativity that 

would ultimately define success under the Turing test. A computerized version of 

Queneau's text simply does not have that degree of intelligence. Today's systems only 

approach asymptotically the virtual text, which remains a challenge. Even so, many 

potential texts seem to "simulate" virtual texts (!). 

The virtual text remains the ultimate object of RP. How does one instill a robot with 

creative subjectivity? There are certainly many tantalizing steps that seem to inch the 

potential text towards the virtual. The single most common step is to borrow the reader's 

subjectivity by implicating him or her in the construction of the text. This transfer of 

subjectivity is no doubt the source of the Eliza effect: readers fill in what the computer 

leaves blank. Though such sleights of hand concerning subjectivity hybridize (reader and 

text) rather than virtualize, they do offer a tantalizing mirage of what RP seeks: 

Definition 3:, RP is the poetics of virtualization and virtual text. 

At present, the only obvious path to virtualization is by degrees, through a dogged 

expansion of the computer's generative potential using ever more powerful techniques - 

such was the evolution of computer chess. It is useful therefore to look at some concrete 

examples of text generation and follow how the generative power of applications can be 

expanded through progressive generalization. 

Generative Applications: Prolog and the Tigerbil 

There are many technical dimensions associated with a generation project, and a 

confusing diversity of applications. Very little can be called standard in this area. Anyone 

wishing to become a text engineer and actually generate text will face a number of 

practical choices (operating systems, programming environment, grammar formalism, 

lexicon format, etc.), many of which have considerable theoretical implications. 

One of the major stumbling blocks in the field is the short life cycle of any software. 

Publications in computer-generated literature included, as late as 1986, pages of poorly 

printed code in DOS BASIC and were fairly unreadable. Transcribing code from books 

was extremely error-prone, especially in the unfriendly DOS BASIC environment. It 

would seem that pseudo code and abstract structures are the best way to approach any 

presentation of this rapidly evolving field. Danlos's use of pseudo-code in her Linguistic 

Basis of Text Generation (Danlos 1987) makes it more accessible today. More recent 

texts on text generation (Reiter and Dale 2000), grammar formalism (Butt et al. 1989), or 

computational linguistics (Gazdar and Mellish 1989; Jurafsky and Martin 2000) use 

higher-level programming languages and grammar formalisms. (See SIGGEN 2002 and 

Calder 2002 for recent publications on generation.)

And yet the heart of generation lies in the code, because it is in the code that virtual 

structures are condensed into single-step operations – the code becomes the text 

engineer's principal language. A simple example, automating Lewis Carroll's 

portmanteau words, should suffice to illustrate, in a very minimal way, both the problems 

and powers associated with a given programming environment. 

Let us program a system that will create portmanteau words by combining words whose 

spelling overlaps. That is, one word will end with the same letters with which the other 

word begins. To investigate that relation over a given set of words (the size of the set 

being almost immaterial for the computer), one must exhaustively compare every 

possible pair of words in the set. Each word must be decomposed into all its possible 

front sequences and corresponding end sequences. For example, "tiger" and "gerbil" must 

be split into: t iger, ti ger, tig er, tige r, tiger / g erbil, ge rbil, ger bil, gerb il, gerbi l, 

gerbil. Then each front of one must be compared to the ends of the other and vice versa to 

check for (the longest) match: tigerbil. 

In a high-level language like Prolog, built-in programming constructs allow the 

specification of that splitting process to be very detailed and yet concise. The following 

code (Coelho et al. 1982: 18) uses a small set of French names of animals. It prints out: 

alligatortue, caribours, chevalligator, chevalapin, vacheval. (Some overlaps in English are 

beelephant, birdog, birdonkey, birdove, birduck, camelephant, camelion, catiger, 

caturkey, chickite, cowl, cowolf, crowl, crowolf, cuckoowl, dogecko, dogibbon, dogoat, 

dogoose, dovelephant, duckite, elephantiger, elephanturkey, frogecko, frogibbon, frogoat, 

frogoose, geckowl, goatiger, goaturkey, gooselephant, horselephant, jackalion, 

kitelephant, mouselephant, owlion, pigecko, pigibbon, pigoat, pigoose, roosterooster, 

sheepig, sheepigeon, snakelephant, tigerooster, wolfox, wolfrog. Of these, only 

camelephant and crowl overlap by more than one letter.) The actual core of the algorithm 

is only in the append and mutation routines; the others just deal with data representation 

and display. On most Prolog systems, append would be a built-in function, and so the 

programmer would only design mutation.

We can explain the mutation algorithm as shown in Table 30.1. The append code will try 

all the possible ways to split a list, just as the animal code will form all possible pairs of 

animals' names. The search for possible combinations (through the backtracking 

function) is a built-in dimension of any procedure and gives Prolog its specific character 

as a programming language. In fact, the programming issue in Prolog is really how to 

contain the search for combinations. Thus, in the list for English overlaps, we find 

"roosterooster"; since we did not specify explicitly that Y and Z should not be the same, 

that combination was produced along with the others. 

AI programming languages such as Lisp and Prolog come closest to a pseudo-code for 

generation. Unfortunately, considerable time and energy is required to master the power 

and concision of a high-level programming language. It is clear nonetheless that if these 

languages are preferred for AI applications, it is precisely because they represent an 

important step towards the virtual. Algorithms must ultimately stand on each other's 

shoulders, as mutation's use of append shows. 

Many generation systems are end-user applications which require no programming at all, 

but they do not allow any extension of virtualization (Kurzweil's poetry generator or the 

Shannonizer). Others are quite sophisticated specifications, such as ALICE and A/ML (AI 

XML), but do not deal in any direct way with crucial linguistic dimensions. Grammar

management packages are by definition still closer to questions of language generation 

(see the NLP Software Registry); some, such as KPML (Reiter 2002; Reiter and Dale 

2000) or Vinci (Lessard and Levison 2002) are explicitly conceived for generation. In all 

cases, the real question is how to make the programming language increasingly highlevel 

and expressive; grammar specification and programming are increasingly fused. For 

the moment, no metalanguage would allow the programmer to code a portmanteau 

generator without specifying in a very detailed manner character-string manipulations. 

30.1 Table The mutation algorithm 

mutation(X):- Try to make an X (a word) with the following properties: 

animal(Y), 

animal(Z), 

append(Y1,Y2,Y), 

Y1 =[], 

append(Y2,Z2,Z), 

Z2/= [], 

append(Y1,Z,X). 

To find that X, you will first have find two animals (whose names 

have been split into lists of characters), Y and Z. (Finding two 

candidate animals will be the job of code defined elsewhere, in the 

animal definition.) 

Divide animal Y into a front part (Y1) and an end part (Y2). 

Make sure that the end part (Y2) is not the whole word, which 

would be the case should Y1 be the empty list. 

Divide the other animal, Z, into two parts such that the front is Y2, 

i.e., the same end found in the Y animal. 

Again, make sure that there is some overlap; 

Y2 should not be empty. 

Splice together Y1 (front of Y), with whole of Z to give X, the 

portmanteau we seek. 

Generalizing Generation: Grammar Levels 

A generator is fundamentally a mail merge function, as is found in most word processors. 

A template for a merge has blank spots that are filled in using a database of content items. 

For example, from the sentence "Jack finds Jill irresistible", we can construct the 

template: " finds ".

With that data a combinatorial generation will produce (6 * 6 * 6 =) 216 sentences having 

the same general form as the original. As can be seen, the combinations depend on the 

distinction between tag classes: the set is used twice, the set once. 

We might wish to refine our set of categories to distinguish between different aspects of 

the content items.

The template " finds " restricts the 

combinatory to (3 * 3 * 2 =) 18 combinations. Other restrictions might be of the logical 

sort, for example excluding the (schizophrenic!) cases where the first and second person 

is the same person: " finds ". Variable A will 

be instantiated randomly and ♭A will be chosen randomly as well, but excluding 

whoever was chosen for A. 

Sentence grammars: context-free grammars 

Advanced generators differ from mail merge only in generality, i.e., advanced systems 

construct the most general descriptions possible of all dimensions of the text. 

Unfortunately, as structures become more general, the metalanguage and the control of 

generation become increasingly complex. The grammar of that seemingly simple 

sentence is not simple if considered as part of English grammar in general. A more 

general description of the sentence requires a framework for grammar description that is 

ultimately similar to a programming language. There is often a direct translation between 

programming constructs and grammar metalanguage. Prolog has in fact a built-in 

grammar description language called definite clause grammars (DCGs), which are 

essentially context-free grammars (CFGs) (see Jurafsky and Martin 2000 for this and 

other formalisms). A CFG of the template " 

" is: 

Each rule represents a step in the description of a possible sentence. What is to the left of 

the arrow is rewritten in the form to the right. When generating a sentence, the 

substitution process continues until all the categories (left-hand side of the arrow) have

een rewritten into words (terminal entries, which are not on the left-hand side of any 

rewrite rule). Diagrammatically a given sentence is a network of rewrites (see Figure 

30.3). This "stacking up" of grammatical categories in a tree form is called a phrasestructure 

tree diagram. 

Distinguishes (attributes) 

If our generative needs were limited to this one type of sentence, this simple CFG would 

be sufficient: from the database of possible nouns, adjectives, and verbs, it could generate 

all possible combinations. However, as it is, this grammar is too specific to be reused as a 

component of a larger grammar; it only describes the very specific behavior of attributive 

verbs (verbs whose adjective complement is attributed to the direct object complement). 

Other sentences, such as the simple transitive sentence "Jack sees Jill", do not fit this 

framework: "Jack sees Jill irresistible" is not grammatical. 

We can generalize our grammar fairly simply by adding subclassifications to the main 

categories. We want to limit the VP to a particular subset of verbs, those that are 

attributive, and expand the grammar to allow for simple transitive constructions: 

30.3 Figure A phrase-structure tree diagram 

This is a more general grammar since it subcategorizes V so that we can distinguish 

between attributive and simple transitive verbs. (In Vinci, described below, distinguishers 

are called attributes.) Other distinguishers could be added to control various kinds of 

agreement between categories, such as number agreement (subject-verb). We will add the 

case of a plural subject:

The first rule states that the value of the number distinguisher, NUM, must be the same in 

both the VP and the NP. That value will be assigned in the rewrite rules that follow it: 

when the NP subject is finally chosen, the value of the number distinguisher will be 

transferred, through NUM, to VP and will constrain the choices from that node down the 

tree to the selection of the verb. 

Word grammars: morphology 

The generalizations we have made so far are generalizing extensions to the grammar; 

they allow for other possible sentences, or variants of the original. Another generalization 

concerns what was originally taken as the data items of the merge: the words themselves 

have their own grammar, their morphology. Most English verbs have a simple 

morphology, just a choice between "s" and "es" in the third person: 

Our morphological rules are more complex than in the previous CFG, but now they are 

more general: adding new verbs to the database requires adding only the infinitives to one 

of the four classes, rather than adding two inflected forms in different rules.

Text grammars: metagrammars 

All grammar might in principle be integrated into a CF sentence grammar. Word 

morphology becomes a module of a sentence grammar, and a text of whatever length can 

be transformed into a single sentence. Yet grammars traditionally describe separately the 

word, sentence, and textual levels. Text grammars bring with them a number of difficult 

issues. Minimally, a text is two or more sentences that cohere. That coherence expresses 

itself in many ways. 

The sentence generator we have described so far could easily generate two sentences to 

make a text: "Jack finds Jill irresistible. Jack finds Jill irrepressible." To add a textual 

level to the grammar, it is sufficient to add the rules: 

This describes minimally a text as two sentences, but it does not generate a very natural 

one. We would prefer our first text to be rendered as: "Jack finds Jill irresistible and 

irrepressible." Furthermore, if we generated "Jack finds Jill irresistible. Jack finds Jill 

irritating", we would prefer to have "Jack finds Jill irresistible yet irritating." 

This process of removing repetitive structures, expressing the underlying logical 

relations, and making a natural synthesis of the information is called aggregation. In the 

first case, it is simply a question of suppressing the start of the second sentence and 

adding "and", but to accomplish that in a general fashion, we would need to understand 

how the two sentences compare in their structure. Not all sentences that end in adjectives 

can be conflated in this same way. (We cannot conflate "Jack finds Jill irresistible" and 

"Jack is irrepressible.") Suppression of the first part of the sentence and coordination of 

the adjectives is possible because each adjective is in exactly the same syntactic 

environment. 

Textual grammars are meta or control grammars; they direct the generation of sentences. 

Descriptions at this level are obviously quite complex since they are twice removed from 

an actual sentence. A control grammar would not normally attempt to reorganize what is 

already generated. Comparing and manipulating two parse trees would be a very complex 

process. Rather, typically it is more straightforward to allow the control grammar to build 

a plan of the text and then to select the grammatical resources needed to fulfill the plan. 

We might represent the plan or semantic skeleton of our text using predicate calculus. In 

this small grammar, each verb would have a general semantic description. One class of 

attributive verbs expresses the fact that the subject, X, believes that the object 

complement, Y, possesses a certain quality, Z: belief(X, is(Y, Z)). The predicate "belief" 

reflects the modal part of the fundamental meaning behind "finds", "considers", or

"believes"; "is", the attributive part. If our text is defined by two facts of this type, the 

rules of predicate logic will allow us to understand how they can be combined. 

From belief(X, is(Y,Z)) and belief(X, is(Y,W)) we can deduce (using logical 

transformations) that belief(X,&(is(Y,Z),is(Y,W))). From that expression, we simplify to 

belief(X, is(Y,&(Z,W))). That is the general plan for our text, derived by transformation of 

formulas that preserve meaning. Our grammar would then have to take that meaning and 

translate it into the appropriate grammatical structures and finally into words. 

Vinci 1 

To understand how control grammars work, we will consider in more detail a small 

fragment of a story generator coded in Vinci, the generation system designed by Lessard 

and Levison. 

Vinci supports all the grammar coding structures we have discussed so far: CFGs, 

inheritance of distinguishers (attributes), sophisticated morphological generation, and 

much more. The feature that will concern us here is their PRESELECT, which plays the 

role of a sentence planner. This system does not (yet) have a text planner as such, but the 

sentence planner does give us insight into how a full-blown text planner would work. 

(KPML is a more complete implementation on this point, but considerably more complex. 

See Reiter and Dale 2000.) 

The following is a Vinci-generated story designed by Lessard. It is presented here divided 

according to the Proppian narrative functions that served as its generative framework (an 

approach that is common since Klein et al. 1977): 

A Vinci-generated fairy tale 

(present king) _Il était une fois un roi (Once upon a time there was a king) 

(name king) qui s'appelait Pierre, (named Pierre.) 

_Il était beau, intelligent, bon, et il avait des cheveux roux. (He was 

(describe king) 

handsome, intelligent, good, and he had red hair.) 

(present 

_Il avait une enfant, la princesse. (He had a child, the princess.) 

victim) 

(describe 

victim) 

(make 

interdiction) 

_Elle était belle, intelligente, forte, bonne, et elle avait des cheveux 

blonds. (She was beautiful, intelligent, strong, good, and she had blond 

hair.) 

_Le roi ordonna a la princesse de rester au chateau. (The king ordered the 

princess to stay in the castle.) 

(disobey) _Elle désobéit quand meme. (She disobeyed nevertheless.) 

(leave) _Elle alia dans la forêt. (She went into the forest.)

(encounter) _où elle rencontra un sorcier. (where she met a sorcerer.) 

(name villain) _Il s'appelait Merloc. (His name was Merloc.) 

(describe _Il était laid, intelligent, fort, mechant, et il avait des cheveux blonds. (He 

villain) was ugly, intelligent, strong, wicked, and he had blond hair.) 

(kidnap) _Il l'enleva. (He kidnapped her.) 

(ask) 

_Pour la sauver, le roi demanda l'aide d'un prince. (To save her, the king 

asked a prince for help.) 

_Heureusement, le prince rencontra un lutin qui lui fournit une grenouille 

(give) magique. (Luckily, the prince met an elf who provided him with a magic 

frog.) 

(kill) 

_Le prince l'utilisa pour tuer le sorcier. (The prince used it to kill the 

sorcerer.) 

(marry) 

_Il épousa la princesse et ils eurent beaucoup d'enfants. (He married the 

princess and they had many children.) 

This story requires a number of support files and a separate grammar for each narrative 

function. The main files that concern us here are: 

1 The lexicon, where words are stored with their attributes and indications for 

generating morphological variants. The lexicon, and the grammar in the user file, is 

expandable. 

2 The attribute file, which defines the attribute categories and the values of each 

attribute (which we called distinguishers in the previous sections). For example, in the 

Genre category (gender) there will be two attribute values, masc and fém. 

3 The user file, which is the main grammar file where the CFGs are found. 

Without any preselection, the system uses whatever grammar has been defined in the user 

file and generates random output. The PRESELECT, along with the SELECT function, 

controls how the grammar will generate. For example, we will want certain verbs to have 

certain nouns as subjects, and not others (e.g., "It is raining" is acceptable; "Paul is 

raining" is not). In this fairy tale, each narrative function concerns the sphere of action of 

a given subject and that subject must be the focus throughout the function. Attributes, the 

SELECT, and the PRESELECT functions of Vinci allow us to describe such constraints. 

Random generation will happen within the boundaries defined by these structures. 

Table 30.2 contains a detailed commentary of the user file for the second function, the 

"Name the king" function, produces the output "qui s'appelait X" (who is called X). 

Grammar control is achieved in Vinci principally through controlling the flow of 

attributes in the phrase structure trees and through loading and unloading grammar 

modules in user files. This is a simulation of grammar control, but it is clear that in a full 

implementation of a metagrammar, the grammar rules themselves become the objects of 

manipulation and take on the same status, but at a higher level, that words have in a

sentence grammar. To correctly generate sentences in a general manner, we had to 

develop a complex system of grammatical categories and attributes. A metagrammar 

must in the same way develop the descriptive language, not to describe sentences, but to 

describe what describes sentences: the CFG rules. 

In short, the most logical way to generate text under Vinci would be to use two Vincis: 

one to (randomly) generate a constrained grammar and the second to use that grammar to 

randomly generate the final text. The PRESELECT and the SELECT rules represent a 

foreshadowing of that ultimate metagrammar. 

30.2 Table The "name the king" function 

PRESELECT = roi 

N[sing]/"roi" 

ROOT = NOMMER[roi] 

NOMMER = INHERIT Ps: 

Personnage; SELECT Ge: 

Genre in Ps, No: Nombre in 

Ps, Mo: Moralite in Ps; 

PRON[pronrel, suj, clit] 

PRON[pronref, p3, No] 

According to this PRESELECT instruction, the system will 

first choose a singular N (noun) whose lexical entry is "roi." 

"Roi" is defined in the lexicon as: "roi"|N|masc, Nombre, 

Determ, humain, male, roux, bon ("king"|N|masc, Number, 

Determiner, human, male, red-haired, good). 

This preselected lexical item will be used in the SELECT 

rule below. (Note that the "|" is a field delimiter in the 

dictionary entries.) 

The highest node is always ROOT. For this generation, its 

only child node is NOMMER with the semantic attribute 

roi. For generating "Name the villain", NOMMER is given 

attribute scelerat (villain) instead. 

In creating the daughter nodes for NOMMER as described 

below, we first inherit its Personnage attribute, in this case 

roi, giving this value to the variable Ps. Through Ps, we 

then access the lexical item preselected for roi (i.e., "roi") to 

set up three variables (Ge, No, Mo). Notice that "roi" is 

masc, and has the attribute bon (good) taken from the 

Moralite category. The preselection has also been 

designated sing. 

To summarize the transfer of attributes: the lexical entry 

"roi" gives its attributes to the preselection labelled roi, one 

of the Personnage attributes defined in the attributes file. 

The NOMMER node has roi as its Personnage attribute, 

which is inherited under the name of Ps. Through Ps, the 

variables Ge, No, and Mo obtain values which are used to 

select appropriate PRON and N words. 

"qui": the attributes select the subject relative pronoun in a 

clitic construction 

"se": a reflexive pronoun, 3rd person, with number (sing) 

defined in the PRESELECT. Morphological rules of this

entry deal with the elision of "e." 

V[vpron, imparf, p3, "appelait": the imperfect tense, 3rd person singular, of the 

No]/"appeler" 

verb "appeler." 

"Pierre" is a possible N[prp] (prp = proper noun) choice. 

His entry in the lexicon is: "Pierre"|N|prp, masc, sing, 

humain, Cheveux, Obeissance, Apparence, Force, Moralite, 

Intelligence." Of course, we do not know whether Pierre is 

actually bon – he is not defined as such in the lexicon. He 

N[prp, Mo, Ge, No] does however possess the attribute category Moralite, and 

that is sufficient for him to qualify as a potential roi. On the 

other hand, "Merloc", another N[prp], will not be chosen 

from the lexicon since his Mo does not match bon (he is 

méchant!: "Merloc"|N|prp, masc, sing, humain, Cheveux, 

Apparence, Force, mechant, Intelligence. 

PONCT[pt] Then a period. 

Definition 4, RP is the study of abstraction and meta processes. 

Art and Virtuality 

We have outlined a seemingly new area of study called robotic poetics. We have not tried 

to define all its dimensions or to subdivide it in any way. Rather we have focused on a 

number of interwoven problematics that seem to concern any form RP might take in the 

future: mechanization, combinatory, virtualization, and abstraction towards 

metastructures. There are many formalist poetics that have dealt with some of these 

issues in the past, and some are now being adapted to the new computational context 

(enterprises such as Rastier's interpretative semantics or Zholkovsky's poetics of 

expressiveness). However, RP's most pivotal shift in emphasis lies in the confluence of 

traditionally separate fields of interest: generation merges with analysis, theory with 

practice, creativity with criticism. 

Whatever the techniques that ultimately come to inform RP, the field is inevitably 

structured by the romantic image of a creative artificial intelligence. Winograd (1990) 

discusses that image in a practical manner. In his Count Zero (1986) William Gibson 

presents perhaps a more powerful literary description of RP's idealized object. One strand 

of the intrigue focuses on Marly Krushkhova, an art dealer who has been hired by Joseph 

Virek to track down the author of beautiful, shoebox-sized found-object sculptures. Her 

employer is astronomically wealthy, but now an immense blob of cancerous cells 

maintained in a vat at a Stockholm laboratory. Virek wants to transfer his mind out of the 

vat and into an AI construct. He believes that the artist Marly seeks – an AI construct – 

holds the key to his transmogrification. Near the end of the novel Marly travels to an 

abandoned space station to meet the artist. She arrives in the weightless domed room 

where the robot is housed. Its arms stir the floating space debris:

There were dozens of the arms, manipulators, tipped with pliers, hexdrivers, knives, a 

subminiature circular saw, a dentist's drill…. They bristled from the alloy thorax of what 

must once have been a construction remote, the sort of unmanned, semiautonomous 

device she knew from childhood videos of the high frontier. But this one was welded into 

the apex of the dome, its sides fused with the fabric of the Place, and hundreds of cables 

and optic lines snaked across the geodesies to enter it. Two of the arms, tipped with 

delicate force-feedback devices, were extended; the soft pads cradled an unfinished box. 

(Gibson 1986: 217) 

The debris, from which the mechanical arms extract components and fashion its box 

sculptures, is composed of personal belongings abandoned by a family of now decadent 

AI pioneers: 

A yellowing kid glove, the faceted crystal stopper from some vial of vanished perfume, 

an armless doll with a face of French porcelain, a fat gold-fitted black fountain pen, 

rectangular segments of perf board, the crimpled red and green snake of a silk cravat…. 

Endless, the slow swarm, the spinning things. 

(1986: 217) 

Virek, a pleasant, human-like "generated image" (1986: 227), appears on the terminal 

screen in the AI's workshop; Marly is caught floating between the fake glowing image 

that masks Virek's deformed and decadent humanity and the very concrete arms of the 

superhuman robotic artist. The AI explains that the Virek teratoma might at best become 

"the least of my broken selves" (ibid.) – Marly recaps to the AI: 

You are someone else's collage. Your maker is the true artist. Was it the mad daughter [of 

the AI pioneer family]? It doesn't matter. Someone brought the machine here, welded it 

to the dome, and wired it to the traces of memory. And spilled, somehow, all the worn 

sad evidence of a family's humanity, and left it to be stirred, to be sorted by a poet. To be 

sealed away in boxes. I know of no more extraordinary work than this. No more complex 

gesture. 

(1986: 227) 

Why this fascination with the robotic poet? Why should the robotic author be so 

"extraordinary", the most "complex gesture"? There are many reasons why artists might 

be drawn to robotics, but one profound reason might lie in the nature of art itself. 

Art, like religion, is a window onto something beyond, though we know not what. The 

work of art entices us beyond who we are and what we understand, and we are left 

floating, like Marly, in a space defined only by the fact that it is new; one step beyond 

who we were, somewhere we thought we could not possibly be. The pleasure and the 

importance of art is perhaps that move up the meta organization of our understanding, to 

a more general space that gives us a different, if not a better insight into the world.

There is nothing new about this confluence of problematics. Art as combinatory, love, 

divine madness, knowledge, and writing are at the heart of Plato's Phaedrus. Socrates 

cites Midas's epitaph, spoken by a bronze Eliza, a kind of rigid chatbot: 

Bronze maiden am I and on Midas' mound I lie. 

As long as water flows and tall trees bloom, 

Right here fixed fast on the tearful tomb, 

I shall announce to all who pass near: Midas is dead and buried here. 

(264e; cited in Carson 1986: 134) 

Socrates's point seems to be that combinatory, like Midas's gold, is without meaning: 

I suppose you notice that it makes no difference which line is read first or which is read 

last. 

(264e; cited in Carson 1986: 135) 

Art lies elsewhere than in combinatory, and yet art is fundamentally combination, as its 

etymology suggests. It lies somewhere in the vast domain of post-play. Socrates contrasts 

uncreative potential and creative virtuality; true knowledge and sophistry; love and 

convention. Artful discourse must have a beginning and an end. It must go somewhere, 

and that where is up. Like eros, it is a winged thing (Carson 1986). 

Plato's multifaceted Phaedrus gives us a very ancient and general definition of RP: 

Definition 5, RP is the study of art through the foil of mechanical art. 

Note 

1 My thanks go to Greg Lessard and Michael Levison for technical assistance with 

Vinci as well as their explanations of its formalism. 


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30.1 Figure The potential, the real, the actual, and the virtual

30.2 Figure Raymond Queneau, Cent mille milliards de poèmes 

31. 

Designing Sustainable Projects and Publications 

Daniel V. Pitti 

Introduction 

Designing complex, sustainable digital humanities projects and publications requires 

familiarity with both the research subject and available technologies. It is essential that 

the scholar have a clear and explicit understanding of his intellectual objectives and the 

resources to be used in achieving them. At the same time, the scholar must have 

sufficient comprehension of available technologies, in order to judge their suitability for 

representing and exploiting the resources in a manner that best serves his intellectual 

objectives. Given the dual expertise required, scholars frequently find it necessary to 

collaborate with technologists in the design and implementation processes, who bring 

different understandings, experience, and expertise to the work. Collaboration in and of 

itself may present challenges, since most humanists generally work alone and share the 

research's results, not the process. 

Collaborative design involves iterative analysis and definition, frequently accompanied 

by prototype implementations that test the accuracy and validity of the analysis. While 

computers enable sophisticated data processing and manipulation to produce desired 

results, the scholar must analyze, define, and represent the data in detail. Each data 

component and each relation between data components needs to be identified, and, when 

represented in the computer, named and circumscribed. Data must not simply be entered 

into a computer, but be accompanied by additional data that identify the data 

components. This decomposition is necessary to instruct the computer, through programs 

or applications, to process the data to achieve the objectives, a process of locating, 

identifying, and manipulating the data.

The data need not be endlessly analyzed and decomposed. It is only necessary to identify 

the data components and relations deemed essential to achieving desired results. To 

determine what is essential in reaching the objectives, it is necessary also to understand 

the objectives in detail and subject them to a rigorous analysis, defining and making 

explicit each important function. Analysis of the objectives informs analysis of the data, 

determining what data and features of the data will be required to meet the objectives, 

and thus what features will need to be identified and explicitly represented. Conversely, 

analysis of the data will inform analysis of the objectives. 

Analyzing objectives relies on identifying and defining the intended user community (or 

communities) and the uses. Are the intended users peers, sharing the same or similar 

research interests? Are they students, or perhaps the interested public? If the users are 

students, then at what level: K-12, K-16, 9–12, or 13–16? Perhaps they are a very focused 

group, such as high school literature or history classes? For the intended user community, 

is the project primarily intended to facilitate research, scholarly communication of 

research, reference, pedagogy, or perhaps a combination of one or more of these? The 

answers to these questions will assist in determining the functions that will be most likely 

to serve the perceived needs and at the appropriate level or levels. 

As important as users are, those responsible for creation and maintenance of the project 

must also be considered. In fact, they may be considered another class of users, as distinct 

from the so-called end users. Before a project begins to produce and publish data content, 

the project must have an infrastructure that will support its production and publishing. 

Someone – project manager, researcher, lead design person – needs to specify the steps or 

workflow and methods involved in the complex tasks of identifying, documenting, and 

digitizing artifacts; creating original digital resources; and the control and management of 

both the assets created and the methods used. The design must incorporate the creator's 

requirements. Control, management, and production methods are easily overlooked if the 

designer focuses exclusively on the publication and end user. Asset management does not 

present a particular problem in the early stages of a project, when there are only a handful 

of objects to track. As the number of files increases, though, it becomes increasingly 

difficult to keep track of file names, addresses and versions. 

Focusing on achieving particular short-term results may also lead to neglecting 

consideration of the durability or persistence of the content and its publication. Ongoing 

changes in computer technology present long-term challenges. Changes in both 

applications and the data notations with which they work will require replacing one 

version of an application with another, or with an entirely different application, and will 

eventually require migrating the data from one notation to another. Generally these 

changes do not have a major impact in the first four or five years of a project, and thus 

may not seem significant to projects that are intended to be completed within three or 

four years. Most researchers and creators, though, will want their work to be available for 

at least a few years after completion. If a project has or will have a publisher, the 

publisher will certainly want it to remain viable as long as it has a market. The cultural 

heritage community, important participants in scholarly communication, may judge a 

publication to have cultural value for future generations. Archivists, librarians, and

museum curators, in collaboration with technologists, have been struggling seriously 

since at least the 1980s to develop methods for ensuring long-term access to digital 

information. Though many critical challenges remain, there is broad consensus that 

consistent and well-documented use of computer and intellectual standards is essential. 

Economy must also be considered. There are the obvious up-front costs of computer 

hardware and software, plus maintenance costs as these break or become obsolete. 

Human resources are also an important economic factor. Human resources must be 

devoted to the design, prototyping, and, ultimately, implementation. Once implemented, 

more resources are needed to maintain a project's infrastructure. Data production and 

maintenance also require human resources. Data needs to be identified, collected, and 

analyzed; created, and maintained in machine-readable form; and reviewed for accuracy 

and consistency. Realizing complex, detailed objectives requires complex, detailed 

implementations, production methods, and, frequently, a high degree of data 

differentiation. As a general rule, the more detailed and complex a project becomes, the 

more time it will take to design, implement, populate with data, and maintain. Thus every 

objective comes with costs, and the costs frequently have to be weighed against the 

importance of the objective. Some objectives, while worthwhile, may simply be too 

expensive. 

Design is a complex exercise: given this complexity, the design process is iterative, and 

each iteration leads progressively to a coherent, integrated system. It is quite impossible 

to simultaneously consider all factors. Both the whole and the parts must be considered in 

turn, and each from several perspectives. Consideration of each in turn leads to 

reconsideration of the others, with ongoing modifications and adjustments. Prototyping of 

various parts to test economic, intellectual, and technological feasibility is important. It is 

quite common to find an apparent solution to a particular problem while considering one 

or two factors but to have it fail when other factors are added, requiring it to be modified 

or even abandoned. Good design, then, requires a great deal of persistence and patience. 

Disciplinary Perspective and Objects of Interest 

Humanists study human artifacts, attempting to understand, from a particular perspective 

and within a specific context, what it means to be human. The term artifact is used here in 

the broadest sense: an artifact is any object created by humans. Artifacts may be primary 

objects of interest or serve as evidence of human activities, events, and intentions. 

Archivists make a useful distinction between an object intentionally created, as an end in 

itself, and an object that is a by-product of human activities, the object typically 

functioning instrumentally as a means or tool. Novels, poems, textbooks, paintings, films, 

sculptures, and symphonies are created as ends in themselves. Court records, birth 

certificates, memoranda, sales receipts, deeds, and correspondence facilitate or function 

as a means to other objectives, or as evidence or a record of particular actions taken. 

After serving their intended purpose, instrumental artifacts primarily function as 

evidence. Artifacts that are ends in themselves may of course also function as evidence. 

A novel, for example, may be viewed as a literary object, with particular aesthetic and 

intellectual qualities, or a source of evidence for language usage or for identifying

important cultural values in a particular historical context. Many disciplines share an 

interest in the same or similar objects or have similar assumptions and methods. Linguists 

and students of literature may share an interest in written texts, perhaps even literary 

texts, but differ substantially in what characteristics of texts are of interest or considered 

significant. Socio-cultural historical studies, while differing widely in the phenomena 

studied, will nevertheless employ similar methods, with the shared objective of a 

historical understanding. 

In order to apply computer technology to humanities research, it is necessary to represent 

in machine-readable form the artifacts or objects of primary or evidentiary interest in the 

research, as well as secondary information used in the description, analysis, and 

interpretation of the objects. The representations must reflect the disciplinary perspective 

and methods of the researcher, and facilitate the analytic and communication objectives 

of the project. In addition to perspective and intended uses, the method of representation 

will depend upon the nature of the artifact to be represented. As modes of human 

communication, pictorial materials, printed texts, mathematical formulae, tabular data, 

sound, film, sculptures, buildings, and maps, to name a few possible artifact types, all 

have distinct observable characteristics or features, and representations will generally 

isolate and emphasize those deemed essential to the research. 

All information in computers exists in a coded form that enables it to be mechanically 

read and processed by the computer. Such codes are ultimately based on a binary system. 

Combinations of fixed sequences of 0s and 1s are used to create more complex 

representations, such as picture elements or pixels, sound samples, and alphabetic and 

numeric characters. Each of these is then combined to create more complex 

representations. For example, an array of pixels is used to form a picture, a sequence of 

alphabetic and numeric characters a text, and a sequence of sound samples a song. The 

codes so developed are used to represent both data and the programs that instruct the 

computer in reading and processing the data. There are a variety of ways of creating 

machine-readable texts. Text can be entered or transcribed from a keyboard. Some 

printed textual materials can be scanned and then rendered into machine-readable form 

using optical character recognition (OCR) software, although many texts, particularly in 

manuscript form, exceed the capability of current OCR technology. Either way, creating 

a sequence of machine-readable characters does not take advantage of major 

technological advances made in the representation and manipulation of written language 

and mathematics. 

Written language and mathematics are composed of character data, marks, or graphs. 

Characters include ideographs (such as Chinese characters) and phonographs (as in the 

Latin alphabet), as well as Arabic numbers and other logograms. The discrete codes used 

in writing are mapped or reduced to machine-readable codes when represented in 

computers. Unicode (defined in ISO/IEC 10646) represents the most extensive attempt to 

map the known repertoire of characters used by humans to machine-readable codes. 

While written language (text) and mathematics are not the only types of information 

represented in computers, text constitutes an essential component of all humanities 

projects. Many projects will use one or more historical texts, the literary works of an

author or group of related authors, a national constitution, or a philosophical work as the 

principal subject. For other projects, particular texts and records may not be the object of 

study, but texts may still be used for information about individuals, families, and 

organizations; intellectual, artistic, political, or other cultural movements; events; and 

other socio-cultural phenomena. Projects employ text to describe and document resources 

and to communicate the results of analysis and research. Further, text is used to identify, 

describe, control, and provide access to the digital assets collected in any project, and is 

essential in describing, effecting, and controlling interrelations between entities and the 

digital assets used to represent them. Comparing, identifying, describing, and 

documenting such interrelations are essential components of all humanities research. 

The power and complexity of computer processing of textual data require complex 

representation of the text. A simple sequence of characters is of limited utility. In order to 

take full advantage of computing, textual data must be decomposed into their logical 

components, with each component identified or named, and its boundaries explicitly or 

implicitly marked. This decomposition and identification is necessary because 

sophisticated, accurate, and reliable processing of text is only possible when the computer 

can be unambiguously instructed to identify and isolate characters and strings of 

characters and to perform specified operations on them. There are a variety of specialized 

applications available for recording and performing mathematical operations on 

numerical data. There are also a variety of applications available for recording and 

processing textual data, though most do not have characteristics and features that make 

them appropriate for sustainable, intellectually complex humanities research. 

Database and markup technologies represent the predominant technologies available for 

textual information. Though they differ significantly, they have several essential 

characteristics and features in common. Both exist in widely supported standard forms. 

Open, community-based and widely embraced standards offer reasonable assurance that 

the represented information will be durable and portable. Database and markup 

technologies are either based on or will accept standard character encodings (ASCII or, 

increasingly, Unicode). Both technologies enable an explicit separation of the logical 

components of information from the operations that are applied to them. This separation 

is effected by means of two essential features. First, each requires user-assigned names 

for designating and delimiting the logical components of textual objects. While there are 

some constraints, given the available character repertoire, the number of possible names 

is unlimited. Second, each supports the logical interrelating and structuring of the data 

components. 

Though each of these technologies involves naming and structurally interrelating textual 

entities and components, each does so using distinctly different methods, and exploits the 

data in different ways. Deciding which technology to use will require analyzing the 

nature of the information to be represented and identifying those operations you would 

like to perform on it. With both technologies, the naming and structuring requires 

anticipating the operations. Each of these technologies is optimized to perform a set of 

well-defined operations. Though there is some overlap in functionality, the two 

technologies are best described as complementary rather than competitive. Information

est represented in databases is characterized as "data-centric", and information best 

represented in markup technologies is characterized as "document-centric." Steve 

DeRose, in a paper delivered in 1995 at a conference devoted to discussing the encoding 

of archival finding aids, made much the same distinction, though he used the terms 

"document" and "form" to distinguish the two information types (see Steve DeRose, 

"Navigation, Access, and Control Using Structured Information", in The American 

Archivist (Chicago: Society of American Archivists), 60,3, Summer 1997, pp. 298–309). 

Information collected on many kinds of forms is data-centric. Job applications are a 

familiar example. Name, birth date, street address, city, country or state, postal codes, 

education, skills, previous employment, date application completed, name or description 

of a position sought, references, and so on, are easily mapped into a database and easily 

retrieved in a variety of ways. Medical and student records, and census data are other 

common examples of data-centric information. These familiar examples have the 

following characteristics in common: 

• Regular number of components or fields in each discrete information unit. 

• Order of the components or fields is generally not significant. 

• Each information component is restricted to data. That is, it has no embedded 

delimiters, other than the formal constraints of data typing (for example, a date may be 

constrained to a sequence of eight Arabic numbers, in the order year-month-day). 

• Highly regularized structure, possibly with a fixed, though shallow, hierarchy. 

• Relations between discrete information units have a fixed number of types (though the 

number of occurrences of each type may or may not be constrained). 

• Processing of data-centric information (such as accurate recall and relevance retrieval, 

sorting, value comparison, and mathematical computation) is highly dependent on 

controlled values and thus highly dependent on machine-enforced data typing, authority 

files, and a high degree of formality, accuracy, and consistency in data creation and 

maintenance. 

Common types of databases are hierarchical, network, relational, and object-oriented. 

Today, relational databases are by far the most prevalent. Structured Query Language 

(SQL), an ISO standard first codified in 1986 and substantially revised in 1992 and again 

in 1999, is the major standard for relational databases. While compliance with the 

standard is somewhat irregular, most relational databases comply sufficiently to ensure 

the portability and durability of data across applications. There are only a few viable 

implementations of object-oriented databases, though the technology has contributed 

conceptual and functional models that influenced the 1999 revision of SQL. SQL is both 

a Data Definition Language (DDL) and a Data Manipulation Language (DML). As a 

DDL, it allows users to define and name tables of data, and to interrelate the tables. Such 

definitions are frequently called database schemas. Tables have rows and columns.

Within the context of databases, rows are commonly called records, and columns called 

fields in the records. Each column or field is assigned a name, typically a descriptive term 

indicating the type of data to be contained in it. The DML facilitates updating and 

maintaining the data, as well as sophisticated querying and manipulating of data and data 

relations. 

Most historical or traditional documents and records are too irregular for direct 

representation in databases. Data in databases are rigorously structured and systematic 

and most historical documents and records simply are not. Nevertheless, some documents 

and records will be reducible to database structures, having characteristics approximating 

those listed above. Many traditional government, education, and business records may 

lend themselves to representation in databases, as will many ledgers and accounting 

books. But a doctoral dissertation, while a record, and generally quite regularized in 

structure, lacks other features of data that it would have to have to fit comfortably in a 

database architecture – for example, the order of the text components matters very much, 

and if they are rearranged arbitrarily, the argument will be rendered incoherent. 

While database technology may be inappropriate for representing most historical 

documents and records, it is very appropriate technology for recording analytic 

descriptions of artifacts and in systematically describing abstract and concrete 

phenomena based on analysis of evidence found in artifacts. Analytic descriptive 

surrogates will be useful in a wide variety of projects. Archaeologists, for example, may 

be working with thousands of objects. Cataloguing these objects involves systematically 

recording a vast array of details, frequently including highly articulated classification 

schemes and controlled vocabularies. Database technology will almost always be the 

most appropriate and effective tool for collecting, classifying, comparing, and evaluating 

artifacts in one or many media. 

Database technology is generally an appropriate and effective tool for documenting and 

interrelating records concerning people, events, places, movements, artifacts, or themes. 

Socio-cultural historical projects in particular may find databases useful in documenting 

and interrelating social and cultural phenomena. While we can digitize most if not all 

artifacts, many concrete and abstract phenomena are not susceptible to direct digitization. 

For example, any project that involves identifying a large number of people, 

organizations, or families will need to represent them using descriptive surrogates. A 

picture of a person, when available, might be very important, but textual identification 

and description will be necessary. In turn, if an essential part of the research involves 

describing and relating people to one another, to artifacts created by them or that provide 

documentary evidence of them, to events, to intellectual movements, and so on, then 

database technology will frequently be the most effective tool. Individuals, artifacts, 

places, events, intellectual movements, can each be identified and described (including 

chronological data where appropriate). Each recorded description can be associated with 

related descriptions. When populated, such databases enable users to locate individual 

entities, abstract as well as concrete, and to see and navigate relations between entities. If 

the database is designed and configured with sufficient descriptive and classificatory 

detail, many complex analytic queries will be possible, queries that reveal intellectual

elations between unique entities, but also between categories or classes of entities based 

on shared characteristics. 

Textbooks, novels, poems, collections of poetry, newspapers, journals, and journal 

articles are all examples of document-centric data. These familiar objects have in 

common the following characteristics: 

• Irregular number of parts or pieces. Documents, even documents of a particular type, do 

not all have the same number of textual divisions (parts, chapters, sections, and so on), 

paragraphs, tables, lists, and so on. 

• Serial order is significant. It matters whether this chapter follows that chapter, and 

whether this paragraph follows that paragraph. If the order is not maintained, then 

intelligibility and sense break down. 

• Semi-regular structure and unbounded hierarchy. 

• Arbitrary intermixing of text and markup, or what is technically called mixed content. 

• Arbitrary number of interrelations (or references) within and among documents and 

other information types, and generally the types of relations are unconstrained or only 

loosely constrained. 

Extensible Markup Language (XML), first codified in 1998 by the World Wide Web 

Consortium (W3C), is the predominant markup technologies standard. XML is a direct 

descendant of Standard Generalized Markup Language (SGML), first codified in 1986 by 

the International Standards Organization (ISO). While the relationship between XML and 

SGML is quite complex, XML may be viewed as "normalized" SGML, retaining the 

essential functionality of SGML, while eliminating those features of SGML that were 

problematic for computer programmers. 

XML is a descriptive or declarative approach to encoding textual information in 

computers. XML does not provide an off-the-shelf tagset that one can simply take home 

and apply to a letter, novel, article, or poem. Instead, it is a standard grammar for 

expressing a set of rules according to which a class of documents will be marked up. 

XML provides conventions for naming the logical components of documents, and a 

syntax and metalanguage for defining and expressing the logical structure and relations 

among the components. Using these conventions, individuals or members of a 

community sharing objectives with respect to a particular type of document can work 

together to encode those documents. 

One means of expressing analytic models written in compliance with formal XML 

requirements is the document type definition, or DTD. For example, the Association of 

American Publishers has developed four DTDs for books, journals, journal articles, and 

mathematical formulae. After thorough revision, this standard has been released as an 

ANSI/NISO/ISO standard, 12083- A consortium of software developers and producers

has developed a DTD for computer manuals and documentation called DocBook. The 

Text Encoding Initiative (TEI) has developed a complex suite of DTDs for the 

representation of literary and linguistic materials. Archivists have developed a DTD for 

archival description or finding aids called encoded archival description (EAD). A large 

number of government, education and research, business, industry, and other institutions 

and professions are currently developing DTDs for shared document types. DTDs shared 

and followed by a community can themselves become standards. The ANSI/NISO/ISO 

12083, DocBook, TEI, and EAD DTDs are all examples of standards. 

The standard DTDs listed above have two common features. First, they are developed 

and promulgated by broad communities. For example, TEI was developed by humanists 

representing a wide variety of disciplines and interests. The development process (which 

is ongoing) requires negotiation and consensus-building. Second, each DTD is 

authoritatively documented, with semantics and structure defined and described in detail. 

Such documentation helps make the language public rather than private, open rather than 

closed, standard rather than proprietary, and thereby promotes communication. These two 

features – consensus and documentation – are essential characteristics of standards, but 

they are not sufficient in and of themselves to make standards successful (i.e., make them 

widely understood and applied within an acceptable range of uniformity). 

The distinction between document-centric and data-centric information, while useful, is 

also frequently problematic. It is a useful distinction because it highlights the strengths of 

markup languages and databases, although it does not reflect information reality, as such. 

Frequently any given instance of information is not purely one or the other, but a mixture 

of both. Predominantly data-centric information may have some components or features 

that are document-centric, and vice-versa. The distinction is of more than theoretical 

interest, as each information type is best represented using a different technology, and 

each technology is optimized to perform a specific set of well-defined functions and 

either does not perform other functions, or performs them less than optimally. When 

deciding the best method, it is unfortunately still necessary to weigh what is more and 

less important in your information and to make trade-offs. 

In addition to text representation. XML is increasingly used in realizing other functional 

objectives. Since the advent of XML in 1998, many database developers have embraced 

XML syntax as a data-transport syntax, that is, for passing information in machine-tomachine 

and machine-to-human communication, XML is also increasingly being used to 

develop standards for declarative processing of information. Rendering and querying are 

probably the two primary operations performed on XML-encoded textual documents. We 

want to be able to transform encoded documents into human-readable form, on computer 

screens, and on paper. We also want to be able to query individual documents and 

collections of documents. There are currently several related standards for rendering and 

querying XML documents that have been approved or are under development: Extensible 

Stylesheet Language – Transformation (XSLT), Extensible Stylesheet Language – 

Formatting Objects (XSLFO), and XQuery. These declarative standards are significant 

because they ensure not only the durability of encoded texts, but also the durability of the 

experienced presentation.

Users and Uses 

Developing XML and database encoding schemes, as we have seen, involves 

consideration of the scholarly perspective, the nature and content of the information to be 

represented, and its intended use. For example, if we have a collection of texts by several 

authors, and we want users to be able to locate and identify all texts by a given author, 

then we must explicitly identify and delimit the author of each text. In the same way, all 

intended uses must be identified, and appropriate names and structures must be 

incorporated into the encoding schemes and into the encoded data. In a sense, then, XML 

and database encoding schemes represent the perspective and understanding of their 

designers, and each DTD or schema is an implied argument about the nature of the 

material under examination and the ways in which we can use it. 

Specifying an intended use requires identifying and defining users and user communities. 

The researcher is an obvious user. In fact, the researcher might be the only user, if the 

intention is to facilitate collection and analysis of data, with analytic results and 

interpretation published separately. Such an approach, though, deprives readers of access 

to the resources and methods used, and there is an emerging consensus that one of the 

great benefits of computers and networks is that they allow us to expose our evidence and 

our methods to evaluation and use by others. If that is considered desirable, design must 

then take other users into consideration. As a general rule, the narrower and more specific 

the intended audience, the easier it will be to identify and define the uses of the data. If 

the intended audience is more broadly defined, it is useful to classify the users and uses 

according to educational and intellectual levels and abilities. While there may be overlap 

in functional requirements at a broad level, there will generally be significant differences 

in the complexity and detail of the apparatus made available to users. 

We can divide user functions or operations into three types: querying, rendering, and 

navigation. This division is simply an analytic convenience. The categories are 

interrelated and interdependent, and some functions may involve combinations of other 

functions. Querying, simply characterized, involves users submitting words, phrases, 

dates, and the like, to be matched against data, with matched data or links to matched 

data returned, typically in an ordered list, to the user. Browsing is a type of querying, 

though the author of the research collection predetermines the query and thus the list of 

results returned. Rendering is the process by which machine-readable information is 

transformed into human-readable information. All information represented in computers, 

whether text, graphic, pictorial, or sound, must be rendered. Some rendering is 

straightforward, with a direct relation between the machine-readable data and its humanreadable 

representation. Navigational apparatus are roughly analogous to the title page, 

table of contents, and other information provided in the preliminaries of a book. They 

inform the user of the title of the research collection, its intellectual content, scope, and 

organization, and provide paths or a means to access and traverse sub-collections and 

individual texts, graphics, pictures, videos, or sound files. The navigational apparatus will 

frequently employ both text and graphics, with each selectively linked to other 

navigational apparatus or directly to available items in the collection. Much like the 

design of the representation of digital artifacts, and necessarily related to it, designing the

navigational apparatus requires a detailed analysis of each navigational function and steps 

to ensure that the underlying representation of the data will support it. 

Determining and defining the querying, rendering, and navigational functions to be 

provided to users should not be an exclusively abstract or imaginative exercise. Many 

designers of publicly accessible information employ user studies to inform and guide the 

design process. Aesthetics play an important role in communication. Much of the 

traditional role of publishers involves the aesthetic design of publications. The "look and 

feel" of digital publications is no less important. While content is undoubtedly more 

important than form, the effective communication of content is undermined when 

aesthetics are not taken sufficiently into consideration. Researchers may wish to enlist the 

assistance of professional designers. While formal, professionally designed studies may 

be beyond the means of most researchers, some attempt at gathering information from 

users should be made. Interface design should not be deferred to the end of the design 

process. Creating prototypes and mockups of the interface, including designing the visual 

and textual apparatus to be used in querying, rendering, and navigating the collection and 

its parts, will inform the analysis of the artifacts and related objects to be digitally 

collected, and the encoding systems used in representing them. 

Creating and Maintaining Collections 

Creating, maintaining, managing, and publishing a digital research collection requires an 

infrastructure to ensure that the ongoing process is efficient, reliable, and controlled. 

Building a digital collection involves a large number of interrelated activities. Using a 

variety of traditional and digital finding aids, researchers must discover and locate 

primary and secondary resources in private collections and in public and private archives, 

museums, and libraries. Intellectual property rights must be negotiated. The researcher 

must visit the repository and digitally capture the resource, or arrange for the resource to 

be digitally captured by the repository, or, when possible, arrange to borrow the resource 

for digital capture. After each resource is digitally captured, the capture file or files may 

be edited to improve quality and fidelity. One or more derivative files may also be 

created. Digital derivatives may be simply alternative versions. For example, smaller 

image files may be derived from larger image files through sampling or compression to 

facilitate efficient Internet transmission or to serve different uses. Derivatives also may 

involve alternative representational technologies that enhance the utility or functionality 

of the information, for example, an XML-encoded text rather than bitmap images of 

original print materials. Creation of enhanced derivatives may constitute the primary 

research activity, and typically will involve extended analysis and editing of successive 

versions of files. Finally, complex relations within and between files and file components 

may be identified and explicitly recorded or encoded, and must be managed and 

controlled to ensure their persistence. All of these activities will require documentation 

and management that in turn will require recording detailed information. 

The detailed information needed to intellectually and physically manage individual files 

and collections of interrelated files can be divided into four principal and interrelated 

categories: descriptive, administrative, file, and relations or structural data.

These categories represent the prevailing library model for recording management and 

control data, commonly called metadata, and they have been formalized in the Metadata 

Encoding and Transmission Standard (METS). Each of these four areas addresses 

specific kinds of management and control data. Each is also dependent on the others. 

Only when they are related or linked to one another will they provide an effective means 

of intellectual, legal, and physical control over collections of digital materials. These 

sorts of data are primarily data-centric, and thus best represented using database 

technology. 

Descriptive data function as an intellectual surrogate for an object. Descriptive 

information is needed to identify the intellectual objects used in building as well as 

objects digitally represented in a research collection. Documenting and keeping track of 

the sources and evidence used in creating a research project is as important in digital 

research as it is in traditional research. For example, if particular sources are used to 

create a database that describes individuals and organizations, those sources need to be 

documented even though they may not be directly accessible in the collection. Typically, 

descriptive information includes author, title, and publisher information and may also 

contain subject information. When existing traditional media are digitized, it is necessary 

to describe both the original and its digital representation. While there may be 

overlapping information, the two objects are distinct manifestations of the same work. 

Traditional media will generally be held in both public repositories and private 

collections. This is particularly important for unique materials, such as manuscripts and 

archival records, but also important for copies, such as published books, as copies are in 

fact unique, even if only in subtle ways. The repositories and collections, even the private 

collection of the researcher, that hold the resources need to be recorded and related to the 

description of each resource used or digitized. When interrelated with administrative, file, 

and relations data, descriptive data serves the dual role of documenting the intellectual 

content, and attesting to the provenance and thus the authenticity of the sources and their 

digital derivatives. 

When linked to related descriptive and file data, administrative data enable several 

essential activities: 

• managing the agents, methods, and technology involved in creating digital files; 

• ensuring the integrity of those digital files; 

• tracking their intellectual relation to the sources from which they are derived; 

• managing the negotiation of rights for use; 

• controlling access based on negotiated agreements. 

A wide variety of hardware and software exists for capturing traditional media, and the 

environmental conditions and context under which capture takes place varies. Digital 

capture always results in some loss of information, even if it enhances access and analytic

uses. An image of a page from a book, for example, will not capture the tactile features of 

the page. Hardware and software used in capture differ in quality. Each may also, in time, 

suffer degradation of performance. Hardware and software also continue to develop and 

improve. The environmental conditions that affect capture may also vary. For example, 

differences in lighting will influence the quality of photographs, ambient noise will 

influence sound recording. The knowledge and skill of the technician will also affect the 

results. Direct capture devices for image, sound, and video have a variety of options 

available that will affect both the quality and depth of information captured. 

Following capture, versions may be directly derived from the capture file or files, or 

alternative forms may be created using different representational methods. Versions of 

files are directly derived from the capture file, and represent the same kind of information 

(such as pixels or sound samples) but employ different underlying representations that 

facilitate different uses of the information. Tag Image File Format (TIFF), for example, is 

currently considered the most lossless capture format for pictorial or image data, but they 

are generally quite large. JPEG (Joint Photographic Experts Group), a compression 

technology, is generally used for creating smaller files that are more efficiently 

transmitted over the Internet. Such compression results in a loss of information, and thus 

impacts the image's intellectual content and its relation to the capture file and the original 

image. Other kinds of capture involve human judgment and intervention, as the 

underlying representations are fundamentally different. A bitmap page image differs 

fundamentally from an XML encoding of the text on the page, since the former is a pixel 

representation and the latter a character-based encoding. While OCR software can be 

used to convert pixel representations of characters to character representations, manual 

correction of recognition errors is still necessary. Accurate encoding of a text's 

components requires judgment and manual intervention, even if some encoding is 

susceptible to machine-assisted processing. The agents, methods, and technology used in 

deriving versions and alternative forms of data need to be recorded and related to each 

file in order to provide the information necessary to evaluate the authenticity and 

integrity of the derived data. 

Intellectual property law or contract law will protect many of the resources collected and 

published in a digital humanities research project. Use of these materials will require 

negotiating with the property owners, and subsequently controlling and managing access 

based on agreements. Currently there are several competing initiatives to develop 

standards for digital rights management, and it is too early to determine which if any will 

satisfy international laws and be widely embraced by users and developers. A successful 

standard will lead to the availability of software that will enable and monitor access, 

including fee-based access and fair use. It is essential for both legal and moral reasons 

that researchers providing access to protected resources negotiate use of such materials, 

record in detail the substance of agreements, and enforce, to the extent possible, any 

agreed-upon restrictions. It is equally important, however, that researchers assert fair use 

when it is appropriate to do so: fair use, like a right of way, is a right established in 

practice and excessive self-policing will eventually undermine that right. The Stanford 

University Library's site on copyright and fair use is a good place to look for guidance on

when it is appropriate to assert this right (see for more 

information). 

File data enable management and control of the files used to store digital data. When 

linked to related descriptive data, the intellectual content of files can be identified, and 

when linked to administrative data, the authenticity, integrity, and quality of the files can 

be evaluated, and access to copyright-protected materials can be controlled. It is quite 

easy to overlook the need to describe and control files early on in projects. It is easy to 

simply create ad hoc file names and directory structures to address their management and 

control. As projects develop, however, the number of files increases and the use of ad hoc 

file names and directory structures begins to break down. It becomes difficult to 

remember file names, what exactly each file name designates, and where in the directory 

structure files will be located. 

On the other hand, it is quite common to create complex, semantically overburdened file 

names when using file names and directories to manage files. Such file names will 

typically attempt, in abbreviated form, to designate two or more of the following: source 

repository, identity of the object represented in a descriptive word, creator (of the 

original, the digital file, or both), version, date, and digital format. If the naming rules are 

not carefully documented, such faceted naming schemes generally collapse quite quickly, 

and even when well documented, are rarely effective as the number of files increases. 

Such information should be recorded in descriptive and administrative data linked to the 

address of the storage location of the file. A complete address will include the address of 

the server, the directory, and the file name. The name and structure of the directory and 

file name should be simple, and easily extensible as a collection grows. 

There are a wide variety of possible approaches to naming and addressing files. 

Completely arbitrary naming and addressing schemes are infinitely extensible, though it 

is possible to design simple, extensible schemes that provide some basic and useful clues 

for those managing the files. For example, most projects will collect representations of 

many artifacts from many repositories. Each artifact may be captured in one or more 

digital files, with one or more derivative files created from each capture file. In such 

scenarios, it is important to create records for each repository or collection source. Linked 

to each of these repository records will be records describing each source artifact. In turn, 

each artifact record will be linked to one or more records describing capture files, with 

each capture record potentially linked to one or more records describing derivative files. 

Given this scenario, a reasonable file-naming scheme might include an abbreviated 

identifier for the repository, followed by three numbers separated by periods identifying 

the artifact, the capture, and the derivative. Following file-naming convention, a suffix 

designating the notation or format of the file is appended to the end. Ideally, the numbers 

reflect the order of acquisition or capture. For example, the first artifact digitally acquired 

from the Library of Congress might have the base name "loc.OOOl", with "loc" being an 

abbreviated identifier for "Library of Congress." The second digital file capture of this 

artifact would have the base name "loc.0001.02" and the fourth derivative of the original 

capture "loc.0001.02.04." A suffix would then be added to the base to complete the file 

name. For example, if the capture file were in the TIFF notation and the derivative in the

JPEG notation, then the file names would be "loc.0001.02.tif" and "loc.0001.02.04.jpg." 

Full identification of any file can be achieved by querying the database for the file name. 

The directory structure might simply consist of a series of folders for each repository. 

The file-naming scheme in this example is relatively simple, sustainable, and despite the 

weak semantics, very useful, even without querying the database. All files derived from 

artifacts in the same repository will be collocated when sorted, and all related versions of 

a file will also be collocated. In addition, the order in which artifacts are identified and 

collected will be reflected in the sorting order of each repository. The example provided 

here is not intended to be normative. Varying patterns of collecting, the nature of the 

artifacts collected, and other factors might lead to alternative and more appropriate 

schemes. The important lesson, though, is that database technology is ideally suited to 

management and control of intellectual and digital assets, and clever naming schemes and 

directory structures are not. Naming schemes should be semantically and structurally 

simple, with linked database records carrying the burden of detailed descriptive and 

administrative data. 

Interrelating information is important as both an intellectual and a management activity. 

Interrelating intellectual objects within and between files requires creating explicit 

machine-readable data that allow automated correlation or collocation of related 

resources. For our purposes, there are two types of relations: intrinsic and extrinsic. 

Intrinsic relation data support is a feature of the character-based database and markup 

technologies. Database technology enables the creation, maintenance, and use of relations 

between records or tables within a particular database. XML technology enables the 

creation, maintenance, and use of relations between components within a particular 

encoded document. Care must be taken in ensuring that the feature is properly employed 

to ensure the integrity of the relations. 

Extrinsic relations are external to databases and XML-encoded documents. Relations 

between one database and another, between a database and XML documents, between 

XML documents, between XML documents and files in other notations or encodings, and 

relations between image or sound files and other image or sound files are all types of 

extrinsic relations. Extrinsic relations are not currently well supported by standards and 

standard-based software. There are a large number of loosely connected or independent 

initiatives to address the standardization of extrinsic relations. Extrinsic relations thus 

present particular challenges in the design of digital research collections. Especially 

difficult are relations between resources in the collection under the control of the 

researcher, and resources under the control of others. 

In the absence of standard methods for recording and managing extrinsic relations, it is 

necessary to design ad hoc methods. Given the complexity of managing relations, it is 

difficult to provide specific guidance. Both database and markup technologies provide 

some support for extrinsic relations, but this support must be augmented to provide 

reasonable control of relations. Some databases support direct storage of Binary Large 

Objects (BLOBs). Despite the term, BLOBs may include not only binary data, such as 

image and sound files, but also complex character data such as XML documents, vector

graphics, and computer programs. While not standardized, XML Catalog, a standard for 

mapping the extrinsic relations of XML documents, provides some support. XML 

catalogues, though, primarily represent a method for communicating data about relations 

and offer no direct support for managing them effectively. Database technology is 

reasonably effective in managing the relations data communicated in XML catalogues. 

XQuery offers a standardized but indirect way of articulating extrinsic relations, though it 

is not intended as a means of controlling them or ensuring referential integrity. According 

to the World Wide Web Consortium, XQuery is "designed to be a language in which 

queries are concise and easily understood. It is also flexible enough to query a broad 

spectrum of XML information sources, including both databases and documents" (see 

). However, at this point none of these 

approaches will be completely reliable and effective without care and vigilance on the 

part of the human editors and data managers. 

Conclusion: Collaboration 

In the late twentieth and early twenty-first centuries, the most significant impact of 

information technology may be increased collaboration. Collaboration, when successful, 

offers many intellectual, professional, and social benefits. A group of scholars working 

together can create research collections more intellectually complex and comprehensive 

than is possible for an individual working alone. Complementary and even competing 

disciplinary perspectives and specialties will support broader and richer analysis and 

understanding. Collaboration will enable greater productivity. Collaboration between 

humanists and technologists may lead to more profound understandings and more 

incisive tools than either would develop by working alone. The explicitness and logical 

rigor required to represent and use digital information exposes to criticism many of the 

traditional disciplinary assumptions, and often leads to a deeper understanding of our 

methods and the subjects to which they are applied. 

Despite these benefits, collaboration, whether with fellow humanists or technologists, 

also presents unfamiliar challenges, which require careful attention and time – often more 

time than is anticipated at the beginning. The most fundamental challenge of 

collaboration is balancing individual interests and shared objectives. Collaborators need 

to discuss, negotiate, and clearly document the research and publication objectives, a 

process that will require cooperation and compromise. Collaborators also need to 

recognize individual differences in aptitude for digital technology, and differences in 

expertise, methodology, and emphasis. Individual responsibilities, obligations, and 

production goals need to be negotiated and documented, and should reflect expertise and 

aptitude. Intellectual and social compatibility, trust, and mutual respect are essential 

characteristics of successful collaboration. Individual professional and intellectual needs 

and goals must also be recognized. The nature of the academic culture of recognition and 

rewards requires that individual scholars be productive and that the scholarship be of high 

quality. Individual contributions need to be clearly documented, in order to provide 

reliable evidence for reviewers. Depending upon the nature of the collaboration, 

individual contributions may be intermingled, and without careful project design it may 

be difficult or impossible to reliably indicate who is responsible for what. Such

documentation is essential, for example, in the statements of responsibility incorporated 

into the descriptive data correlated with digital objects, and in bylines of digital objects, 

where this is possible. 

In collaborative projects, standards and guidelines or manuals will need to be augmented 

with training. Left alone, different people will interpret and apply guidelines differently. 

With respect to character-based technologies, in particular text encoding and database 

entry, but including geographic information systems (GIS), computer-assisted design 

(CAD), and many graphic systems, data entry and encoding will also involve analysis 

and interpretation of source materials. Because human judgment is involved, it will be 

impossible to entirely eliminate differences and to achieve absolute, complete intellectual 

and technological consistency. Happily, absolute consistency is rarely necessary – it is 

generally only necessary to have absolute consistency in those data components that are 

directly involved in automated processing. An acceptable range of consistency can 

generally be achieved with training. Typically such training, as in all education, will 

require both instruction and an initial period of intensive, thorough review, evaluation, 

and revision. After intellectual and technological quality reaches an acceptable level, 

periodic review will ensure that the level is sustained. 

Given the amount of communication involved, it is generally easier to collaborate when 

all or most of the participants work in the same location. One of the most attractive 

benefits of the Internet, though, is the ability to communicate with anyone else on the 

Internet, regardless of where they are connected, and to work remotely in the creation and 

maintenance of shared resources. As with all other aspects of designing a research 

collection, care must be taken to provide an infrastructure to support communication and 

shared editing of the database. It is advisable to set up an e-mail list for project 

communication (using software such as Mailman, Majordomo, or Listserv), and to 

archive the correspondence it distributes (software such as Hypermail can automatically 

produce a web-accessible version of this archive). An e-mail list will facilitate 

broadcasting messages to a group. Archiving the messages will serve the purpose of 

preserving discussions, negotiations, decisions, and other transactions that document the 

creation and maintenance of the collection. Among other things, such an archive will 

serve the important function of providing documentary evidence for reviewers of 

individual participation and contributions. Shared editing also needs to be managed, 

typically by a database, to control and manage workflow, tracking additions, changes, 

and deletions. As attractive and useful as remote collaboration can be, it frequently is still 

necessary to meet periodically in person for discussions, negotiations, and training. 

Collaboration with technologists may also present its own challenges. Although 

technologists who elect to participate in digital humanities projects may themselves have 

some background in the humanities, it will more often be the case that technologists have 

little training in humanities disciplines. Humanities methods, perspectives, and values 

may be strange to them, and unfamiliar terminology may hinder communication. 

Carefully negotiated and apparently shared understandings will frequently be illusory, 

requiring further discussion and renegotiation. Close and successful collaboration will

equire goodwill and persistence, and will rely on developing a shared language and an 

enduring mutual understanding. 

Depending upon the size and constitution of a collaborative group, it may be necessary to 

formally address issues of administration and supervision. A small number of peers who 

are comfortable working with one another may rely only on informal discussion and 

consensus. A project driven by one scholar who enlists graduate assistants will implicitly 

be led and managed by that scholar. Larger groups, involving scholars of various ranks 

and prestige and perhaps also graduate assistants, may require formal designation of a 

leader. Even when a particular individual is in charge, a project manager or 

administrative assistant may be beneficial, to tend to coordination and monitoring 

deadlines and the like. 

Finally, collaboration depends on sustainable project design. It is relatively easy for one 

person to work, in the short run, in an idiosyncratic framework of his or her own design: 

it is more difficult for two people to do this, and it becomes increasingly difficult the 

more people are involved, and the longer the project continues. If a project is 

collaborative, and if it is to succeed, it will require the attributes of standardization, 

documentation, and thoughtful, iterative design that have been recommended throughout 

this chapter, in different contexts. 

32. 

Conversion of Primary Sources 

Marilyn Deegan and Simon Tanner 

Material Types 

The primary source materials for humanists come in many data forms, and the digital 

capture of that data needs to be carefully considered in relation to its formats and the 

materials that form the substrates upon which it is carried. It is difficult to list these 

comprehensively, but it is true to say that anything that can be photographed can be 

digitized, and that some materials can be photographed or digitized with more fidelity 

than others. This is an interesting point, as many digitization projects aim to present some 

kind of "true" representation of the original, and some materials can get closer to this goal 

than others. 

The materials of interest to humanists are likely to include the following, and there are 

probably many more examples that could be given. However, this list covers a 

sufficiently broad area as to encompass most of the problems that might arise in 

digitization.

Documents 

This category covers a huge variety of materials, from all epochs of history, and includes 

both manuscript and printed artifacts. Manuscripts can be from all periods, in any 

language, and written on a huge variety of surfaces: paper, parchment, birch bark, 

papyrus, lead tablets, wood, stone, etc. They will almost certainly have script or character 

set issues, and so may require special software for display or analysis. They may be 

music manuscripts, which will have issues of notation and representation of a whole 

range of special characters; or a series of personal letters, which being loose-leafed will 

pose their own organizational problems. Manuscripts may be composite in that they will 

have a number of different (often unrelated) texts in them, and there may be difficulties 

with bindings. They may be very large or very small – which will have implications for 

scanning mechanisms and resolutions. With handwritten texts, there can be no automated 

recognition of characters (although some interesting work is now being done in this area 

by the developers of palm computers and tablet PC computers – we need to wait to see if 

the work being done on handwriting recognition for data input feeds into the recognition 

of scripts on manuscript materials of the past), and they may also have visual images or 

illuminated letters. The documents that humanists might want to digitize also include 

printed works from the last 500 years, which come in a huge variety: books, journals, 

newspapers, posters, letters, typescript, gray literature, musical scores, ephemera, 

advertisements, and many other printed sources. Earlier materials and incunabula will 

have many of the same problems as manuscripts, and they may be written on paper or 

parchment. With printed materials, there can be a wide range of font and typesetting 

issues, and there is often illustrative content to deal with as well. Printed materials will 

also come in a huge range of sizes, from posters to postage stamps. 

Visual materials 

These may be on many different kinds of substrates: canvas, paper, glass, film, fabric, 

etc., and for humanists are likely to include manuscript images, paintings, drawings, 

many different types of photographs (film negatives, glass plate negatives, slides, prints), 

stained glass, fabrics, maps, architectural drawings, etc. 

Three-dimensional objects and artifacts 

With present technologies, it is not possible to create a "true" facsimile of such objects, 

but there are now three-dimensional modeling techniques that can give good 

representations of three-dimensional materials. These are likely to include the whole 

range of museum objects, sculpture, architecture, buildings, archaeological artifacts. 

Time-based media 

There is increasing interest in the digitization of film, video, and sound, and indeed there 

have been large advances in the techniques available to produce time-based media in 

born-digital form in the commercial world, and also to digitize analogue originals. The 

Star Wars movies are a good example of the historical transition from analogue to digital.

The first three movies were filmed totally in analogue and then later digitally remastered; 

then all the films were made available on DVD. Finally, the last film to be made, Attack 

of the Clones, was totally filmed in the digital medium. 

The conversion of three-dimensional objects and of time-based media is more 

problematic than that of text or still images. It draws upon newer technologies, the 

standards are not as well supported, and the file sizes produced are very large. The 

hardware and software to manipulate such materials is also generally more costly. 

The Nature of Digital Data 

If the nature of humanities data is very complex, the nature of digital data in their 

underlying form is seemingly very simple: all digital data, from whatever original they 

derive, have the same underlying structure, that of the "bit" or the fonary digit. A bit is an 

electronic impulse that can be represented by two states, "on" or "off", also written as "1" 

or "0." A "byte" consists of 8 bits, and 1 byte represents 1 alphanumeric character. A 10letter 

word, for example, would be 10 bytes. Bits and bytes are linked together in chains 

of millions of electronic impulses; this is known as the "bit stream." A "kilobyte" is 1,024 

bytes, and a "megabyte" 1,024 kilobytes. Digital images are represented by "pixels" or 

picture elements – dots on the computer screen or printed on paper. Pixels can carry a 

range of values, but at the simplest level, one pixel equals one bit, and is represented in 

binary form as "black" (off) or "white" (on). Images captured at this level are "bi-tonal" – 

pure black and white. Images can also be represented as 8-bit images, which have 256 

shades of either gray or color and 24-bit images, which have millions of colors – more 

than the eye can distinguish. The number of bits chosen to represent each pixel is known 

as the "bit depth", and devices capable of displaying and printing images of higher bit 

depths than this (36 or 48 bits) are now emerging. 

Bit depth is the number of bits per pixel, "resolution" is the number of pixels (or printed 

dots) per inch, known as ppi or dpi. The higher the resolution, the higher the density of a 

digital image. The resolution of most computer screens is generally in the range of 75 to 

150 pixels per inch. This is adequate for display purposes (unless the image needs to be 

enlarged on-screen to show fine detail), but visual art or photographic content displayed 

at this resolution is inadequate for printing (especially in color), though images of black 

and white printed text or line art are often acceptable. High-density images of originals 

(manuscripts, photographs, etc.) need to be captured in the range 300–600 ppi for print 

quality output. Note that this will depend on the size of the original materials: 35 mm 

slides or microfilm originals will need to be captured at much higher resolutions, and 

scanners are now available offering resolutions of up to 4,000 dpi for such materials. 

These issues are discussed further below. 

Almost any kind of information can be represented in these seemingly simple structures, 

as patterns of the most intricate complexity can be built up. Most primary sources as 

outlined above are capable of digital representation, and when digital they are susceptible 

to manipulation, interrogation, transmission, and cross-linking in ways that are beyond 

the capacity of analogue media. Creating an electronic photocopy of a plain page of text

is not a complex technical process with modern equipment, but being able to then 

automatically recognize all the alphanumeric characters it contains, plus the structural 

layout and metadata elements, is a highly sophisticated operation. Alphanumeric symbols 

are the easiest objects to represent in digital form, and digital text has been around for as 

long as there have been computers. 

As will be clear from what is outlined above, there are diverse source materials in the 

humanities and, using a variety of techniques, they are all amenable to digital capture. 

Nowadays there are many different approaches that can be taken to capture such artifacts, 

dependent upon (a) the materials themselves; (b) the reasons for capturing them; (c) the 

technical and financial resources available to the project; and (d) the potential uses and 

users. One point that should be emphasized is that digital capture, especially capture of 

materials with significant image content, is a skilled process that is best entrusted to 

professionals if high-quality archive images are required. However, for the production of 

medium- or lower-quality materials for Web delivery or for use in teaching, and for 

learning about the issues and processes attendant upon digital capture, scholars and 

students may find it valuable to experiment with the range of medium-cost, good-quality 

capture devices now available commercially. 

There is rarely only one method for the capture of original source materials, and so 

careful planning and assessment of all the cost, quality, conservation, usage, and access 

needs for the resultant product needs to be done in order to decide which of several 

options should be chosen. It is vital when making these decisions that provision is made 

for the long-term survivability of the materials as well as for immediate project needs: the 

costs of sustaining a digital resource are usually greater than those of creating it (and are 

very difficult to estimate) so good planning is essential if the investments made in digital 

conversion are to be profitable. (See chapters 31 and 37, this volume, for more 

information on project design and long-term preservation of digital materials.) However, 

when working with sources held by cultural institutions, the range of digitization options 

may be limited by what that institution is willing to provide: rarely will an institution 

allow outsiders to work with its holdings to create digital surrogates, especially of rare, 

unique, or fragile materials. Many now offer digitization services as adjuncts or 

alternatives to their photographic services, at varying costs, and projects will need to 

order digital files as they would formerly have ordered photographs. See Tanner and 

Deegan (2002) for a detailed survey of such services in the UK and Europe. 

The Advantages and Disadvantages of Digital 

Conversion 

Digital conversion, done properly, is a difficult, time-consuming and costly business, and 

some of the properties of digital objects outlined above can be prove to be 

disadvantageous to the presentation and survivability of cultural materials – the resultant 

digital object, for instance, is evanescent and mutable in a way that the analogue original 

isn't. It can disappear in a flash if a hard drive crashes or a CD is corrupt; it can be 

changed without trace with an ease that forgers can only dream about. However, the

digitization of resources opens up new modes of use for humanists, enables a much wider 

potential audience, and gives a renewed means of viewing our cultural heritage. These 

advantages may outweigh the difficulties and disadvantages, provided the project is well 

thought out and well managed – and this applies however large or small the project might 

be. The advantages of digitization for humanists include: 

• the ability to republish out-of-print materials 

• rapid access to materials held remotely 

• potential to display materials which are in inaccessible formats, for instance, large 

volumes or maps 

• "virtual reunification" – allowing dispersed collections to be brought together 

• the ability to enhance digital images in terms of size, sharpness, color contrast, noise 

reduction, etc. 

• the potential for integration into teaching materials 

• enhanced searchability, including full text 

• integration of different media (images, sounds, video etc.) 

• the potential for presenting a critical mass of materials for analysis or comparison. 

Any individual, group or institution considering digitization of primary sources will need 

to evaluate potential digitization projects using criteria such as these. They will also need 

to assess the actual and potential user base, and consider whether this will change when 

materials are made available in digital form. Fragile originals which are kept under very 

restricted access conditions may have huge appeal to a wide audience when made 

available in a form which does not damage the originals. A good example of this is the 

suffrage banners collection at the Women's Library (formerly known as the Fawcett 

Library). This is a unique collection of large-sized women's suffrage banners, many of 

which are woven from a variety of materials – cotton and velvet, for instance, often with 

applique lettering – which are now in a fragile state. The sheer size of the banners and 

their fragility means that viewing the original is heavily restricted, but digitization of the 

banners has opened up the potential for much wider access for scholarly use, adding a 

vital dimension to what is known about suffrage marches in the UK at the beginning of 

the twentieth century (see ). 

An important question that should be asked at the beginning of any digitization project, 

concerns exactly what it is that the digitization is aiming to capture. Is the aim to produce 

a full facsimile of the original that when printed out could stand in for the original? Some 

projects have started with that aim, and then found that a huge ancillary benefit was 

gained by also having the digital file for online access and manipulation. The Digital

Image Archive of Medieval Music (DIAMM) project, for instance, had as its original 

goal the capture of a specific corpus of fifteenth-century British polyphony fragments for 

printed facsimile publication in volumes such as the Early English Church Music 

(EECM) series. However, early studies showed that there was much to be gained from 

obtaining high-resolution digital images in preference to slides or prints. This was not 

only because of the evidence for growing exploitation of digital resources at that time 

(1997), but also because many of these fragments were badly damaged and digital 

restoration offered opportunities not possible with conventional photography. The project 

therefore decided to capture manuscript images in the best quality possible using highend 

digital imaging equipment, set up according to the most rigorous professional 

standards; to archive the images in an uncompressed form; to enhance and reprocess the 

images in order to wring every possible piece of information from them; to preserve all 

these images – archive and derivative – for the long term. That has proved to be an 

excellent strategy for the project, especially as the image enhancement techniques have 

revealed hitherto unknown pieces of music on fragments that had been scraped down and 

overwritten with text: digitization has not just enhanced existing humanities sources, it 

has allowed the discovery of new ones (see ). 

Digital techniques can also allow a user experience of unique textual materials that is 

simply not possible with the objects themselves. Good-quality images can be integrated 

with other media for a more complete user experience. In 2001, the British Library 

produced a high-quality digital facsimile of the fifteenth-century Sherborne Missal that is 

on display in its galleries on the largest touch screen in the UK. The unique feature of this 

resource is that the pages can be turned by hand, and it is possible to zoom in at any point 

on the page at the touch of a finger on the screen. High-quality sound reproduction 

accompanies the images, allowing users to hear the religious offices which make up the 

text being sung by a monastic choir. This is now available on CD-ROM, and such 

facsimiles of other unique objects are now being produced by the British Library. See 

. 

The Cornell Brittle Books project also started with the aim of recreating analogue 

originals: books printed on acid paper were crumbling away, and the goal was to replace 

these with print surrogates on non-acid paper. Much experimentation with microfilm and 

digitization techniques in the course of this project has produced results which have 

helped to set the benchmarks and standards for the conversion of print materials to digital 

formats all around the world. See Chapman et al. (1999) for further details. 

Another aim of digital conversion might be to capture the content of a source without 

necessarily capturing its form. So an edition of the work of a literary author might be 

rekeyed and re-edited in electronic form without particular reference to the visual 

characteristics of an existing print or manuscript version. Or, if the aim is to add 

searchability to a written source while preserving the visual form, text might be converted 

to electronic form and then attached to the image. 

With a visual source such as a fine art object or early manuscript, what level of 

information is needed? The intellectual content or the physical detail of brushstrokes,

canvas grain, the pores of the skin of the animal used to make the parchment, scratched 

glosses? Is some kind of analysis or reconstruction the aim? The physics department at 

the University of Bologna has developed a digital x-ray system for the analysis of 

paintings (Rossi et al. 2000) and the Beowulf Project, a collaboration between the British 

Library and the University of Kentucky, has used advanced imaging techniques for the 

recovery of letters and fragments obscured by clumsy repair techniques, and 

demonstrated the use of computer imaging to restore virtually the hidden letters to their 

place in the manuscript. See . 

With three-dimensional objects, no true representation of three-dimensional space can be 

achieved within the two-dimensional confines of a computer screen, but some excellent 

results are being achieved using three-dimensional modeling and virtual reality 

techniques. The Virtual Harlem project, for instance, has produced a reconstruction of 

Harlem, New York, during the time of the Harlem Renaissance in the 1920s, using 

modeling and VR immersion techniques. (See 

and Carter 1999.) The Cistercians in Yorkshire project is creating imaginative 

reconstructions of Cistercian abbeys as they might have been in the Middle Ages, using 

three-dimensional modeling and detailed historical research. (See 

.) 

Methods of Digital Capture 

Text 

Humanists have been capturing, analyzing, and presenting textual data in digital form for 

as long as there have been computers capable of processing alphanumeric symbols. 

Thankfully, long gone are the days when pioneers such as Busa and Kenny laboriously 

entered text on punchcards for processing on mainframe machines – now it is an 

everyday matter for scholars and students to sit in the library transcribing text straight 

into a laptop or palmtop computer, and the advent of tablet PC computers could make this 

even easier and more straightforward. Gone, too, are the days when every individual or 

project invented codes, systems, or symbols of their own to identify special features, and 

when any character that could not be represented in ASCII had to be receded in some 

arcane form. Now the work of standards bodies such as the Text Encoding Initiative 

(TEI, ), the World Wide Web Consortium (W3C, ) and 

the Dublin Core Metadata Initiative (DCMI, ) has provided 

standards and sche-mas for the markup of textual features and for the addition of 

metadata to non-textual materials that renders them reusable, interoperable, and 

exchangable. The work done to develop the Unicode standard, too, means that there is a 

standard way of encoding characters for most of the languages of the world so that they 

too are interoperable and exchangable 

(). 

In considering the different features of electronic text, and the methods of their capture, it 

is important to make the distinction between machine-readable electronic text and 

machine-viewable electronic text. A bitmap of a page, for instance, is machine-

displayable, and can show all the features of the original of which it is a representation. 

But it is not susceptible to any processing or editing, so though it is human-readable, it is 

not machine-readable. In machine-readable text, every individual entity, as well as the 

formatting instructions and other codes, is represented separately and is therefore 

amenable to manipulation. Most electronic texts are closer to one of these two forms than 

the other, although there are some systems emerging which process text in such a way as 

to have the elements (and advantages) of both. These are discussed further below. 

For the scholar working intensively on an individual text, there is probably no substitute 

for transcribing the text directly on to the computer and adding appropriate tagging and 

metadata according to a standardized framework. The great benefit of the work done by 

the standards bodies discussed above is that the schemas proposed by them are 

extensible: they can be adapted to the needs of individual scholars, sources and projects. 

Such rekeying can be done using almost any standard word processor or text editor, 

though there are specialist packages like XMetaL which allow the easy production and 

validation of text marked up in XML (Extensible Markup Language). 

Many scholars and students, empowered by improvements in the technology, are 

engaging in larger collaborative projects which require more substantial amounts of text 

to be captured. There are now some very large projects such as the Early English Books 

Online Text Creation Partnership (EEBO TCP, ), 

which is creating accurately keyboarded and tagged editions of up to 25,000 volumes 

from the EEBO corpus of 125,000 volumes of English texts from between 1473 and 

1800, which has been microfilmed and digitized by ProQuest. The tagged editions will be 

linked to the image files. For projects like these, specialist bureaux provide rekeying and 

tagging services at acceptable costs with assured levels of accuracy (up to 99–995 

percent). This accuracy is achieved through double or triple rekeying: two or three 

operators key the same text, then software is used to compare them with each other. Any 

differences are highlighted and then they can be corrected manually. This is much faster, 

less prone to subjective or linguistic errors, and therefore cheaper than proofreading and 

correction. There will always be a need for quality control of text produced by any 

method, but most specialist bureaux give an excellent, accurate, reliable service: the 

competition is fierce in this business, which has driven costs down and quality up. For an 

example of a project that effectively used the double rekeying method see the Old Bailey 

Court Proceedings, 1674 to 1834 (). 

Optical character 

The capture of text by rekeying either by an individual or by a specialist bureau is 

undertaken either because very high levels of accuracy are needed for textual analysis or 

for publication, or because the originals are not susceptible to any automated processes, 

which is often the case with older materials such as the EEBO corpus discussed above. 

While this level of accuracy is sometimes desirable, it comes at a high cost. Where 

modern, high-quality printed originals exist, it may be possible to capture text using 

optical character recognition (OCR) methods, which can give a relatively accurate result. 

Accuracy can then be improved using a variety of automated and manual methods:

passing the text through spellcheckers with specialist dictionaries and thesauri, and 

manual proofing. Research is currently being done to improve OCR packages and to 

enable them to recognize page structures and even add tagging: the European Unionfunded 

METAe Project (the Metadata Engine Project) is developing automatic processes 

for the recognition of complex textual structures, including text divisions such as 

chapters, sub-chapters, page numbers, headlines, footnotes, graphs, caption lines, etc. See 

. Olive Software's Active Paper Archive can also recognize 

complex page structures from the most difficult of texts: newspapers. This is described 

more fully below. OCR techniques were originally developed to provide texts for the 

blind. OCR engines can operate on a wide range of character sets and fonts, though they 

have problems with non-alphabetic character sets because of the large number of 

symbols, and also with cursive scripts such as Arabic. Software can be "trained" on new 

texts and unfamiliar characters so that accuracy improves over time and across larger 

volumes of data. Though this requires human intervention in the early stages, the 

improvement in accuracy over large volumes of data is worth the initial effort. 

Though OCR can give excellent results if (a) the originals are modern and in good 

condition and (b) there is good quality control, projects must consider carefully the costs 

and benefits of deciding between a rekeying approach and OCR. Human time is always 

the most costly part of any operation, and it can prove to be more time-consuming and 

costly to correct OCR (even when it seems relatively accurate) than to go for bureau 

rekeying with guaranteed accuracy. It is worth bearing in mind that what seem like 

accurate results (between 95 and 99 percent, for instance) would mean that there would 

be between 1 and 5 incorrect characters per 100 characters. Assuming there are on 

average 5 characters per word then a 1 percent character error rate equates to a word error 

rate of 1 in 20 or higher. 

OCR with fuzzy matching 

OCR, as suggested above, is an imperfect method of text capture which can require a 

great deal of post-processing if accurate text is to be produced. In an ideal world, one 

would always aim to produce electronic text to the highest possible standards of 

accuracy; indeed, for some projects and purposes, accurate text to the highest level 

attainable is essential, and worth what it can cost in terms of time and financial outlay. 

However, for other purposes, speed of capture and volume are more important than 

quality and so some means has to be found to overcome the problems of inaccurate OCR. 

What needs to be taken into account is the reason a text is to be captured digitally and 

made available. If the text has important structural features which need to be encoded in 

the digital version, and these cannot be captured automatically, or if a definitive edition is 

to be produced in either print or electronic form from the captured text, then high levels 

of accuracy are paramount. If, however, retrieval of the information contained within 

large volumes of text is the desired result, then it may be possible to work with the raw 

OCR output from scanners, without post-processing. A number of text retrieval products 

are now available which allow searches to be performed on inaccurate text using "fuzzy 

matching" techniques. However, at the moment, fuzzy searching will only work with 

suitable linguistic and contextual dictionaries, and therefore is pretty much limited to the

English language. Other languages with small populations of speakers are poorly 

represented, as are pre-1900 linguistic features. 

Hybrid solutions: page images with underlying searchable text 

An increasing number of projects and institutions are choosing to deliver textual content 

in digital form through a range of hybrid solutions. The user is presented with a facsimile 

image of the original for printing and viewing, and attached to each page of the work is a 

searchable text file. This text file can be produced by rekeying, as in the EEBO project 

described above, or by OCR with or without correction. Decisions about the method of 

production of the underlying text will depend on the condition of the originals and the 

level of accuracy of retrieval required. With the EEBO project, the materials date from 

the fifteenth to the eighteenth centuries, and they have been scanned from microfilm, so 

OCR is not a viable option. The anticipated uses, too, envisage detailed linguistic 

research, which means that retrieval of individual words or phrases will be needed by 

users. The highest possible level of accuracy is therefore a requirement for this project. 

The Forced Migration Online (FMO) project based at the Refugee Studies Centre, 

University of Oxford, is taking a different approach. FMO is a portal to a whole range of 

materials and organizations concerned with the study of the phenomenon of forced 

migration worldwide, with content contributed by an international group of partners. One 

key component of FMO is a digital library of gray literature and of journals in the field. 

The digital library is produced by attaching text files of uncorrected OCR to page images: 

the OCR text is used for searching and is hidden from the user; the page images are for 

viewing and printing. What is important to users of FMO is documents or parts of 

documents dealing with key topics, rather than that they can retrieve individual instances 

of words or phrases. This type of solution can deliver very large volumes of material at 

significantly lower cost than rekeying, but the trade-offs in some loss of accuracy have to 

be understood and accepted. Some of the OCR inaccuracies can be mitigated by using 

fuzzy search algorithms, but this can give rise to problems of over-retrieval. FMO 

() uses Olive Software's Active Paper Archive, a product 

which offers automatic zoning and characterization of complex documents, as well as 

OCR and complex search and retrieval using fuzzy matching. See Deegan (2002) and 

. 

One document type that responds well to hybrid solutions is newspapers, which are highvolume, 

low-value (generally), mixed media, and usually large in size. Newspaper 

digitization is being undertaken by a number of companies, adopting different capture 

strategies and business models. ProQuest are using a rekeying solution to capture full text 

from a number of historic newspapers, and are selling access to these through their portal, 

History Online (). These newspapers include The 

Times, the New York Times and the Wall Street Journal. A number of Canadian 

newspapers are digitizing page images and offering searchability using the Cold North 

Wind software program (); and the TIDEN project, a 

large collaboration of Scandinavian libraries, is capturing content using advanced OCR 

and delivering page images with searchability at the page level using a powerful

commercial search engine, Convera's RetrievalWare (). OCLC (the 

Online Computer Library Center) has an historic newspaper digitization service which 

provides an end-to-end solution: the input is microfilm or TIFF images, the output a fully 

searchable archive with each individual item (article, image, advertisement) separated 

and marked up in XML. Searches may be carried out on articles, elements within articles 

(title, byline, text), and image captions. OCLC also uses Olive Software's Active Paper 

Archive. 

Images 

As we suggest above, humanists deal with a very large range of image-based materials, 

and there will be different strategies for capture that could be employed, according to 

potential usage and cost factors. Many humanists, too, may be considering digital images 

as primary source materials rather than as secondary surrogates: increasingly 

photographers are turning from film to digital, and artists are creating digital art works 

from scratch. Many digital images needed by humanists are taken from items outside 

their control: objects that are held in cultural institutions. These institutions have their 

own facilities for creating images which scholars and students will need to use. If they 

don't have such facilities, analogue surrogates (usually photographic) can be ordered and 

digitization done from the surrogate. The costs charged by institutions vary a great deal 

(see Tanner and Deegan 2002). Sometimes this depends on whether a reproduction fee is 

being charged. Institutions may offer a bewildering choice of digital and analogue 

surrogate formats with a concomitant complexity of costs. A thorough understanding of 

the materials, the digitization technologies and the implications of any technical choices, 

the metadata that need to be added to render the images useful and usable (which is 

something that will almost certainly be added by the scholar or student), and what the 

potential cost factors might be are all essential for humanists embarking on digital 

projects, whether they will be doing their own capture or not. Understanding of the 

materials is taken as a given here, so this section will concentrate upon technical issues. 

Technical issues in image capture 

Most image materials to be captured by humanists will need a high level of fidelity to the 

original. This means that capture should be at an appropriate resolution, relative to the 

format and size of the original, and at an appropriate bit depth. As outlined above, 

resolution is the measure of information density in an electronic image and is usually 

measured in dots per inch (dpi) or pixels per inch (ppi). The more dots per inch there are, 

the denser the image information provided. This can lead to high levels of detail being 

captured at higher resolutions. The definition of "high" resolution is based upon factors 

such as original media size, the nature of the information, and the eventual use. Therefore 

600 dpi would be considered high-resolution for a photographic print, but would be 

considered low-resolution for a 35 mm slide. Careful consideration must be given to the 

amount of information content required over the eventual file size. It must be 

remembered that resolution is always a factor of two things: (1) the size of the original 

and (2) the number of dots or pixels. Resolution is expressed in different ways according 

to what particular part of the digital process is being discussed: hardware capabilities,

absolute value of the digital image, capture of analogue original, or printed output. 

Hardware capabilities are referred to differently as well: resolution calculated for a 

flatbed scanner, which has a fixed relationship with originals (because they are placed on 

a fixed platen and the scanner passes over them at a fixed distance) is expressed in dpi. 

With digital cameras, which have a variable dpi in relation to the originals, given that 

they can be moved closer or further away, resolution is expressed in absolute terms, 

either by their x and y dimensions (12,000 × 12,000, say, for the highest-quality 

professional digital cameras) or by the total number of pixels (4 million, for instance, for 

a good-quality, compact camera). The digital image itself is best expressed in absolute 

terms: if expressed in dpi, the size of the original always needs to be known to be 

meaningful. 

The current choices of hardware for digital capture include flatbed scanners, which are 

used for reflective and transmissive materials. These can currently deliver up to 5,000 

dpi, but can cost tens of thousands of dollars, so most projects can realistically only 

afford scanners in the high-end range of 2,400 to 3,000 dpi. Bespoke 35 mm film 

scanners, which are used for transmissive materials such as slides and film negatives, can 

deliver up to 4,000 dpi. Drum scanners may also be considered as they can deliver much 

higher relative resolutions and quality, but they are generally not used in this context as 

the process is destructive to the original photographic transparency and the unit cost of 

creation is higher. Digital cameras can be used for any kind of material, but are generally 

recommended for those materials not suitable for scanning with flatbed or film scanners: 

tightly bound books or manuscripts, art images, three-dimensional objects such as 

sculpture or architecture. Digital cameras are becoming popular as replacements for 

conventional film cameras in the domestic and professional markets, and so there is now 

a huge choice. High-end cameras for purchase by image studios cost tens of thousands of 

dollars, but such have been the recent advances in the technologies that superb results can 

be gained from cameras costing much less than this – when capturing images from 

smaller originals, even some of the compact cameras can deliver archive-quality scans. 

However, they need to be set up professionally, and professional stands and lighting must 

be used. 

For color scanning, the current recommendation for bit depth is that high-quality 

originals be captured at 24 bit, which renders more than 16 million colors – more than the 

human eye can distinguish, and enough to give photorealistic output when printed. For 

black and white materials with tone, 8 bits per pixel is recommended, which gives 256 

levels of gray, enough to give photorealistic printed output. 

The kinds of images humanists will need for most purposes are likely to be of the highest 

possible quality for two reasons. First, humanists will generally need fine levels of detail 

in the images, and, secondly, the images will in many cases have been taken from rare or 

unique originals, which might also be very fragile. Digital capture, wherever possible, 

should be done once only, and a digital surrogate captured that will satisfy all anticipated 

present and future uses. This surrogate is known as the "digital master" and should be 

kept under preservation conditions. (See chapter 37, this volume.) Any manipulations or 

post-processing should be carried out on copies of this master image. The digital master

will probably be a very large file: the highest-quality digital cameras (12,000 × 12,000 

pixels) produce files of up to 350Mb, which means that it is not possible to store more 

than one on a regular CD-ROM disk. A 35 mm color transparency captured at 2,700 dpi 

(the norm for most slide scanners) in 24-bit color would give a file size of 25 Mb, which 

means that around 22 images could be stored on one CD-ROM. The file format generally 

used for digital masters is the TIFF (Tagged Image File Format), a de facto standard for 

digital imaging. There are many other file formats available, but TIFF can be 

recommended as the safest choice for the long term. The "Tagged" in the title means that 

various types of information can be stored in a file header of the TIFF files. 

The actual methods of operation of scanning equipment vary, and it is not the intention of 

this chapter to give details of such methodologies. There are a number of books which 

give general advice, and hardware manufacturers have training manuals and online help. 

Anyone who wishes to learn digital capture techniques in detail is advised to seek out 

professional training through their own institution, or from one of the specialist 

organizations offering courses: some of these are listed in the bibliography at the end of 

this chapter. 

Compression and derivatives 

It is possible to reduce file sizes of digital images using compression techniques, though 

this is often not recommended for the digital masters. Compression comes in two forms: 

"lossless", meaning that there is no loss of data through the process, and "lossy", meaning 

that data is lost, and can never be recovered. There are two lossless compression 

techniques that are often used for TIFF master files, and which can be recommended here 

– the LZW compression algorithm for materials with color content, and the CCITT 

Group 4 format for materials with 1-bit, black and white content. 

Derivative images from digital masters are usually created using lossy compression 

methods, which can give much greater reduction in file sizes than lossless compression 

for color and greyscale images. Lossy compression is acceptable for many uses of the 

images, especially for Web or CD-ROM delivery purposes. However, excessive 

compression can cause problems in the viewable images, creating artifacts such as 

pixelation, dotted or stepped lines, regularly repeated patterns, moire, halos, etc. For the 

scholar seeking the highest level of fidelity to the originals, this is likely to be 

unacceptable, and so experimentation will be needed to give the best compromise 

between file size and visual quality. The main format for derivative images for delivery 

to the web or on CD-ROM is currently JPEG. This is a lossy compression format that can 

offer considerable reduction in file sizes if the highest levels of compression are used, but 

this comes at the cost of some compromise of quality. However, it can give color 

thumbnail images of only around 7 KB and screen resolution images of around 60 KB – a 

considerable benefit if bandwidth is an issue.

Audio and video capture 

As technology has advanced since the 1990s, more and more humanists are becoming 

interested in the capture of time-based media. Media studies is an important and growing 

area, and historians of the modern period too derive great benefit from having digital 

access to time-based primary sources such as news reports, film, etc. Literary scholars 

also benefit greatly from access to plays, and to filmed versions of literary works. 

In principle the conversion of audio or video from an analogue medium to a digital data 

file is simple, it is in the detail that complexity occurs. The biggest problem caused in the 

digital capture of video and audio is the resultant file sizes: 

There is no other way to say it. Video takes up a lot of everything; bandwidth, storage 

space, time, and money. We might like to think that all things digital are preferable to all 

things analog but the brutal truth is that while analog formats like video might not be 

exact or precise they are remarkably efficient when it comes to storing and transmitting 

vast amounts of information. 

(Wright 2001a) 

Raw, uncompressed video is about 21 MB/sec. 

(Wright 2001b) 

There are suppliers that can convert audio and video, and many scholars will want to 

outsource such work. However, an understanding of the processes is of great importance. 

The first stage in the capture process is to have the original video or audio in a format that 

is convertible to digital formats. There are over thirty types of film and video stock and 

many types of audio formats that the original content may be recorded upon. Most 

humanists, however, should only need to concern themselves with a limited number of 

formats, including: 

• VHS or Betamax video 

• mini-DV for digital video cameras 

• DAT tape for audio 

• cassette tape for audio 

• CD-ROM for audio 

The equipment needed to capture video and audio includes: 

• video player capable of high-quality playback of VHS and mini-DV tapes

• hi-fi audio player capable of high-quality playback of DAT and cassette tapes 

• small TV for review and location of video clips 

• high-quality speakers and/or headphones for review and location of audio clips 

Once the suitable video or audio clip has been identified on the original via a playback 

device, this has to be connected to the capture device for rendering in a digital video 

(DV) or digital audio (DA) format upon a computer system. This is normally achieved by 

connecting input/output leads from the playback device to an integrated digital capture 

card in the computer or via connection to a device known as a "breakout box." The 

breakout box merely allows for more types of input/output leads to be used in connecting 

to and from the playback device. Whatever the method of connection used, there must be 

a capture card resident in the machine to allow for digital data capture from the playback 

device. Usually, a separate capture card is recommended for video and for audio: 

although all video capture cards will accept audio input, the quality required for pure 

audio capture is such that a bespoke capture card for this purpose is recommended; 

indeed, it is probably best to plan the capture of audio and video on separate PCs – each 

card requires its own Input/Output range, Interrupt Request number and its own software 

to control the capture. These are temperamental and tend to want to occupy the same I/O 

range, interrupts and memory spaces, leading to system conflicts and reliability problems. 

This of course adds to cost if a project is planning to capture both audio and video. 

The benefit of having a capture card is that it introduces render-free, real-time digital 

video editing, DV and analogue input/output, and this is usually augmented with direct 

output for DVD, and it also provides Internet streaming capabilities, along with a suite of 

digital video production tools. Render-free real-time capture and editing is important 

because capture and editing can then happen without the long waits (which can be many 

minutes) associated with software packages for rendering and editing. The capture from 

video or audio will be to DV or DA, usually at a compression rate of around 5:1 which 

gives 2.5–3.5 MB/sec. This capture is to a high standard even though there is 

compression built into the process. It is also too large for display and use on the Internet 

and so further compression and file format changes are required once edited into a 

suitable form for delivery. 

Editing of Captured Content 

The editing of the content captured is done via software tools known as non-linear editing 

suites. These allow the content to be manipulated, edited, spliced, and otherwise changed 

to facilitate the production of suitable content for the prospective end user. The ability to 

do this in real time is essential to the speed and accuracy of the eventual output. Also, the 

editing suite should have suitable compressors for output to Web formats and Internet 

streaming.

Compressors for the Web 

When viewing video or listening to audio on the Web the content has to go through a 

process of compression and de-compression (CODEC). There is initial compression at 

the production end using a suitable CODEC (e.g., Sorenson, Qdesign) to gain the desired 

file size, frames per second, transmission rate, and quality. This content may then be 

saved in a file format suitable for the architecture of the delivery network and expected 

user environment – possibly QuickTime, Windows Media or Real. The user then uses 

viewer software to decompress the content and view/hear the content. 

Compression is a difficult balancing act between gaining the smallest file size and 

retaining suitable quality. In video, compression works by taking a reference frame that 

contains all the information available and then subsequent frames are represented as 

changes from the reference frame. The number of frames between the reference frames is 

a defining factor in the compression and quality – the fewer reference frames the more 

compressed the file but the lower quality the images are likely to be. However, if the 

number of reference frames is increased to improve visual quality then the file size will 

also rise. As the nature of the content in video can differ radically, this compression 

process has to be done on a case by case basis. A video of a "talking head" type interview 

will require fewer reference frames than, say, sports content because the amount of 

change from frame to frame is less in the "talking head" example. Thus higher 

compression is possible for some content than for others if aiming for the same visual 

quality – there is no "one size fits all" equation in audio and video compression. 

It is generally assumed with images that the main cost will be in the initial capture and 

that creating surrogates for end-user viewing on the Internet will be quick and cheap. In 

the video sphere this paradigm is turned on its head, with the initial capture usually 

cheaper than the creation of the compressed surrogate for end-user viewing as a direct 

consequence of the increased amount of human intervention needed to make a goodquality 

Internet version at low bandwidth. 

Metadata 

Given that humanists will almost certainly be capturing primary source data at a high 

quality and with long-term survival as a key goal, it is important that this data be 

documented properly so that curators and users of the future understand what it is that 

they are dealing with. Metadata is one of the critical components of digital resource 

conversion and use, and is needed at all stages in the creation and management of the 

resource. Any creator of digital objects should take as much care in the creation of the 

metadata as they do in the creation of the data itself – time and effort expended at the 

creation stage recording good-quality metadata is likely to save users much grief, and to 

result in a well-formed digital object which will survive for the long term. 

It is vitally important that projects and individuals give a great deal of thought to this 

documentation of data right from the start. Having archive-quality digital master files is 

useless if the filenames mean nothing to anyone but the creator, and there is no indication

of date of creation, file format, type of compression, etc. Such information is known as 

technical or administrative metadata. Descriptive metadata refers to the attributes of the 

object being described and can be extensive: attributes such as: "title", "creator", 

"subject", "date", "keywords", "abstract", etc. In fact, many of the things that would be 

catalogued in a traditional cataloguing system. If materials are being captured by 

professional studios or bureaux, then some administrative and technical metadata will 

probably be added at source. Or it may be possible to request or supply project-specific 

metadata. Descriptive metadata can only be added by experts who understand the nature 

of the source materials, and it is an intellectually challenging task in itself to produce 

good descriptive metadata. 

Publications 

Understanding the capture processes for primary source materials is essential for 

humanists intending to engage in digital projects, even if they are never going to carry out 

conversion activities directly. Knowing the implications of the various decisions that 

have to be taken in any project is of vital importance for short- and long-term costs as 

well as for the long-term survivability of the materials to which time, care, and funds 

have been devoted. The references below cover most aspects of conversion for most 

types of humanities sources, and there are also many websites with bibliographies and 

reports on digital conversion methods. The technologies and methods change constantly, 

but the underlying principles outlined here should endure. 


Baca, M., (ed.) (1998). Introduction to Metadata, Pathways to Digital Information. Los 

Angeles, CA: Getty Research Institute. 

Carter, B. (1999). From Imagination to Reality: Using Immersion Technology in an 

African American Literature Course. Literary and Linguistic Computing 14: 55–65. 

Chapman, S., P. Conway, and A. R. Kenney (1999). Digital Imaging and Preservation 

Microfilm: The Future of the Hybrid Approach for the Preservation of Brittle Books. 

RLG DigiNews. Available at http://www.rlg.org/preserv/diginews/diginews3-1.html. 

Davies, A. and P. Fennessey (2001). Digital Imaging for Photographers. London: Focal 

Press. 

Deegan, M., E. Steinvel, and E. King (2002). Digitizing Historic Newspapers: Progress 

and Prospects. RLG DigiNews. Available at 

http://www.rlg.org/preserv/diginews/diginews6-4.html#feature2. 

Deegan, M. and S. Tanner (2002). Digital Futures: Strategies for the Information Age. 

London: Library Association Publishing.

Feeney, M., (ed.) (1999). Digital Culture: Maximising the Nation's Investment. London: 

National Preservation Office. 

Getz, M. (1997). Evaluating Digital Strategies for Storing and Retrieving Scholarly 

Information. In S. H. Lee (ed.), Economics of Digital Information: Collection, Storage 

and Delivery. Binghamton, NY: Haworth Press. 

Gould, S. and R. Ebdon (1999). IFLA/UNESCO Survey on Digitisation and Preservation. 

IFLA Offices for UAP and International Lending Available at 

http://www.unesco.org/webworld/mdm/survey_index_en.html. 

Hazen, D., J. Horrell, and J. Merrill-Oldham (1998). Selecting Research Collections for 

Digitization CLIR. Available at http://www.clir.org/pubs/reports/hazen/pub74.html. 

Kenney, A. R. and O. Y. Rieger, (eds.) (2000). Moving Theory into Practice: Digital 

Imaging for Libraries and Archives. Mountain View, CA: Research Libraries Group. 

Klijn, E. and Y. de Lusenet (2000). In the Picture: Preservation and Digitisation of 

European Photographic Collections. Amsterdam: Koninklijke Bibliotheek. 

Lacey, J. (2002). The Complete Guide to Digital Imaging. London: Thames and Hudson. 

Lagoze, C. and S. Payette (2000). Metadata: Principles, Practices and Challenges. In A. 

R. Kenney and O. Y. Rieger (eds.), Moving Theory into Practice: Digital Imaging for 

Libraries and Archives (pp. 84–100). Mountain View, CA: Research Libraries Group. 

Lawrence, G. W., et al. (2000). Risk Management of Digital Information: A File Format 

Investigation. Council on Library and Information Resources. Available at 

http://www.clir.org.pubs/reports/reports.html. 

Parry, D. (1998). Virtually New: Creating the Digital Collection. London: Library and 

Information Commission. 

Robinson, P. (1993). The Digitization of Primary Textual Sources. London: Office for 

Humanities Communication Publications, 3, King's College, London. 

Rossi, M. F. Casali, A. Bacchilega, and D. Romani (2000). An Experimental X-ray 

Digital Detector for Investigation of Paintings. 15th World Conference on 

NonDestructive Testing, Rome, 15–21 October. 

Smith, A. (1999). Why Digitize? CLIR. Available at 

http://www.clir.org/pubs/abstract/pub80.html. 

Tanner, S. and M. Deegan (2002). Exploring Charging Models for Digital Cultural 

Heritage. Available at http://heds.herts.ac.uk.

Watkinson, J. (2001). Introduction to Digital Video. London: Focal Press. 

Wright, G. (2001a). Building a Digital Video Capture System. Part I, Tom's Hardware 

Guide. Available at http://www.tomshardware.com/video/20010524/. 

Wright, G. (2001b). Building a Digital Video Capture System. Part II, Tom's Hardware 

Guide. Available at http://www.tomshardware.com/video/20010801/. 

Courses 

School for Scanning, North East Documentation Center, . 

Cornell University Library, Department of Preservation and Conservation, 

. 

SEPIA (Safeguarding European Photographic Images for Access), project training 

courses, . 

33. 

Text Tools 

John Bradley 

One should not be surprised that a central interest of computing humanists is tools to 

manipulate text. For this community, the purpose of digitizing text is to allow the text to 

be manipulated for scholarly purposes, and text tools provide the mechanisms to support 

this. Some of the scholarly potential of digitizing a text has been recognized from the 

earliest days of computing. Father Roberto Busa's work on the Index Thomisticus (which 

began in the 1940s) involved the indexing of the writings of Thomas Aquinas, and arose 

out of work for his doctoral thesis when he discovered that he needed to systematically 

examine the uses of the preposition in. Busa reports that because of this he imagined 

using "some sort of machinery" that would make it possible. Later, but still relatively 

early in the history of humanities computing, the writings of John B. Smith recognized 

some of the special significance of text in digitized form (see a relatively late piece 

entitled "Computer Criticism", Smith 1989). Indeed, even though computing tools for 

manipulating text have been with us for many years, we are still in the early stages of 

understanding the full significance of working with texts electronically. 

At one time an important text tool that one would have described in an article such as this 

would have been the word processor. Today, however, the word processor has become 

ubiquitous both inside and outside the academy and it is no longer necessary to describe 

its benefits or categorize its applications. Today many people believe that the World 

Wide Web can be at the center of computing for humanists, and much discussion has 

focused on the significance of presenting materials on the WWW for a worldwide

audience. Although some of the tools I will describe here could greatly facilitate the 

preparation of materials for the WWW, I will not be writing very much about HTML or 

the significance of using it. Instead, this article will focus on tools that can manipulate 

texts in ways that might usefully support the development of scholarship on those texts 

rather than merely prepare and present them. 

Some of the tools described here have been developed specifically to support humanities 

scholarship. The intellectual origins of many in this group flow directly from the wordoriented 

work of Father Busa and other early researchers, and these ones tend to be 

mainly designed to support a certain kind of word- or wordform-oriented inquiry. 

Another important group of tools described later were not developed by humanists and 

have a broad range of applications in general computing, although they have proven 

themselves to be powerful tools when applied to scholarly tasks. The chapter finishes 

with a brief description of TuStep – a system developed by Wilhelm Ott that is 

potentially useful in a range of applications within and outside the humanities, but was 

developed with a humanities focus. 

Not surprisingly, given their very different origins, the two groups present very different 

challenges to the humanities researcher who is beginning to use them. The first group of 

tools, generally designed to support text analysis, are relatively straightforward to 

understand once one is operating within a tightly defined problem domain. The generalpurpose 

tools, however, are not so easily categorized, and can perform a very broad range 

of tasks on a text. Because they are not focused on a specific problem domain, as the 

"Text Analysis" tools are, they contain very abstract components, and the difficulty for 

the beginner is to understand whether, and then how, these abstract elements can be 

applied to any particular problem. 

Those who begin with the tools that are specially developed for textual analysis or critical 

work often find that they wish to do something that goes beyond what their software does 

– for example, apply a statistical program to some materials that the software has given 

them. Often the form of the data generated by their first program is different from that 

needed by the second program. As we will see, one of the important tasks of the generalpurpose 

tools described later in this chapter is to provide ways to bridge these gaps. 

Tools to Support Text Analysis 

Paradoxically, the task of writing general-purpose software is highly specialized; it is also 

both costly and difficult. Perhaps for this reason most humanities computing software 

available "off the shelf" (with the exception of TuStep) performs a relatively simple set of 

functions. Of those pieces of software that do exist, many are designed to assist with text 

analysis. 

Wordform-oriented tools 

Most of this section describes software that supports the analysis of texts through wordforms, 

and relies on the KWIC (Key Word In Context) and related displays. These tools

are the most accessible for new users, and often are taught to students. See, for example, 

Hawthorne's (1994) article for examples of student uses of TACT. An ancestor of many 

of the currently available wordform-oriented programs is the Oxford Concordance 

Program (OCP), with which they share a number of concepts. 

All the "wordform" programs described in this section provide a relatively limited set of 

operations. Nonetheless, there are reports of them being useful in a broad range of textresearch 

activities, including: 

• language teaching and learning; 

• literary research; 

• linguistic research, including corpus linguistics, translation and language engineering, 

supported at least at a relatively basic level (although see the next paragraph); 

• lexicography; and 

• content analysis in many disciplines including those from both the humanities and the 

social sciences. 

As the category suggests, they all operate on wordforms and to work they must be able to 

automatically collect the wordforms from the provided texts. Most of the programs 

described here are thus oriented towards alphabetic languages, and allow one to identify 

both the characters that are acting as letters in the target language, and to identify 

characters that may appear in a word but are not letters (such as a hyphen). 

At the center of most of these programs is the word list – a list of all wordforms that 

occur in the processed text, generally shown with a count of how many times that 

wordform occurred and usually sorted in alphabetical order or by frequency, with the 

most frequently occurring forms listed first. In most programs, users can ask to view 

information about a wordform by selecting it from this list, e.g., "show me all 

occurrences of the word 'avuncular.'" In some programs one types in a query to select the 

words one wants to view. Wildcard schemes in queries can support word stemming, e.g., 

"show me all occurrences of the word beginning 'system,'" and some allow for the 

wildcard to appear in other places than the end, thereby allowing for queries such as 

"show me all occurrences of words ending in 'ing.'" Some pieces of software allow for 

selection based around the occurrence close together of one or more wordforms. An 

example of selection by phrase would be "show me where the phrase 'the* moon' occurs" 

(where "*" means any word). A collocation selection query would allow one to ask 

"show me where the words 'moon' and 'gold' appear close together." 

After you have selected wordforms of interest the programs are ready to show results. All 

the tools offer a KWIC display format – similar to that shown below – where we see 

occurrences of the word "sceptic" in David Hume's Dialogues Concerning Natural 

Religion.

Sceptic (11) 

[1,47] abstractions. In vain would the sceptic make a distinction 

[1,48] to science, even no speculative sceptic, pretends to entertain 

[1,49] and philosophy, that Atheist and Sceptic are almost synonymous. 

[1,49] by which the most determined sceptic must allow himself to 

[2,60] of my faculties? You might cry out sceptic and railer, as much as 

[3,65] profession of every reasonable sceptic is only to reject 

[8,97] prepare a compleat triumph for the Sceptic; who tells them, that 

[11,121] to judge on such a subject. I am Sceptic enough to allow, that 

[12,130] absolutely insolvable. No Sceptic denies that we lie 

[12,130] merit that name, is, that the Sceptic, from habit, caprice, 

[12,139] To be a philosophical Sceptic is, in a man of 

A viewer uses the KWIC display to see what the context of the word says about how it is 

used in the particular text. 

The KWIC display shown above also indicates where in the text the word occurs: the first 

of the two numbers in square brackets is the number of the dialogue, and the second is a 

page number in a standard edition. This information comes from textual markup. Markup 

can often provide useful information about the text's structure, and some software allows 

the markup to be identified and used in several ways: 

(a) As shown in the KWIC display above, it can be used to indicate where a particular 

word occurrence occurs in the text – e.g., "paragraph 6 of chapter 2", or "spoken by 

Hamlet." 

(b) It can be used to select wordform occurrences that might be of interest – e.g., "tell me 

all the places where Hamlet speaks of a color." 

(c) It can be used to define a collocation range – e.g., "show me all the words that occur 

in the same sentence as a color-word." 

(d) It can be used as the basis for a distribution – "show me how a word is used by each 

speaker." 

(e) It can be used to delimit text to be ignored for concording – e.g., "do not include text 

found in the prologue in your index." 

Although working with markup is clearly useful, support for XML/SGML markup is 

poor in current wordform programs. None of the wordform-oriented software described

here can make good use of the full expressive power of TEI (the Text Encoding 

Initiative) XML markup, and some cannot even properly recognize the appearance of 

markup in a text document so that they can at least ignore it. A couple of the programs 

recognize a rather more simple form of markup – called COCOA markup after the piece 

of concordance software (COCOA, for count and concordance generation on the Atlas) 

for which it was first designed. A typical COCOA tag might look like this: "." The angle brackets announce the presence of a COCOA tag, and the tag's 

content is divided into two parts. The first part (here, an "S") announces the kind of thing 

being identified – "S" presumably stands for "speaker." The second part announces that 

starting with the spot in the text where the tag has appeared, the Speaker is "Hamlet." 

Although perhaps at first glance the COCOA tagging scheme might remind one a little of 

XML or SGML markup, it is technically quite different, and is structurally largely 

incompatible with texts marked up in XML/SGML. 

Some wordform software offers other kinds of displays for results. A distribution graph, 

for example, could show graphically how the word use was distributed through the text, 

and might allow the user to see areas in the text where usage was concentrated. Some 

programs provide displays that focus on words that appear near to a selected word. 

TACT's collocation display, for example, is a list of all wordforms that occur near to 

selected words, ordered so that those with proportionally the most of their occurrences 

near the selected words (and therefore perhaps associated with the selected word itself) 

appear near the top of the list. 

Some of the programs allow for some degree of manual lemmatization of wordforms – 

the grouping together of different forms that constitute a single word into a single group, 

although none of the wordform-oriented tools include software to do this task 

automatically. Stemming with wildcards can help to lemmatize many words in languages 

where the initial word stems can be used to select possible related wordforms. Several 

programs, including Concordance, WordSmith Tools, and TACT, allow one to store a list 

of wordforms that belong in a single lemma in a separate file and automatically apply 

these against a text. A group of words for the verb "to be" in English would contain the 

various wordforms that make it up: "am, are, is, be, was, were", etc. By applying this list 

the software would group these various wordforms under a single heading: "to be." Of 

course, the task of doing a proper lemmatization is often more complex than this. For one 

thing, one must separate homographs (a single wordform that might have several 

different meanings, such as "lead" in English). To allow the machine to do this task 

automatically would require it to understand the use of each word occurrence in its 

particular context -something outside the capabilities of any of the software reviewed 

here. TACT, however, allows the user to review the groups created by the simple 

wordform selection process and individually exclude wordform occurrences – thereby 

allowing the separation of homographs to be carried out manually, although at the cost of 

a significant amount of work. 

The following paragraphs outline some of the important characteristics of the pieces of 

wordform-oriented software that are available today. (Only software readily available at 

the time of writing will be discussed – thus software such as OCP or WordCruncher,

important and influential as they were in their day, are not described.) There are many 

differences in detail that might make one tool better suited to a task at hand than another. 

If you choose and use only one, you may need to direct your investigations to suit the 

program's strengths – a case of the tool directing the work rather than the other way 

around. 

Concordance 3.0 is a Windows-based program written by R. J. S. Watt, who is Senior 

Lecturer in English at the University of Dundee. An evaluation copy (30 days) of the 

software is available at , with information about 

current prices. As the name suggests, the program focuses on providing a good KWIC 

Concordance generation and display engine. The user loads a text, selects words from the 

resultant word list (displayed on the left) and immediately sees a KWIC display on the 

right. The program supports some degree of lemmatization, and makes limited use of 

COCOA-like tags. I found its Windows interface quite easy to use. Concordance has 

been used with languages written in a range of alphabets. Furthermore, Professor Marjoie 

Chann, Ohio State University, reports success in using Concordance 3–0 even with 

Chinese, Japanese, and Korean texts on Windows 2000/XP. See 

for details. 

Concordance 3–0 has the ability to transform its concordance into what becomes a large 

number of interconnected HTML pages that together could be made available on the 

WWW – a "Web Concordance." The resulting web pages present the full KWIC 

concordance, with links between the headwords in one frame, and the KWIC contexts in 

another. In the sample text that I tried (0.127 MB in size, containing about 23,300 words) 

the resulting concordance website was 7.86 MB, and contained 204 interlinked HTML 

documents. 

MonoConc and ParaConc is software developed by Michael Barlow at the Department of 

Linguistics, Rice University. See , and A 

Guide to MonoConc written by Barlow at . 

MonoConc is sold by the Athelstan company (Houston, Texas). With MonoConc you 

enter a search string to select words and it then generates a KWIC concordance. The 

search query language supports various kinds of wildcards, so that, for example, "speak*" 

will select all words beginning with "speak." There is also a query specification language 

(similar to regular expressions) that provides a more sophisticated pattern-matching 

capability. MonoConc also provides a way of sorting the KWIC display by preceding or 

following words – an option that causes similar phrases involving the selected word to be 

more evident. Like other programs described here, MonoConc can also generate some 

wordform frequency lists. ParaConc provides similar functions when one is working with 

parallel texts in more than one language. 

TACT is a suite of programs developed first by John Bradley and Lidio Presutti, and then 

somewhat further developed by Ian Lancashire and Mike Stairs, at the University of 

Toronto. The software was developed in the late 1980s and early 1990s and presents a 

DOS, rather than Windows, interface. It is still used, in spite of this limitation, by a 

significant user community. The software is available free from

– however, there is no manual for it 

available from that site. Instead, a user's guide – containing both the suite of programs 

and a selection of electronic texts on CD-ROM – can be purchased from the Modern 

Language Association (MLA). 

Although TACT's DOS-based interface is difficult to learn for users familiar with 

Windows, TACT remains popular with some users because it presents a relatively broad 

range of functions within the general wordform-oriented context described above, 

including the most sophisticated support for textual markup and word collocation of any 

of the programs described here. TACT works immediately with texts from the standard 

western European languages, and can be extended to work with other languages based on 

the Roman alphabet, and with classical Greek, although how to do this is unfortunately 

poorly documented. Compared to other programs described here, TACT is limited in the 

size of texts it can handle. Although users have reported working successfully with texts 

as large as the Bible, that is roughly TACT's upper limit. New users will find the system 

difficult at the beginning because of the DOS interface, the need to purchase the User's 

Guide to receive any written help, and the lack of technical support. TACTweb 

(http://tactweb.humanities.mcmaster.ca/) allows TACT's textbases to be searched by 

queries over the WWW. 

WordSmith Tools Version 3–0 is a suite of programs developed by Mike Scott 

(Department of English Language and Literature, University of Liverpool) and now 

distributed by the Oxford University Press. A version may be downloaded for evaluation 

from or the author's website at 

. Word-Smith Tools consists of six programs, one of 

which is a Controller component – used to invoke the others – and several others provide 

facilities for the basic text editing of large text files, and will not be described further 

here. Most users will begin by pointing the WordList component at one or more text 

documents to generate a word list. From there the user can select some words to form the 

basis for KWIC concordance, displayed in the Concord component. An unusual feature 

of WordSmith Tools is its KeyWords component. This allows the word list generated by 

your text to be compared with a control word list (for example, a word list generated 

from the British National Corpus is available over the WWW for this purpose), and the 

words that occur proportionally more frequently in your text than in the control text are 

emphasized in the resulting display. 

Although WordSmith Tools has only limited facilities to handle text with markup, it 

provides a richer set of functions to support statistical analysis than the other programs 

mentioned here. WordSmith Tools are Windows-based, but the modular nature of the 

program, where the word list, concordance, and keyword functions are packaged in 

different, albeit linked, components, made the software slightly more difficult for me to 

master than some of the other programs mentioned here. 

Readers of this chapter may have noted that all the software mentioned so far runs on the 

IBM PC and/or Windows. Users of the Apple Macintosh have a smaller number of 

programs to choose from. The AnyText search engine, marketed by Linguist's Software, is

described as "a HyperCard -based Full Proximity Boolean Search Engine and Index 

Generator that allows you to create concordances and do FAST word searches on 

ordinary text files in English, Greek and Russian languages." The software was originally 

designed to work with Biblical text, and it can be purchased by itself, or with one or more 

versions of the Bible included (at a higher cost). More information is available at 

– but it should be noted that HyperCard 

itself is no longer supported in the current generation of Macintosh operating systems 

(OS X). D. W Rand's Concorder Version 3–1 is available free of charge, although a 

printed manual is sold by Les Publications CRM of the Universite de Montreal, Canada. 

More information about Concorder can be found at 

. 

Several sets of tools developed for linguistics-oriented research support more 

sophisticated word-oriented research. See, for example, the GATE system developed at 

the University of Sheffield's Natural Language Processing Group at , 

or TIPSTER, developed with the support of various agencies in the USA 

(http://www.itl.nist.gov/iaui/894.02/related_projects/tipster/). As powerful as these 

environments generally are, they have been developed in a technical culture that is 

largely foreign to the humanities, and they require both extensive knowledge of 

linguistics and computing environments such as Unix to operate. They are not discussed 

further in this chapter. 

Qualitative analysis 

Software developed for qualitative analysis within the social sciences provides a very 

different view of how computers can assist with the analysis of texts. Here the focus is on 

software that allows the user to explicitly label and organize thematic categories that they 

discover while reading the text. Three widely used pieces of software that work in this 

way are Nud*ist, NVivo (both from QSR International: ) and 

Atlas.ti from Scientific Software Development, Berlin 

(). An interesting review of text analysis software that 

comes from this social sciences perspective is provided in the book A Review of Software 

for Text Analysis (Alexa and Zuell 1999). An article describing the use of these tools for 

research can be found in Kelle (1997). 

Text collation 

The use of computers to support the collation of textual variants is almost as old as the 

wordform work described earlier in this chapter. Although the early belief that collation 

was largely a mechanical task has been shown to be false, much useful work has been 

done in this area, nonetheless. Perhaps the best known tool available to support collation 

is Peter Robinson's Collate program for the Macintosh. You can read more about it at 

Getting Started with Collate 2, from 

.

Wilhelm Ott's TuStep system has components in it that are specifically designed to 

support collation and the broader task of the preparation of a critical edition. See the 

further discussion about TuStep later in this chapter. 

A Text Tool for XML: XSLT 

Up to now we have focused primarily on tools specifically developed to support certain 

kinds of research in the humanities. If your work is focused on these particular tasks, then 

you may find many of your needs met by these tools. However, for many of us, the work 

we do with a text is not so tightly focused, and these tools will rarely be sufficient to 

support all the work we wish to do. 

In this section we will describe a set of tools built on XSLT that are available to work 

with XML markup. The Text Encoding Initiative (TEI) has made it evident that many of 

the things in a text that are of interest to textual scholars can be usefully identified with 

SGML or XML markup. However, once these things are marked up this is rarely the end 

of the story – after all, one puts the markup in so that one can do further work with it 

later. These days one often wants to publish such materials on the WWW and, of course, 

the resulting website would contain the base text in HTML. Often, however, the markup 

can provide the source for various supporting index documents that provide a set of 

alternative entry points into the text itself. The automatic generation of such indices and 

of the original text in HTML is well supported by XSLT. 

XSLT stands for "Extensible Stylesheet Language: Transformations" (compare with 

XML: "Extensible Markup Language"). It is one of a pair of standards that are named 

XSL (Extensible Stylesheet Language). The second part of XSL, XSLFO – "Extensible 

Stylesheet Language: Formatting Objects" – provides a language describing how a text 

should be laid out for display – usually for the printed page. The XSLT language is 

developed by the World Wide Web Consortium (W3C: ), a group 

of individuals and corporations who have taken on the task of maintaining and 

developing standards for the World Wide Web. 

The emphasis in XSLT is on transformation, and today the principal use of XSLT is the 

transformation of an XML document into HTML for presentation on the WWW. The 

virtues of using a form of XML more complex and specifically adapted than HTML as 

the initial markup scheme for a text have been extensively described elsewhere in this 

book, and I will not repeat them here. However, if one is to present material on the 

WWW then (at least today) one needs to present the material in HTML, and an approach 

that consistently transforms any well-formed XML into HTML is very valuable. Often a 

single tag in XML needs to be transformed into a set, sometimes a complex set, of HTML 

tags. In XSLT one describes this transformation once, in what is called in XSLT a 

template. An XSLT processor can then apply the specification given in the template to all 

places in the original text where it fits. Normally the transformation process requires the 

specification of many different templates. Thus, one groups the set of templates one 

needs in a single document, called an XSLT stylesheet.

Here is a simple example that will give a little of the flavor of XSLT. Suppose the 

original text is marked up in TEI (the example, slightly modified to be in XML rather 

than SGML, is from section 6.3–2.2. "Emphatic Words and Phrases" of the TEI P3 

guidelines): For presentation in 

HTML, the q element needs to be transformed so that the contained text is surrounded by 

quote marks. The empb element, when the rend attribute is set to "italic", needs to be 

transformed into HTML's / element. The following two templates, which would, of 

course, be included with many others in a complete XSLT stylesheet, would accomplish 

this: XSLT is itself written in XML. In 

this excerpt we see two XSLT template elements. Each has a match attribute which 

specifies where the particular transformation contained therein should be applied. The 

match attribute for the first template element simply asserts that this template is 

applicable for all q elements in the input text. The match attribute in the second template 

asserts that it is applicable to all emph elements which have their rend attribute set to 

italic. The content of each template element describes what should be generated as output 

from the transformation process. In both template elements you can see the applytemplates 

element which, in effect, says "place the matched element's content here while 

also applying their templates as appropriate." In the first template, then, the content is 

simply surrounded by quote marks. In the second template, the content is surrounded by 

HTML's / element. If these transformation templates were applied to the text above, then, 

the resulting output would be: Although this 

example gives a little bit of the flavor of what XSLT can do, by itself it barely hints at the 

kinds of transformations that can be expressed in XSLT. With an XSLT stylesheet, for 

example, it is possible to specify a transformation that reorganizes and duplicates the 

content of some elements so that, say, each section heading in an input document appears 

in two places in the output – once as heading for its respective section, but also at the 

front of the document as an element in a table of contents. Material can also be sorted and 

grouped in different ways (although grouping is rather complex to express in the XSLT 

version 1). It is possible, for example, to write a transformation stylesheet that scans an 

XML document looking for references to people that have been tagged using TEI's 

"name" tag: (e.g., from TEI P3 guidelines section 6.4.1, slightly modified): 

XSLT can be used to select all material in name tags and generate an index from them. 

All the references to "de la Mare, Walter" and to the places "Charlton" and "Kent" could 

be grouped together under their respective headwords, and links could be generated

elow each heading to take the user from the index to the spots in the text where the 

references were found. 

Examples so far have emphasized the use of XSLT to generate output in HTML for 

making material available on the WWW. However, XSLT is designed to produce output 

in other XML formats as well as HTML. As XML becomes the scheme of choice for 

feeding data to many programs, it is likely that XSLT will be used more and more to 

transform one type of XML markup into another. For example, suppose you had a text 

with all the words tagged, and you wished to perform a statistical calculation based, say, 

on the number of occurrences of the wordforms in different chapters, one could write a 

(complex) stylesheet that would locate the words, count up the number of occurrences of 

each wordform in each chapter, and generate a document containing this information. If 

the statistical analysis program accepted data of this kind in XML then your stylesheet 

could write out its results in the XML format your statistical program wanted. 

Learning XSLT is difficult, particularly if you wish to take advantage of its more 

advanced features. Jeni Tennison's Book Beginning XSLT (Tennison 2002) provides a 

thorough introduction to many features of XSLT and is understandable to the nonprogrammer, 

although one does have to put up with examples drawn from the processing 

of television program listings! Michael Kay's XSLT: Programmer's Reference (Kay 2001) 

is, as the name suggests, a complete, in-depth review of XSLT and the software that 

supports it, and is aimed at a rather technical audience. A search on Google for "XSLT 

Tutorials" will find many online tutorials. A number of learning resources are also listed 

at . 

There are several XSLT transformation tools available, and all the ones described here 

are free. 

Microsoft's MSXML (XML Core Services) contains an XSLT processor for Windows and 

is incorporated inside the company's Internet Explorer (IE) browser. If you send IE an 

XML document with an XSLT stylesheet, it will transform the XML within the browser 

and present the display as if the resulting HTML document had been sent instead. 

Microsoft was much involved in the development of XSLT, and until Internet Explorer 6, 

the processor provided was not compatible with the now-standard version of XSLT. 

Hence, be sure that you are using the latest version. Information about MSXML, and a 

download package to set it up on your Windows machine can be found at 

. 

There are several XSLT processors available that run separately from a browser. To use 

one of them you would give your XML document and XSLT stylesheet to it, and it would 

save results of the transformation into a file on your hard disk. HTML files generated this 

way would then be made available using any web server, and by transforming the 

documents in HTML before serving them, you allow any web browser to view them. The 

two most widely used XSLT processors are SAXON and XALAN. Both these XSLT 

processors are most readily run on a Windows machine from the DOS prompt. You will

need to be familiar with the basics of a DOS window in order to be able to use these 

processors effectively. 

Michael Kay's SAXON processor () is considered to be 

very highly conformant with the XSLT standard. Saxon versions up to 6.5.2 have been 

packaged in an "Instant Saxon" format that runs readily on many Windows machines, and 

is relatively easy to set up and use. It is packaged in a ZIP file on the Saxon website, and 

setting it up involves only unzipping the two files it contains into an appropriate directory 

on your computer. Once I had unzipped the Saxon program from the ZIP file I was 

immediately able to use it with a Hamlet text and stylesheet by starting a DOS/ 

Command window and typing the command: 

(which asked SAXON to read an XML 

document in hamlet, xml, and an XSLT stylesheet in hamlet.xsl and to generate as output 

a file hamlet.htm). The ZIP file contains an HTML document that describes how to 

invoke the Saxon program in some detail. A version of SAXON is also available which 

operates with the support of Sun's Java system. If you are familiar with Java, you might 

find it as easy to use this version instead. 

XALAN, from the Apache Software Foundation, is another widely used XSLT processor. 

It comes in two versions. One version, implemented in Java, can be found at 

, and requires you to also set up Sun's Java 

system on your computer. The second version (at ) 

does not require Java, but seems to be a less complete implementation of 

XSLT. 

Macintosh OS X users who are able to set up Java on their machines in the background 

Unix environment that underpins OS X will be able to use XALAN or SAXON on their 

machines. 

Perl and TuStep: General Purpose Text Manipulation 

In the first section of this chapter we described tools designed to support particular kinds 

of text-oriented tasks. The section on XML introduced tools that are potentially much 

more general-purpose. In this section we come to the tools that could be applied the most 

broadly, and we will be describing two "toolkit environments" that at first may seem 

quite different: Perl and TuStep. By the end of the chapter we will have shown some of 

the ways in which they are also similar. 

Perl 

Perl was created by Larry Wall in 1987 to respond to a need to be able to perform 

complex administrative-oriented text processing and report generation of data on Unix 

systems. Perl was so successful that it soon became the tool of choice for getting all sorts 

of things done in the Unix environment, and it eventually spread from the Unix world to 

many other computing environments. It has become (in Andrew Johnson's words) "the 

tool to use for filling gaps everywhere." Perl is good at reading, processing, transforming

and writing plain text. If you have ASCII data that come out of one program in one 

format, and you want to feed them into another program that requires a slightly different 

format, then Perl might allow you to quickly develop the transformation you need in a 

matter of minutes. We will introduce Perl here, but other languages such as Python can 

do similar work. 

Perl is a programming language, and as such shares some of its characteristics with other 

programming languages – including a syntactical structure that is broadly similar to 

several important ones. Thus, those who are familiar with other widely used languages 

such as Java or C will start off already knowing something of how to write Perl 

programs. This means that for many computing professionals, Perl is "easy to learn." 

However, if you do not have a programming background, you will find that you will not 

be able to draw in the years of training and experience that allow Perl to be "easy to 

learn" for this community. Instead, initially you are likely to find learning to program in 

Perl slow-going. Make friends with a programmer! 

Filling of gaps – Perl's strength – often involves the creation of small pieces of 

throwaway software specifically tailored for a task at hand. When one knows how to 

write Perl, one can often throw a little program together to do something simple in a 

matter of minutes. The price for this informal nature of the language is sometimes paid in 

performance – the program might take longer to run than a carefully written program to 

do the same task in C would take – however, the C program to do the same job might 

take a day to write. 

To help introduce Perl, I will describe a bit of the Perl programming I did for Willard 

McCarty's Onomasticm of Ovid's Metamorphoses. In this project, McCarty has marked 

up some aspects of personification in the Metamorphoses by a tagging scheme of his own 

invention. Here is an example from his text: 

These seven lines represent 

one line from book 13 of the Metamorphoses. The first line, above, is the actual Latin 

text of Ovid's poem. The remaining six lines represent the tagging that McCarty has 

added. Most of the lines of poetry, like this one, have a number of tags attached to them. 

Each tag is surrounded by brace brackets "[" and "]", and the contents are divided into 

three parts. The first part (in the first tag "on") categorizes the kind of personification – 

here the individual is personified by being given a person-like attribute. Tags beginning 

with "v" represent personification through a verb. The second part (here "Achilles") 

identifies the person/god being talked about, and the third part (here "[arma] optima") 

links the tag to words in the text. Tags can be more complex, but this is sufficient for our 

purposes here.

One obvious way to transform this text and make the tagging very useful would be to turn 

it inside out – producing indices based around the tags, producing something that looks 

like this (here showing a fragment from the index of Attributes): 

You can see the reference to Achilles arma optima from line 13–40 in the second line of 

the Arma/Achilles entry. The indices were represented as a set of linked HTML pages, 

with references to the poem acting as links. One can use the indices to first get an 

overview of the kind of attributes that appear across the entire text, and can then use the 

index to review all the occurrences in the poem. 

Writing the Perl code to transform this tagged text into these indices was, of course, 

significantly more than a 10-minute job, and it is beyond the space we have here to begin 

to analyze the processing in any detail. However, a small example of Perl coding will 

give you the flavor of what working with Perl is like. 

An important component of Perl is its regular expression processor. Regular expressions 

provide a way to express relatively complex patterns to select and pull apart text for 

further processing. The expression: uses the regular expression operator 

sequence ".*?" and ".*" to specify "any sequence of characters." This pattern matches the 

form of all the tags in McCarty's text: a "{" followed by any sequence of characters, 

followed by a "/", followed by any sequence of characters, followed by a ":", followed by 

any sequence of characters and ending with a "}". 

If we put parentheses around the "any sequence of characters" in this expression, then 

Perl will make available for further processing the component of each specific text string 

that matched the pattern. Thus, the sequence: if applied against the 

sequence "[on/ Achilles: [arma] optima]" would have Perl identify "on" as the first part, 

"Achilles" as the second part, and "[arma] optima" as the third part, and make those 

subcomponents available to the rest of the program for further processing. Here is the 

same regular expression as it might be used in a bit of Perl code: 

The example has been slightly simplified from

what was actually used. Even then one can see the formal nature of the Perl language, and 

perhaps get a sense of the challenge it presents for non-programmers to learn. The first 

line says "fetch a new line of input from the input file and do the following chunk of Perl 

code (enclosed in '[]' s) for each line." The second line says "assign the line of input you 

read in the first line to $txt, and chop off any ending newline character that you find." 

The third line says "if the string of characters in $txt matches the given pattern (you can 

see the pattern we described above embedded in this line), do the lines enclosed below 

within '[]' s." The fourth line asks Perl to save the first bit of the matched regular 

expression in $code, the second bit in $name, and the third bit in $text. The code would 

continue with instructions that made Perl do something useful with the chunks of the tag 

in $code, $name, and $text. 

During my analysis of the task of transforming the tagged text into a set of linked indices, 

it became evident that the process to generate the separate indices was largely the same 

for each index, albeit using different data by selecting different tags from the tagged text. 

Thus, it became clear that the entire processing could be written as a small set of separate 

modular programs that could be directed to each appropriate tagset in turn. We chose, 

then, to apply Perl to the task of transforming the marked-up text into indices (expressed 

as collections of HTML pages) by applying a modular approach – breaking the 

transformation tasks into a number of smaller steps, writing Perl code for each one of the 

bits. Then, by combining them together in the correct order, the entire task would be 

performed. 

For example, a small program we called text2htm.pl transformed the text and tags into a 

set of HTML documents. A small module was also written (called tagproc.pl) that 

extracted data from the tags and stored them in a large table-like structure which would 

be used, and added to, by the other processing Perl programs that worked on the material. 

These two programs only needed to be run once. A set of three Perl scripts then did the 

bulk of the work of transforming the tabular data derived from the tags into a particular 

index presentation (divided into a number of separate HTML pages) you see above. Two 

further scripts were also used during the generation of each index to create two kinds of 

navigational aids for the index. Thus, after the first two programs were run, the other five 

could be used in sequence for each of the three indices that were to be generated. 

When the Perl scripts were ready, it was a simple matter to record the sequence in which 

they should run and what data they should process in a small DOS batch file. When this 

has been done the process of transforming the entire text with its thousands of tags into 

the integrated collection of several thousand HTML pages could then be performed by 

typing only one command. When the batch file was invoked, it was then a matter of 

simply waiting for the couple of minutes while the computer worked its way through the 

batch file and Perl scripts to generate the completed version. 

The writing of the Perl scripts to work over the material was not a trivial matter. 

However, because they could be packaged up and invoked in such a simple manner, they 

allowed McCarty to quickly generate a new version of the reference website for his 

materials as often as he liked.

Getting started with Perl 

Perl is free software. It was originally developed for Unix systems and is nowadays 

provided as a built-in tool in systems like Linux. The ActiveState company 

(http://www.activestate.com) has produced the most popular (free) version of Perl for 

Windows machines. Versions of Perl have been available on the Macintosh for several 

years (see, for example, MacPerl), although until OS X (which is Unix-based), they were 

generally incomplete implementations. 

Development of Perl is, these days, a worldwide phenomenon. There is not only 

continued work to enhance and further develop the basic Perl language, but there is a 

substantial number of developers who develop Perl modules that allow the Perl 

programmer to do tasks other than simply process text files. The DBI module, for 

example, provides a standard way to allow a Perl script to connect to and extract data 

from a relational database. There is a set of XML modules that allow XML files to be 

manipulated in Perl programs. Indeed, among the thousands of modules now available it 

is possible to find one that will help you apply Perl to almost any kind of computer data 

now available, including material available over the Internet. The CPAN website 

() is the usual place to go for information about all the modules 

that people have written. For Windows Perl users, the ActiveState company maintains a 

set of these modules specially packaged up for use on Windows, and allows you to 

download any of them and set them up for free, simply by invoking their "ppm" program 

and asking for them by name. 

Perl, like any full-function programming language, is difficult for non-programmers to 

learn, and most books and online tutorials that purport to provide an "easy" introduction 

to Perl are, in fact, written for the programmer audience. Andrew L. Johnson's book 

Elements of Programming with Perl (Johnson 2000), however, provides an introduction 

to Perl programming for non-programmers, and is recommended for this purpose. 

TuStep 

Willhelm Ott's TuStep (Tubingen System of Text Processing Programs) system has a 

long history. The software that developed into TuStep was begun in 1966, and the first 

version of TuStep that was made available under that name appeared in 1978. It is still in 

use today – extensively at Tubingen and in other centers in Germany, and in a number of 

other places in the world – for the manipulation of texts for all sorts of scholarly 

purposes. TuStep consists of a set of 45 commands that operate in an environment 

containing a text editor and a macro program. Ott describes the task domain for his 

system as "textdata processing", and describes what he means by this in the introduction 

to the English version of the TuStep manual: 

These basic operations of textdata processing (and the corresponding TUSTEP programs) 

encompass functions that can be generally characterized as follows: Collation of different 

text versions; correcting not only in the interactive Editor mode, but also with the use of 

prepared (or automatically generated) correction instructions; breakdown of text into

user-defined elements (z. B. semantic patterns); sorting text elements or lengthy units of 

text according to a number of different alphabets and other criteria; Generating an index 

by compiling sorted text-elements; Processing textdata according to user-defined 

selective criteria, replacing, moving, enhancing, concluding and comparing text, 

calculating numerical values contained in the text (e.g. calender dates) or those which can 

be determined from the text (e.g. the number of words in a sentence), and output in 

various formats, including those required by other operating systems (e.g. SPSS for 

statistical evaluation). 

(1993: 8) 

TuStep, then, consists of a large number of rather abstract processes that can be instructed 

to perform a large number of individual operations on a set of text files. The processes 

are designed to be combined together in a sequence to perform a large number of useful 

tasks. The sequence of operations can then be stored in a command file and subsequently 

reapplied at any time. One of TuStep's components is a high-quality typesetting program 

which is controlled by a command language that can be generated by other elements of 

the system, and which is capable of producing letterpress-quality printing – indeed a 

significant use of TuStep is in the preparation of printed critical editions. In the same way 

that a set of woodworking tools can, in the hands of a skilled craftsperson, produce 

beautiful results, TuStep's tools can be applied to text to produce beautiful printed 

editions. Of course, it is only after years of practice that the woodworker can produce a 

beautiful bookcase using the basic carpentry tools such as a saw, plane, chisel, etc. In the 

same way, one has to learn how to take the similarly abstract tools provided by TuStep 

and combine them to generate the best product that they are capable of producing. 

We mentioned earlier that TuStep modules are meant to be combined into a sequence of 

operations. To generate an index of McCarty's Onomasticm material described earlier 

using TuStep, one would first invoke its PINDEX program to extract the materials that 

are meant to make up the index – putting the result into an intermediate file. Then one 

would sort the resulting intermediate file and use the result of that to feed into another 

program called GINDEX to transform the properly ordered file into a hierarchical indexlike 

structure. Next one could take the hierarchically structured entries and write 

instructions that would transform them into a series of typesetting codes that could drive 

the typesetting program. 

TuStep modules often incorporate a large number of separate functions within a single 

program. Parameters are provided to tell the PINDEX program how to recognize material 

in the input text file that belongs in the index, and how to assign the pieces that are found 

into a multilevel index. Instructions to TuStep programs are given in a notational scheme 

that is especially developed for the program. For example, the following lines would be 

stored in a file and passed to the PINDEX program to tell it how to extract the tags from

McCarty's Onomasticon texts: The letters at the front of each line specify 

parameters that the PINDEX program is to use. The "ea" line specifies that an index entry 

from the text file begins after a ":." The "ee" line indiciates that the text for an index entry 

ends before either a "]" or a "_." The "ena" and "ene" lines identify characters that delimit 

supplementary text that belong with the collected index entry. The "enk" line specifies 

that the text after the index entry should be in bold. 

TuStep is a very versatile system, but learning how to use the software components to do 

the things you want is not easy. TuStep can be inexpensively purchased from the 

University of Tubingen. Most of the documentation for TuStep is in German. There is a 

version of the TuStep reference manual in English, but it cannot really be used to learn 

the program in the first place. There is a tutorial on TuStep, in German, called Lernbucb 

TUSTEP, and there are training courses each March and September in Tubingen that will 

help you get started. There is a brief introduction to TuStep, its concepts, and its facilities 

in Ott (2000). There is also further information on the TuStep website at 

. 

TuStep and Perl compared 

Both TuStep and Perl can be used to do almost any kind of manipulation of a text in 

electronic form. Perl is a general-purpose programming language and can also perform 

any kind of transformation that you can ask a computer to do on a text file, although the 

task of learning how to use it is large – especially for non-programmers. TuStep is 

especially developed for performing tasks of interest to scholars working with texts, 

although it will still take real work to learn how to use it. TuStep also comes with a highquality 

typesetting program which is capable of professional-quality typesetting – 

something Perl does not have (although, of course, Perl could be used to generate texts to 

feed into other typesetting systems such as TeX). For non-programmers, then, both 

TuStep and Perl have steep and long learning curves. Since TuStep is specifically 

designed for scholarly work, it does contain some helpful features – such as strategies 

that are built in for the proper sorting of text in many languages – that make specifying 

processing easier. However, on the other hand, there is a great deal of material available 

about how to program in Perl, and it is possible to hire a programmer who already knows 

Perl to do work for you. 

Conclusion 

The reader will, by this point, have hopefully seen that the range of text tools described 

here can support a very wide range of scholarly activities on texts, and in the limited 

space available here it is possible to only hint at the full potential of these tools. Susan 

Hockey's book Electronic Texts in the Humanities (Hockey 2000), while not explicitly

about text tools, does describe many different projects and suggests the range of things 

that a set of tools like the ones described here can accomplish. 

Certainly, XSLT, Perl, and TuStep are complex and powerful systems. Certain kinds of 

useful results can be achieved with a modest amount of work, but learning to use any of 

them both efficiently and to their full potential will commit the learner to a process that 

could take years to complete. Often reviews of software for the humanities talk about 

"user friendliness." Are these tools "user-friendly?" If you mean, "are they immediately 

usable", then the answer is "no." However, are a good set of carpentry tools userfriendly? 

They are if you are skilled in their use. In a similar way, these tools can do 

significant work on texts, but all of them require a significant amount of skill to make use 

of them. 

Bibliography 

ActivePerl (2002). ActiveState Corp. At 

http://www.activestate.com/Products/ActivePerl/. 

Alexa, Melina and Cornelia Zuell (1999). A Review of Software for Text Analysis. 

Mannheim: ZUMA. 

Barlow, Michael (1995). A Guide to MonoConc. At 

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34. 

"So the Colors Cover the Wires": Interface, Aesthetics, 

and Usability 

Matthew G. Kirschenbaum 

Introduction: A Glass Darkly 

The idea of interface, and related concepts such as design and usability, are some of the 

most vexed in contemporary computing. Definitions of interface typically invoke the 

image of a "surface" or a "boundary" where two or more "systems", "devices", or 

"entities" come into "contact" or "interact." Though these terms encourage spatial 

interpretation, most interfaces also embody temporal, haptic, and cognitive elements. The 

steering wheel of a car, the control panel of a VCR, and the handle of a door are all 

examples of everyday interfaces that exhibit these dimensions. In the context of

computers and computing, the word "interface" is often used interchangeably with 

"graphical user interface", or GUI, most frequently encountered as a desktop windows 

environment. The command line prompt is perhaps the best-known alternative to the 

GUI, but there are a plethora of others, including screen readers, motion trackers, tangible 

user interfaces (TUIs, breathtakingly rendered in the 2002 film Minority Report), and 

immersive or augmented computing environments. In the humanities, meanwhile, it is 

increasingly common to encounter the idea that a book or a page is a kind of interface, a 

response to the understanding that the conventions of manuscript and print culture are no 

less technologically determined than those of the digital world. At least one observer, 

Steven Johnson, has defined our present historical moment as an "interface culture", a 

term he wields to embrace not only the ubiquity of computers and electronic devices but 

also the way in which interface has come to function as a kind of trope or cultural 

organizing principle – what the industrial novel was to the nineteenth century or 

television to the suburban American 1950s are his examples. 

As much as it is talked about, however, interface can at times seem little loved. Usability 

guru Donald A. Norman writes: "The real problem with interface is that it is an interface. 

Interfaces get in the way. I don't want to focus my energies on interface. I want to focus 

on the job" (2002: 210). Nicholas Negroponte holds that the "secret" of interface design 

is to "make it go away" (1995: 93). To further complicate matters, interface is often, in 

practice, a highly recursive phenomenon. Take the experience of a user sitting at her 

computer and browsing the Web, perhaps accessing content at a digital humanities site. 

The site's internal design imposes one layer of interface between the user and the content, 

and the web browser – its buttons and menus and frames – immediately imposes another. 

The user's desktop environment and operating system then impose a third layer of 

interface. The ergonomics of the situation (we'll assume our user is working with a 

keyboard and mouse, looking at a screen positioned the recommended 18 inches away) 

create still another layer of interface, a layer which becomes apparent when one considers 

alternatives such as accessing the same content with a PDA or a wearable device, or in a 

room-based virtual reality setting like a CAVE. Importantly, each of these "layers", as I 

have been calling them, exhibits the potential for interaction with one another as well as 

with the user. The desktop environment governs the behavior of the browser software, 

whose features and functions in turn directly affect many aspects of the user's interaction 

with the site's internal design and content. 

While everything I have just been rehearsing is familiar enough in computer science 

circles, particularly the domain known as human-computer interaction (HCI, also 

sometimes identified as human-computer interface), aspects of this narrative may seem 

problematic to readers trained in humanities disciplines. It would not be hard to find 

someone willing to argue that my entire scenario is the product of yet another 

unacknowledged interface, a kind of common cultural gateway whose socially 

constructed ideologies govern our expectations with regard to technology, representation, 

and access to information. Moreover, in the scenario sketched above, my distinction 

between different layers of interface and something I casually called "content" is one that 

runs counter to decades of work in literary and cultural criticism, where form and content 

are almost instinctively understood as inextricable from one another. Thus, the weight of

established wisdom in a field like interface design rests on a fundamental disconnect with 

the prevailing intellectual assumptions of most humanists – that an "interface", whether 

the windows and icons of a website or the placement of a poem on a page, can somehow 

be ontologically decoupled from whatever "content" it happens to embody. This is 

precisely the point at which Brenda Laurel begins her dissent in Computers as Theatre, 

her influential critique of mainstream interface theory: 

Usually we think about interactive computing in terms of two things: an application and 

an interface. In the reigning view, these two things are conceptually distinct: An 

application provides specific functionality for specific goals, and an interface represents 

that functionality to people. The interface is the thing that we communicate with – the 

thing we "talk" to – the thing that mediates between us and the inner workings of the 

machine. The interface is typically designed last, after the application is thoroughly 

conceived and perhaps even implemented; it is attached to a preexisting bundle of 

"functionality" to serve as its contact surface. 

(Laurel 1991: xv) 

Laurel is writing to challenge this prevailing viewpoint. Yet the truth is that, from a 

developer's perspective, the interface is often not only conceptually distinct, but also 

computationally distinct. As John M. Carroll points out, it wasn't until the comparatively 

recent advent of languages like Visual Basic that it even became practical to program 

both a user interface and an application's underlying functionality with the same code 

(Carroll 2002: xxxii). Like the history of hand-press printing, which teaches us that 

paper-making, typesetting, etching or engraving, and bookbinding came to encompass 

very different domains of labor and technical expertise (rarely housed under the same 

roof), resource development in the digital world is typically highly segmented and 

compartmentalized. 

Interfaces are mostly discussed in mundane and utilitarian terms, but computing lore 

contains its share of instances where poor interface design has had lethal and catastrophic 

results. One notorious episode involved the Therac-25, a machine employed for cancer 

radiation therapy in the mid-1980s, whose cumbersome software interface contributed to 

several patient deaths from overexposure. Likewise, the small-plane accident that killed 

singer-songwriter John Denver has been attributed to a poorly designed cockpit interface, 

specifically the placement of a fuel switch. While the stakes in digital humanities 

research are (happily) other than life and death, interface is nonetheless an indispensable 

component of any project. 

Indeed, interface presents a number of interesting and unique problems for the digital 

humanist. Understandably driven by pragmatic and utilitarian needs, the interface is also 

where representation and its attendant ideologies are most conspicuous to our critical 

eyes. Ostensibly wedded to the ideal of user-friendliness, the interface is also where we 

deploy our most creative features and imaginative flourishes. Too often put together as 

the final phase of a project under a tight deadline and an even tighter budget, the interface 

becomes the first and in most respects the exclusive experience of the project for its end

users. Seemingly the most creative or intuitive stage of the development process, the 

interface is also potentially the most empirical, subject to the rigorous quantitative 

usability testing pioneered by HCI. This chapter makes no attempt to offer a 

comprehensive survey of the vast professional literature on interface and usability, nor 

does it seek to serve as a design primer or guide to best practices. (Readers interested in 

those topics should consult the chapter's suggestions for further reading.) Instead, in the 

pages that follow I seek to develop some broader frameworks for thinking about the 

major challenges that interface poses for both theorists and developers in the digital 

humanities. 

Ways of Seeing 

Computers compute, of course, but computers today, from most users' points of view, are 

not so much engines of computation as venues for representation. The first use of CRT 

displays as output devices in the 1950s irretrievably situated computers within a complex 

cultural genealogy of screens, a genealogy which also includes video, television, cinema, 

photography, and indeed, as Lev Manovich and others have argued, the full lineage of 

visual aesthetics in the West since the advent of perspective. This context is important 

because it allows interface design to take its place alongside the other representational 

forms that have inhabited our many varieties of screens, frames, and windows. Moreover, 

although a graphical user interface built around the familiar desktop and windows 

metaphor currently constitutes the normative interface experience for the vast majority of 

users, it is worth remembering that this particular graphical environment, and the 

representational practices it encourages, is historically quite specific. 

From one perspective, our interface and display technologies have remained remarkably 

stable and consistent over the past thirty years. Though the Alto, built at Xerox PARC in 

1973, is widely regarded as the first computer to implement a functional graphical user 

interface, the more important date is probably 1968, when Stanford's Douglas Engelbart 

demonstrated an operational GUI to a standing-room-only audience in San Francisco. 

Steven Johnson articulates the importance of Engelbart's presentation, which included a 

feature recognizable as "windows", this way: 

[H]istorians a hundred years from now will probably accord it the same weight and 

significance we now bestow on Ben Franklin's kite-flying experiments or Alexander 

Graham Bell's accidental phone conversation with Watson. Engelbart's thirty-minute 

demo was our first public glimpse of information-space, and we are still living in its 

shadow. 

(Johnson 1997: 11) 

By "information-space", Johnson means the abrupt transformation of the screen from a 

simple and subordinate output device to a bounded representational system possessed of 

its own ontological integrity and legitimacy, a transformation that depends partly on the 

heightened visual acuity a graphical interface demands, but ultimately on the combined 

concepts of interactivity and direct manipulation. Engelbart's demo included the first

public use of a mouse, and the sight of its pointer sweeping across the screen instantly 

collapsed the stark input/output rhythms of batch-process and command-line computing 

into a single, continuous sweep of user activity. Just as important, however, was the 

spectacle of Engelbart dividing his screen into distinct regions, heterogeneous in content 

but spatially adjoining, a feat demonstrated most dramatically by a window with a live 

audio and video feed to a colleague in Menlo Park, California. The computer – or more 

specifically, the screen – had clearly become a much more complex representational 

space, an information space whose surface owed as much to modernist collage as it did to 

brute force calculation. A crucial refinement came several years later when a team at 

Xerox PARC, led by Alan Kay (and including many of Engelbart's former Stanford 

colleagues), arrived at the realization that windows could actually overlap, thereby 

immediately imbuing the screen with the third dimension we take for granted today. As 

Johnson suggests, "The whole idea of imagining a computer as an environment, a virtual 

world, comes out of this seemingly modest innovation" (1997: 47). 

While Engelbart's 1968 demo is the most venerable touchstone for contemporary humancomputer 

interaction, there are other origin stories that bear repeating. Ivan Sutherland, 

for example, working on a PhD thesis at MIT in 1963, introduced Sketchpad, a system 

that allowed users to draw lines on a screen in real time with what we would today 

recognize as a light pen. Sketchpad is significant because it reminds us that the current 

hegemony of mouse and keyboard was not always in place, and indeed, there are 

indications that alternative input devices like light pens – which fundamentally alter the 

nature of one's bodily interaction with a computer – may once again displace the mouse 

(see this chapter's "Coda" section, below). Sketchpad is also significant in another 

context, the history of computer graphics (without which there would be no graphical 

user interfaces). Nicholas Negroponte comments: 

The achievement was of such magnitude and breadth that it took some of us a decade to 

understand and appreciate all of its contributions. Sketchpad introduced many new 

concepts: dynamic graphics, visual simulation, constraint reduction, pen tracking, and a 

virtually infinite coordinate system, just to name a few. Sketchpad was the big bang of 

computer graphics. 

(1995: 103) 

Sketchpad was a vector system, meaning that the lines and shapes drawn by the user were 

mathematical formulas (vectors) that could be reproduced at will on-screen. Vector 

images provide another important context for understanding the significance of 

Englebart's work because the latter led directly to Xerox PARC's refinement of 

bitmapping as an alternative to Sketchpad and the era's prevailing vector displays. A 

"bitmap", as many readers will know, is a grid or matrix of pixels ("picture elements"), 

which, not unlike a Seurat painting or a photographic halftone, yield a coherent visual 

image through the optical interpretation of the aggregate composition. Bitmapped 

displays are what permit the gorgeous, high-quality facsimile images that we see in many 

of today's digital humanities projects, but their significance is much greater. (Note that 

bitmap displays, which are also known as raster displays, can refer to individual image

files in formats such as JPEG, TIFF, or GIF, as well as to an entire screen display; there 

is also a proprietary image format known as "Bitmap", or BMP, which is not to be 

confused with bitmapping as a general concept.) If vector images were the graphical 

inheritance of the computer's mathematical roots, bitmapped images were the visual 

realization of Turing's ideal of the universal machine: bitmaps enabled the computer 

screen to function as a representational surface capable of emulating other 

representational surfaces. Through bitmapping, the computer screen was transformed into 

a second-order or "meta" representational venue. This transformation quickly gave rise to 

intensive research on photorealistic rendering techniques in computer graphics as well as 

(eventually) the advent of hardware devices like scanners and digital cameras – which 

enable the computer screen to operate in the photographic tradition. (JPEG compression 

algorithms, it is worth noting, were introduced precisely to provide an image format that 

lent itself to reproducing photographic images.) 

William M. Ivins, in Prints and Visual Communication (1953), his landmark survey of 

print-making technologies in the West, argues eloquently for the importance of 

photography and what he terms "exactly repeatable visual statements" in enabling the 

dissemination of scientific knowledge. Bitmapping, I would argue, endows the computer 

screen with much those same qualities and capabilities, and although Manovich is right to 

point to the origins of computer graphics in the vector images of Cold War radar displays, 

the visual authority of computers as we know it today clearly owes more to the advent of 

bitmapping. Taken together, however, Sutherland and Englelbart laid the foundations for 

contemporary computer graphics and today's graphical user interface through their 

competing paradigms of vector and bitmap displays; competing not in a strict commercial 

sense, but in offering alternative visions of the computer as a representational medium 

and as an information space. 

Unlike bitmap or raster graphics, vector graphics are not well suited to representing 

continuous tone (especially photographic) images. Consequently, they may seem of little 

use in the digital humanities, where much of our work consists in providing high-quality 

facsimile renderings of documents, artwork, and other artifacts of cultural heritage. 

However, because vector graphics exist as a kind of mathematical abstraction, with no 

one-to-one mapping to an external referent, they are scalable and modular in ways that 

raster graphics are not. Today the most popular vehicle for vector graphics is the 

animation tool Flash, which, characterized by its colorful, dynamic displays, is rapidly 

colonizing large segments of the Web; indeed, there are those who believe that at the 

level of interface design the Web itself will eventually be made over as an animated 

Flash-based environment, with static HTML (more likely, XML) documents existing as 

subsidiary, special-purpose content. Interestingly for our purposes, Flash is also capable 

of supporting embedded bitmapped images, suggesting that the representational field I 

described earlier has receded by one full order of magnitude and that vector graphics are 

now the true heir to Turing's universalism. (On the other hand, it remains true that all 

general-purpose screen displays, whether LCD or CRT, are rendered as bitmaps.) 

To build a Flash animation, the designer creates a so-called "movie" consisting of a series 

of timed, sequenced, or triggered events. For some this may suggest that the Web is

evolving into a medium that owes more to television than to the now-familiar (and 

comfortably humanistic) conceit of the "page." I would cast the argument differently. If, 

as Jerome McGann has repeatedly said, computers lend themselves to representing books 

because they exist on a different material plane, then vector images, whose mathematical 

construction allows them to operate in registers unavailable to raster images, may offer 

the opportunity for a similar re-conception of our current information spaces. Whereas 

Alan Kay's overlapping windows added a third dimension to our interfaces, event-based 

vector graphics may, I believe, give rise to more playful and pliable information places: 

interfaces that occupy a material middle ground between the bitmapped data objects they 

enfold and the bitmapped surface of the screen. 

If all of that sounds hopelessly speculative and abstract, there are nonetheless some 

tantalizing glimpses to be had from the digital art world. Tomoko Takahashi's 

WordPerbect [sic], for instance, a Flash-based parody of a typical word processing 

interface: users encounter what appears to be a hand-drawn cartoon GUI, roughly limned 

in black ink on a white background. Typing generates crude, seemingly handwritten, lessthan-perfect 

characters on the screen. Takahashi's word processor is fully functional, but 

the interface yields an inversion of the typical user-friendly experience, one that serves to 

underscore the distinctive materialities of both print and electronic textuality. Clicking 

the Mail icon produces the following set of instructions, which appear as a scrap of 

notepaper "taped" to the screen: "print the document, put into an envelope or ssomething 

similair [sic] which can contain the document. Go to post office and weigh it and buy 

stamps…" and so on, for another hundred words, including further typos and blemishes. 

Jason Nelson, another Flash artist, in a piece entitled the last machine with moving parts, 

deploys the color picker interface familiar to users of image processing and paint 

programs to control the sequence of events in an animated word poem. As the reader 

moves the picker over the color palette, arrangements of words are pulled in and out of 

the visual field. "So the colors / cover the wires", reads this text at one point – and so the 

brilliant disguises of Nelson and Takahashi's localized parodies of interface culture cover 

(and discover) the hardwired histories of our information spaces. 

The Artist of the Beautiful 

Mary Shelley's 1818 novel Frankenstein is often invoked in discussions of computing for 

its meditation on the dualistic nature of science and technology, and has long been read as 

a parable of the promise and the peril of the Promethean flame. Yet Frankenstein is also, 

importantly, a novel of aesthetics. The anonymous creature at the center of the text 

("Frankenstein's monster") is described repeatedly as a "wretch", "a thing such as even 

Dante could not have conceived" (Shelley 1992: 57). In fact, the creature's visage is so 

hideous that apart from Victor (his creator), the only person who can stand to conduct an 

extended conversation with him is a blind man. 

It might be tempting to adopt Frankenstein for my own ends and make Shelley's creature 

into a figure for the graphical user interfaces of the current day and age; wretched and 

hideous are qualifiers we can debate, but there is no doubt that most of our desktop views 

are oddly unlovely, dull and listless information spaces that, as has been pointed out

many times, hew to the conspicuously corporate metaphor of the office. But I would like 

to turn instead to another, less well-known text in the Frankenstein tradition: Nathaniel 

Hawthorne's (1844) short story, "The Artist of the Beautiful." It is the tale of one Owen 

Warland, a watchmaker with an exquisitely refined aesthetic sensibility who, bored with 

crafting mechanisms in the service of "old blind Father Time", eventually succeeds in his 

experiments to "spiritualize machinery", imbuing a tiny, mechanical butterfly with the 

living breath of his artist's imagination: 

He produced, as he spoke, what seemed a jewel box…. This case of ebony the artist 

opened, and bade Annie place her fingers on its edge. She did so, but almost screamed as 

a butterfly fluttered forth, and, alighting on her finger's tip, sat waving the ample 

magnificence of its purple and gold-speckled wings, as if in prelude to a flight. It is 

impossible to express by words the glory, the splendor, the delicate gorgeousness which 

were softened into the beauty of this object. Nature's ideal butterfly was here realized in 

all its perfection; not in the pattern of such faded insects as flit among earthly flowers, but 

of those which hover across the meads of paradise for child-angels and the spirits of 

departed infants to disport themselves with. The rich down was visible upon its wings; 

the lustre of its eyes seemed instinct with spirit. The firelight glimmered around this 

wonder – the candles gleamed upon it; but it glistened apparently by its own radiance, 

and illuminated the finger and outstretched hand on which it rested with a white gleam 

like that of precious stones. 

(Hawthorne 1948: 235–6) 

Unlike Shelley's Frankenstein, however, there is some ambiguity as to the true nature of 

Warland's creation – whether it is indeed a living creature or a delicate clockwork 

automaton. "But is it alive?" the onlookers in the story ask repeatedly. Warland refuses to 

answer definitively: "'Wherefore ask who created it, so it be beautiful?' … 'it may well be 

said to possess life, for it has absorbed my own being into itself; and in the secret of that 

butterfly, and in its beauty, – which is not merely outward, but deep as its whole system, 

– is represented the intellect, the imagination, the sensibility, the soul of an Artist of the 

Beautiful!'" (1948: 237). 

As in Frankenstein, "The Artist of the Beautiful" is structured by the classic binaries of 

nature and culture, animate spirit and technological artifice; but it is also a story about 

form versus function, art for art's sake colliding with honest industry and labor. Warland's 

chief rival and antagonist is Robert Danforth, the village blacksmith, whose response to 

the butterfly is to exclaim, "There is more real use in one downright blow of my sledge 

hammer than in the whole five years' labor that our friend Owen has wasted on this 

butterfly" (1948: 238). These are the same binaries that have structured debate in 

interface and design since Apple introduced the Macintosh; it is not hard to hear the echo 

of Danforth's sledgehammer in the firm finger striking the Return key at the end of the 

command line, or to see Warland's labored handicraft reflected in the incessant mouse 

manipulations of the GUI. What role, then, should aesthetics have in interface design? 

How do we balance the competing demands of truth and beauty? For while most software 

and websites have pragmatic or functional ends, an interface such as Apple's OS X – with

its zooming windows, gossamer transparencies, and luscious drop shadows – encourages 

us to cultivate an aesthetic sensibility even in the most mundane corners of the desktop. 

Yale computer scientist David Gelernter has written at length on the subject of aesthetics 

in software design: 

Most computer technologists don't like to discuss it, but the importance of beauty is a 

consistent (if sometimes inconspicuous) thread in the software literature. Beauty is more 

important in computing than anywhere else in technology. … Beauty is important in 

engineering terms because software is so complicated…. Beauty is our most reliable 

guide to achieving software's ultimate goal: to break free of the computer, to break free 

conceptually…. But as we throw off the limits, what guides us? How do we know where 

to head? Beauty is the best guide we have. 

(Gelernter 1998: 22–3) 

Gelernter, who often comes across as an unreconstructed Platonist in his writing, goes on 

to speak of "deep beauty", his term for an idealized integration of form and function that 

bears a striking resemblance to Owen Warland's statement (above) that the beauty he 

apprehends is "not merely outward, but deep as its whole system." Gelernter also quotes 

approvingly Ted Nelson's dictum that "the integration of software cannot be achieved by 

committee…. It must be controlled by dictatorial artists" (1998: 22). Historically, 

however, human-computer interaction has its origins not in the poet's eye in a fine frenzy 

rolling, but rather in the quantitative usability testing of the sort pioneered by Ben 

Shneiderman and his colleagues in the 1970s. (Shneiderman himself, interestingly, has 

just recently put forward the Renaissance artist/technologist Leonardo da Vinci as the 

inspirational muse for his vision of a user-oriented "new computing.") I propose to begin 

addressing the aesthetics of interface by narrowing the field to look closely at two 

projects, both from the realm of text analysis, and each of which, it seems to me, exhibits 

a "beautiful" user interface. 

The first is a prototype tool called Eye-ConTact. Conceived and implemented by 

Geoffrey Rockwell and John Bradley in explicit relation to its predecessor TACT, Eye- 

ConTact is perhaps not beautiful in the superficial sense of being (especially) pleasing to 

look at; nonetheless, I believe the program well instantiates Gelernter's "deep beauty" in 

its stated approach to textual analysis. Rockwell describes its operations this way: 

Eye-Contact deals with the problem of recording the logic of an exploration by 

encouraging the user to lay out the fundamental steps in a visual environment. The user 

creates the logic by dragging out icons and "connecting the dots." This has the advantage 

of acting as both a record of the flow of choices made and a synoptic description of that 

flow, which should make the research easier to grasp. 

(Rockwell, website)

Note then that Eye-ConTact requires the user to make explicit procedural choices which 

are then recorded and represented in an evolving graphical construction of each specific 

analytical operation. The software, in other words, serves to model a series of ongoing 

hermeneutical events (by contrast, TACT lacks the means to log a record of a user's 

operations with a text). Eye-ConTact is also a realization of what Bruce Tognazzini, who 

has had a distinguished career at Apple, celebrates as a visible (as opposed to a merely 

graphical) interface: "A visible interface is a complete environment in which users can 

work comfortably, always aware of where they are, where they are going, and what 

objects are available to them along the way" (Tognazzini 1992: xiii). Still another way of 

thinking about Eye-ConTact is as an example of what happens when an application 

adopts as central that which is peripheral in most other tools, in this case the History 

feature – an approach that offers the basis for some interesting experiments in software 

design (see also in this regard Matthew Fuller's essay on his Web Stalker browser). Eye- 

ConTact clearly illustrates the way in which a particular tool, rather than seeking to hide 

its user interface, can instead use that interface as an active – indeed essential – 

component of the intellectual activity it aims to support: "not merely outward, but deep as 

its whole system." 

My second example is even more contemporary. In April 2002, information designer and 

digital artist W. Bradford Paley debuted a web-based tool called TextArc. Drawing upon 

publicly available electronic texts in the online collections of Project Gutenberg, TextArc 

produces intricate visualizations of novels and other literary works. Every word of the 

original text is rendered on-screen, both in a one-pixel font that reprints the entire work 

line by line clockwise around the perimeter of the display, and then again in a larger font, 

a cloud of words with those appearing most frequently clustered brightly in the center. 

Paley describes the result as "[s]ome funny combination of an index, concordance, and 

summary." Clicking on a word highlights all of its appearances within the visualized text 

as well as generating rays or spokes connecting them so that a user can study whatever 

patterns may emerge. The visualization is also dynamically linked to a clear reading text 

of the work and a keyword-in-context concordance, and together these tools offer a 

powerful package for textual investigation. What is most striking about TextArc, 

however, is not its analytical engine, but rather its gorgeous, luminescent fields of display 

that seem to subordinate traditional hermeneutics to more stochastic modes of knowledge 

representation. The visualizations have a marked aesthetic dimension, asserting their 

integrity on a purely visual register independent of any functional use to which they 

might be put. Paley understood this from the start, and indeed supports TextArc (which is 

free) in part by selling hard copies of its output, each offset-printed and ready for framing 

on high-quality paper. 

TextArc has received a great deal of positive press, including mainstream coverage in the 

New York Times; yet the basic functionality it provides – word frequency counts, 

distribution patterns, and keyword-in-context displays – has long been available with 

other tools. The platform-independent Oxford Concordance Program, which was capable 

of generating word lists, indices, and concordances for text analysis, first appeared in 

1981, followed by desktop software such as WordCruncher (1985) and TACT (1988). 

Yet all of these programs, as well as more recent packages like WordSmith Tools, deploy

stark, ascetic interfaces very different from the illuminated constellations of a TextArc 

visualization. 

Undoubtedly that contrast has much to do with the tools and development environments 

available to the authors of those earlier packages, as well as limited time and limited 

resources. These factors cannot be overstated: in one software project with which I was 

involved we spent eleven months of a one-year development period on the underlying 

architecture – which performed flawlessly – but mere days on the final user interface (all 

but unusable). Then the money ran out. Such problems are endemic to any institutional 

setting. But the digital humanities have also not yet begun (or else only just begun – see 

chapter 29, this volume) to initiate a serious conversation about its relationship to visual 

design, aesthetics, and, yes, even beauty. And just as Owen Warland's gilded creation fed 

the skepticism of Robert Danforth and the other artisans of his village, so too do 

conspicuous graphical displays often engender mistrust in contemporary academic 

settings – as though, like the traditional library carrel, our electronic surroundings have to 

be sensually impoverished in order to be intellectually viable. Visually suggestive 

interfaces are often derided as "slick", or "eye candy", or gratuitously "cool", or else – in 

an interesting bit of synaesthesia – too cluttered with "bells and whistles." 

To understand what is at issue here we might return to Donald A. Norman's contention, 

quoted in the opening section: "The real problem with interface is that it is an interface. 

Interfaces get in the way. I don't want to focus my energies on interface. I want to focus 

on the job" (2002: 210). Interestingly, despite this fierce disavowal of interface, in their 

original typographic presentation Norman's words are printed in boldface, for emphasis. 

Pausing for a moment, we can enumerate many typographical conventions for cueing a 

reader: not just the weight, but also the size and face of the type, its justification, margins, 

and so forth. Not to mention more diffuse bibliographical features including paper, 

binding, illustrations, and even the size, shape, and heft of the codex. As scholars such as 

Johanna Drucker and Jerome McGann (among others) have long argued, these extralinguistic 

elements cannot be written off as merely expendable or extraneous to "the text 

itself." They are, indeed, the working vocabulary of a particular graphical user interface 

that has become transparent to us only through long familiarity. All of us know how to 

read a modern newspaper or magazine in terms of its visual and typographic layout as 

well as its journalistic content. The debate over transparency in interface design that 

Donald Norman and Brenda Laurel (and more recently Matthew Fuller, Jay David Bolter 

and Diane Gromala) have all participated in thus mirrors the debate in the literary and 

textual studies community over the nature of a book's "contact surface", those physical 

and material features widely seen as incidental to the production of textual meaning. In 

both communities, the ideal of transparency is now being called into question and 

replaced with a broader awareness of how the visual (and aural, or tactile and olfactory) 

elements on page or screen function as integral aspects of the information experience, 

rather than as afterthoughts to some "pre-existing bundle of functionality." 

Some may object that the language I have been using ("integral aspects of the 

information experience", "deep beauty", "deep as the whole system"), in addition to 

fostering a curious kind of New Critical organicism – for Cleanth Brooks, a poem was,

famously, a well-wrought urn – is also militated against by actual information design 

practices, which frequently make a virtue of the deliberate and explicit segregation of 

form and function. We see this in markup languages such as SGML and XML, when data 

tagged with a descriptive schema are then rendered with external stylesheets. Or if not 

stylesheets then so-called "skins", which allow different visual themes to be swapped in 

and out of a web page or desktop application. With the appropriate skins a generic MP3 

application can be dressed to look like a jukebox or a crystal radio or in a thousand other 

guises. Likewise, a site called the CSS Zen Garden offers a striking demonstration of the 

power of stylesheets to represent the "same" underlying content in a dazzling array of 

different configurations. Moreover, as noted earlier, interface is recursive: thus we can 

use stylesheets to skin our data, skins to thematize our browsers and applications, and 

desktop themes to stylize our information spaces. Do we not then see interface and 

application cleaving apart in precisely the ways Laurel and Gelernter speak against? 

The truth is that the variability introduced by such effects is literally and deliberately only 

skin-deep. Standards compliance and validated code are essential. At the Zen Garden site, 

valid XHTML and CSS "cleave" in the other sense of that Janus-word: they work 

together as an integrated whole fused by the centripetal force of an open, communitybased 

standards environment. Littering this "road to enlightenment", notes the site's 

designer, are the "dark and dreary" relics of the past: "browser-specific tags, incompatible 

[Document Object Models], and broken CSS support." The visual effects that play across 

the shimmering surface of a site such as the Zen Garden are now practical precisely 

because the Web design community has come to an understanding (as expressed by its 

standards and tools) that clearly regards a well-wrought information space as a deeply 

integrated system, not just a series of on-screen effects. Compare this to the state of 

affairs captured in David Siegel's old essay "Severe Tire Damage on the Information 

Superhighway", advertised as "an open letter to Netscape Communications and the 

WWW community", which details the baroque array of spoofs, tricks, workarounds, 

fixes, and kludges that characterized professional Web design circa 1996. 

If there is a lesson here for the digital humanities it is simply this: just as interface cannot 

– finally – be decoupled from functionality, neither can aesthetics be decoupled from 

interface. Nor does talk of beauty always have to resort to mystification. I ultimately 

prefer the poet and critic Lisa Samuels to either Gelernter's neo-Platonism or Hawthorne's 

transcendentalism: "[W]e think that those parts of beauty which resist the translation back 

to knowledge are uselessly private and uncommunicative. In fact, they are what beauty 

'knows': that knowledge is also – perhaps most importantly – what we do not yet know…. 

Beauty is therefore endlessly talk-inspiring, predictive rather than descriptive, dynamic 

rather than settled, infinitely serious and useful." The important new wave of software 

studies (led by Manovich, Fuller, Bolter and Gromala, and others), which cultivates 

granular, material readings of the inevitable cultural and ideological biases encoded by 

particular applications and interfaces, offers one way to offset the potential risks of 

unvarnished aestheticism, and this literature should be watched – many of its contributors 

are also practicing digital artists. In the meantime, in the digital humanities, where we 

deal with the rich and varied legacy of cultural heritage, we ought to think about what it

means to be artists of the beautiful ourselves. As we will see in the next section, however, 

being an artist isn't always easy. 

The Blake Archive for Humans 

"The Blake Archive for Humans" is the title of a page that collects William Blake 

resources on the Web. It is authored and maintained by an amateur enthusiast who links 

together a wide range of material. As visitors quickly learn, however, the page defines 

itself in part through explicit contrast to the Blake Archive for "non-humans": that is, the 

William Blake Archive (WB A), an extensive scholarly text- and image-encoding project 

that has been freely available online since 1995, with funding and support from the Getty 

Grant Program, the National Endowment for the Humanities, the Library of Congress, 

and the Institute for Advanced Technology in the Humanities at the University of 

Virginia. As of this writing the Archive contains SGML-encoded electronic facsimile 

editions of some 49 separate copies of all 19 of Blake's illuminated books, as well as a 

growing selection of paintings, drawings, separate plates, engravings, and manuscript 

materials. The Blake Archive has also won the Modern Language Association's 2002–3 

prize for a Distinguished Scholarly Edition, the first time this award has been given to an 

electronic project. 

What could possibly be wrong with any of that? The author of the "Blake Archive for 

Humans" site expresses his objections this way: 

Their site may be complete and accurate, but it is not particularly easy to use, and it's 

chock-full of all the ridiculous trappings of the scholarly profession. While such things 

certainly have their place, they can also interfere with the appreciation of the actual work 

itself. On the other hand, this site is the only place you're going to find this many of the 

illuminated books in forms you can actually read…. And it, too is a work in progress, so 

it will only be getting better; especially when we all have broadband connections. 

I will say at the outset that I am not a disinterested party, having been affiliated with the 

William Blake Archive since 1997, first as its project manager and now as a consultant. If, 

however, as Blake claimed, "opposition is true friendship", then the opinions expressed 

above offer a friendlier-than-usual occasion for examining aspects of the WB As 

interface – and the development and design process that produced it – in some detail as 

this chapter's central case study in usability. For the phrase "the Blake Archive for 

humans" can't help but raise the damning specter of divided loyalties, the suggestion that 

the Blake Archive, with its admittedly formidable layouts of menus and button panels and 

search tables, may ultimately be more suited to a machine's vision than the human eye. 

And there is a certain truth to that, since the evolution of the Archive has been marked by 

constant trade-offs between the (perceived) needs of our (human) users and certain nonnegotiable 

demands of our adopted technologies. This divide between humans and 

machines is the crux of applied human-computer interaction, and is nowhere more visible 

than at the level of the user interface.

The notion that the WBA is "not particularly easy to use" and "chock-full" of "scholarly 

trappings" in fact typifies a certain range of responses we have received over the years. 

On the one hand, while we are happy to have users from many different constituencies, 

the site's primary mission has always been expressly conceived as scholarly research. To 

a certain point we make no apologies for that, since we emphatically believe there is 

place for serious humanities scholarship – presented in all its customary "completeness 

and accuracy" – on the Web. On the other hand, however, the comments I quoted above 

clearly speak to a real, felt frustration that the most comprehensive online resource on its 

subject should sometimes prove awkward for the non-specialist to navigate and use. 

Don't we understand (I hear these users saying) that what really matters here is Blake, and 

access to "the actual work itself?" Sometimes a particular feature of the site, which may 

appear intrusive to lay users (one of those aforementioned bells and whistles), is indeed 

there for more specialized audiences; for example, the ImageSizer applet, which allows 

scholars to display Blake's work on-screen at its true physical dimensions. This slippage 

between scholarly complexity and bad design is suggestive, however, and deserving of 

further study – many years ago, in the pages of the New York Times Review of Books, 

Lewis Mumford and Edmund Wilson famously railed against the user interfaces they 

found in the edited texts of the Center for Editions of American Authors, with their 

"barbed wire" thickets of scholarly apparatus. I suspect something of that same dynamic 

is at work here, and the challenge would seem to lie in distinguishing legitimate 

intellectual complexity from what is merely a poorly executed design. 

But that is perhaps easier said than done. The interface users encounter when they come 

to the Archive on the Web today is known to us behind the scenes as "WBA 2.0." In 

2000–1 we undertook a major overhaul that introduced several new features into the 

Archive and also remedied certain design flaws that had been noted by users and 

reviewers. 

As Stuart Curran wrote in his review of the 1.0 version of the site (Figure 34.1), the thencurrent 

design was "luckily malleable and [could] be altered to accommodate altering 

circumstances." The revised design we see in WBA 2.0 (Figure 34.2) clearly has been 

altered in a number of ways, and there is not space here to discuss all of them. A modest 

interface element, such as the arrow icons that are visible just beneath the pulldown 

menus toward the bottom of the screen, serves well to illustrate the nature of the revision 

process. The problem was that users who wished to view the images of a work in 

sequence were forced to scroll down the page to examine the image and perhaps work 

with the transcription, enlargement, or other associated features, and then scroll back up 

again to where the "Previous" and "Next" links were located. Moreover, the long vertical 

profile of the page meant that its top and bottom halves could not both be kept on-screen 

at the same time. This turned out to be particularly awkward in classroom settings, where 

the instructor was forced to do much scrolling upwards and downwards in order present 

students with a sequential series of images. The arrows situated in the lower portion of 

the screen now alleviate that extraneous scrolling, a simple design solution to a more or 

less straightforward usability problem.

The generous screen-space around the facsimile image of Blake's work (which 

contributes significantly to the page's vertical dimensions) is partly the result of the 

display area for our ImageSizer applet, but it is also a product of the site's aesthetic 

values. From the outset, the WBA was conceived as an image-based resource, with the 

"actual work itself" (to borrow our friendly critic's phrase) visibly central to the user's 

active eye, and buttons, links, and apparatus positioned either above or below. While the 

earlier version of the site relied on a tables layout for its presentation of this apparatus, 

WBA 2.0 employs pulldown menus. The pull-downs offer an efficient way to condense a 

number of different user options, but there was considerable internal debate among the 

project team as to whether or not they were the right solution. I reproduce below an 

abridged version of a long and thoughtful piece of e-mail from Morris Eaves, one of the 

WBA's three general editors (Eaves, e-mail to blake-proj Discussion List, "WBA 2.0." 

July 11, 2000), not only because it captures well the flavor of those internal discussions, 

but also because it illustrates the way in which humanities scholars are nowadays thrust 

into the role of interface designer – an impromptu role that some are quite comfortable in 

assuming, while others find it altogether unnatural. Eaves is clearly in the former 

category:

34.1 Figure WBA 1.0: The William Blake Archive (1997–2001)

34.2 Figure WBA 2.0: William Blake Archive (2001 to the present) 

I'm pretty sure I've read the whole discussion of the WBA 2.0 interface from start to 

finish. I know that time is short – but one of the nicest things about these digital 

confections is that they can be changed…. Here are a few personal reactions to the 

options that have been presented and discussed:… 

For me at least, there's a small chasm between form and function here. We all want the 

page to look good, and we all want it to respond readily and efficiently to the user's 

needs…. The way to make it look the best, probably, is to put everything on the page 

except the image behind a digital curtain and hide it from users until they want to do 

something – then they can open the curtain, rummage through those ugly old technical 

contents (previous/next, comparisons, enlargements, indexes, etc.) to find what they

want, and close the curtain again to recreate the pretty image on a nice, paperlike 

background. … 

On the other hand, the way to make the page function the best is probably to pull down 

the curtain, arrange all the tools neatly, and label them clearly, so that the user always has 

everything needful right at hand. Commercial kitchens usually have open shelves and 

overhead racks; home kitchens usually have doors on the cabinets and tools in drawers 

that close. 

One of our problems is that our user's toolbox is so large – much larger than the toolbox 

of a book-reader. So we have a lot more to label, organize, and (maybe) hide until 

needed…. It's worth remembering how hard it is for us to look at this stuff without 

insider's prejudice – after all, we've been using Imagesizer and Inote and several of the 

other functions available in those lower areas of the page for a very long time now, so we 

constitute the least confusable set of users on earth. But I think it's very hard to imagine 

other users – and I don't mean dumb users, I mean very smart, alert, expert Blakean users 

– who wouldn't welcome some visual help in organizing that formidable display of tools 

and information below the image. 

PS: Final confession about my kitchen: Georgia and I have two big commercial overhead 

racks full of hanging stuff, but then, at her insistence, we also have doors on our cabinets 

and drawers that close. I may be showing my hand when I say that I've never been 

completely happy about those doors on the cabinets – we built open shelves in our 

Albuquerque kitchen and I always loved them. The Rochester kitchen compromise must 

have something to do with living together for 100 years. 

As it happens, we arrived at a similar compromise (in much less time) for WBA 2.0, the 

doors variously put on and left off different interface elements. And as both Curran and 

Eaves note, "digital confections" like the WBA can always be changed – at least in 

theory. But in practice, as I suggested in my introduction, the interface tends to come 

very late in a project's development cycle (Eaves's recognition above that "time is short" 

is a reference to the fact that our lead programmer was about to be reassigned to another 

project). Most digital humanities scholarship is produced incrementally, in layers; at least 

for large-scale projects like the WBA, housed within particular institutional ecosystems, 

those layers tend to be more sedentary than we like to admit. The truth is that WBA 2.0 is 

unlikely to change much in its interface until the site's next major incarnation as WBA 

3.0. 

There is also another consideration, beyond the exigencies of project management. The 

conventional wisdom, reinforced by commercial marketing campaigns like Microsoft's 

"Where Do You Want to Go Today?" and AT&T's "You Will", is that computers can do 

just about anything we want them to do. As programmers and developers know, however, 

there are always limitations that come with particular hardware and software 

environments, and those limitations render the computer an eminently material medium, 

this materiality not so different in its way from the characteristic marks and traces left by 

Blake's brushes and burins. Both versions of the WBA, for example, rely upon Inso's

DynaWeb software to provide the stylesheets needed to display our SGML-encoded 

electronic editions in a conventional web browser. We have, in the process, customized 

the appearance and behavior of the DynaWeb environment to such an extent that it may 

be unrecognizable from its out-of-the-box implementation. But while DynaWeb has been 

an enormous boon to the Archive, making possible (among much else) powerful search 

functions, its idiosyncratic architecture clearly imposes constraints that manifest 

themselves at the level of the site's interface. In the case of the navigation arrows that I 

discussed above, for example, some readers may have wondered why we opted for icons 

on the bottom half of the page but retained the textual "Previous/Next" links up top. The 

answer is that (for reasons too esoteric to detail) the DynaWeb environment could not be 

made to accommodate the graphical image files of the icons in the top portion of the 

screen. Curran, likewise, notes the strong hierarchical nature of the WBA: "the user must 

descend four levels to get to the texts of the individual illuminated works…. This notion 

of penetrating to an inner sanctum is, of course, antithetical to Blake." We have attempted 

to rectify this in WBA 2.0 through the addition of Comparison and Navigator features, 

but the broader point is that this particular order of things is largely an artifact of the 

DynaWeb architecture, which was originally intended to support not Blake's illuminated 

visions but, rather, large volumes of text organized in top-down hierarchical structures. 

The technologies we work with at the WBA thus constantly make their presence felt, 

visibly and palpably pushing back against the interface we attempt to enfold around them. 

This situation is particularly acute in the digital humanities, where necessity often 

dictates that we adopt and adapt tools and technologies that were originally developed for 

other needs and audiences. If, as I suggested earlier, interface design is a dialectic 

between the competing demands of human and machine, then the art and science of 

usability lies in striking a balance between the two. 

The William Blake Archive is – emphatically – for humans. But humans are not 

homogeneous, and different users will have different needs. While it will be impossible 

to please everybody all of the time, a design team must at least ensure that it is meeting 

the needs of its most important user communities most of the time. We believe we can 

now make this claim. Following the example of the large portals, more and more sites on 

the Web are also trending towards user-customizable interfaces, and this is a potential 

long-term solution for a project like the WBA. But it is a solution that, like all others, will 

have to be tempered by the non-virtual realities of staffing and development time, 

software and data standards, and project funding. 

Coda: Magic Carpet Ride 

The basic conventions of the desktop windows GUI have not evolved much since their 

popular inception with the Apple Macintosh in 1984. Until recently, however, our display 

hardware had not changed very greatly either (despite higher resolutions and the 

universal shift from monochrome to color). But if, as I insisted earlier, the addition of a 

glass pane in the 1950s irrevocably situated the computer within the sphere of visual 

representation, then everywhere today there are signs that computers may be on the verge 

of another broad-based shift in the tangible construction of their information spaces. The 

rise of featherweight laptops, tablet computers, PDAs, and wearable devices, on the one

hand, and wall-sized or room-based projection and display systems, on the other, is even 

now wrenching apart the Procrustean setup of the desktop workstation, which has forced 

users to accept what hindsight will reveal to be an almost unbearably constricted and 

contorted relationship with our machines (while the ongoing pandemic of carpal tunnel 

syndrome and other repetitive strain injuries offer more immediate and irrefutable bodily 

evidence). In this same climate, HCI research has also convincingly demonstrated that the 

conventions of the desktop GUI do not scale well to either larger displays, such as one 

might find with wall-sized projections, or to smaller displays, such as one now finds on a 

PDA. 

What, then, does the future hold? Of one thing I am sure: the typewriter and the television 

set will not enjoy their conceptual monopoly over our computing machinery for much 

longer. My own ideal system might look and feel something like this. I think of it as a 

magic carpet: a rectangle of thin, flexible, waterproof plastic, perhaps 3x4 feet, which I 

carry about rolled up under my arm (or folded in a bag). I can lay it out on any tabletop or 

flat surface, or else unfold only a corner of it, like newspaper readers on a train. The 

plastic sheet is actually an LCD screen, with an embedded wireless uplink to the Web. 

Applications, both local and remote, appear on its surface like the tiles of a mosaic. I 

move them about physically, dragging, shrinking, or enlarging them with my hands, 

pushing and pulling them through the information space. Text entry is primarily by voice 

recognition. The keyboard, when needed, is a holographic projection coupled to a motion 

tracker. Data are stored on a solid state memory stick I keep on my keychain, or else 

uploaded directly to secure network servers. 

All of this may sound like over-indulgent science fiction – "Hamlet on the holodeck", to 

borrow a phrase from Janet Murray. But in fact, most of the elements listed here – 

wireless networking, voice recognition, keychain data storage, touch-screen and tangible 

user interfaces – are already in common use. And the rest – the motion trackers, paperthin 

LCDs, and holographic input devices – are all already at or past the initial 

development stage. The "magic" carpet is actually just an extrapolation of real-world 

research that is ongoing at places like the Tangible Media Group (and elsewhere) in the 

Media Lab at MIT, the Interactivity Lab at Stanford, the Metaverse Lab at the University 

of Kentucky, the GVU Center at Georgia Tech, and the Human-Computer Interaction 

Lab at the University of Maryland. While the fortunes of any one individual technology 

will invariably prove volatile, even a quick scan of these sites leaves no question that the 

next decade of research in interface, usability, and HCI will take us for quite a ride. One 

of the major challenges for the digital humanities in the coming decade will therefore be 

designing for interfaces (and designing interfaces themselves) outside of the 13- to 21inch 

comfort zone of the desktop box. 

Works Cited or Suggestions for Further Reading 

The Blake Archive for Humans. Accessed November 15, 2002. At 

http://www.squibix.net/blake/.

Bolter, J. D. and D. Gromala (2003). Windows and Mirrors: Design, Digital Art, and the 

Myth of Transparency. Cambridge, MA: MIT Press. 

Carroll, J. M. (2002). Human-Computer Interaction in the New Millennium. Boston: 

Addison-Wesley. 

Cooper, A. (1995). About Face: The Essentials of User Interface Design. Hoboken, NJ: 

John Wiley. 

CSS Zen Garden: The Beauty of CSS Design. Accessed December 30, 2003. At 

http://www.csszengarden.com/. 

Curran, S. (1999). Review of the William Blake Archive. TEXT 12: 216–19. 

Drucker, J. (1994). The Visible Word: Experimental Typography and Modern Art, 1909– 

1923. Chicago: University of Chicago Press. 

Fuller, M. (2003). Behind the Blip: Essays on the Culture of Software. Brooklyn, NY: 

Autonomedia. 

Gelernter, D. (1998). Machine Beauty: Elegance and the Heart of Technology. New 

York: Basic Books. 

GVU Center, Georgia Tech. Accessed May 16, 2003. At http://www.cc.gatech.edu/gvu/. 

Hawthorne, N. (1948). The Portable Hawthorne, ed. M. Cowley. NewYork: Penguin 

Books. 

Human-Computer Interaction Lab, University of Maryland. Accessed May 16, 2003. At 

http://www.cs.umd.edu/hcil. 

Ivins, W. M. (1953). Prints and Visual Communication. Cambridge, MA: MIT Press. 

Johnson, S. (1997). Interface Culture: How New Technology Transforms the Way We 

Create and Communicate. San Francisco: HarperEdge. 

Laurel, B. (1991). Computers as Theatre. Reading, MA: Addison-Wesley. 

Manovich, L. (2001). The Language of New Media. Cambridge, MA: MIT Press. 

McGann, J. (1993). Black Riders: The Visible Language of Modernism. Princeton, NJ: 

Princeton University Press. 

Metaverse Lab, University of Kentucky. Accessed November 15, 2002. At 

http://www.metaverselab.org/.

Negroponte, N. (1995). Being Digital. New York: Knopf. 

Nelson, J. (2000). The Last Machine with Moving Parts. Accessed May 14, 2003. At 

http://www.heliozoa.com/ending3.html. 

Nielsen, J. (2000). Designing Web Usability: The Practice of Simplicity. Indianapolis: 

New Riders Publishing. 

Norman, D. A. (1990). Why Interfaces Don't Work. In B. Laurel (ed.), The Art of Human- 

Computer Interface Design (pp. 209–19). Reading, MA: Addison Wesley. 

Norman, D. A. (2002). The Design of Everyday Things. New York: Basic Books. 

Paley, B. Text Arc. Accessed November 15, 2002. At http://www.textarc.org. 

Raskin, J. (2000). The Humane Interface: New Directions for Designing Interactive 

Systems. Boston: Addison-Wesley. 

Rockwell, G. Eye-ConTact: Towards a New Design for Text-Analysis Tools. Computers 

and the Humanities Working Papers. November 15, 2002. At 

http://www.chass.utoronto.ca/epc/chwp/rockwell/index.html. 

Samuels, L. (1997). Poetry and the Problem of Beauty. Modern Language Studies 27, 2. 

At http://epc.buffalo.edu/authors/samuels/beauty.html. 

Shelley, M. (1992). Frankenstein, ed. M. Hindle. London: Penguin Classics. 

Shneiderman, B. (1998). Designing the User Interface: Strategies for Effective Human- 

Computer Interaction. Reading, MA: Addison-Wesley. 

Shneiderman, B. (2002). Leonardo's Laptop: Human Needs and the New Computing 

Technologies. Cambridge, MA: MIT Press. 

Siegel, D. (1995–6). Severe Tire Damage on the Information Superhighway. At 

http://www.dsiegel.com/damage/index.html. 

Stephenson, N. (1999). In the Beginning Was the Command Line. New York: Avon. 

Takahashi, T. Word Perhect. Accessed May 14, 2003. At http://www.e- 

2.org/commissions_wordperhect.html. 

Tangible Media Group, MIT Media Lab. Accessed May 16, 2003. At 

http://tangible.media.mit.edu/. 

Tognazzini, B. (1992). Tog on Interface. Reading, MA: Addison-Wesley.

William Blake Archive. Eds. M. Eaves, R. N. Essick, and J. Viscomi. Accessed 

November 15, 2002. At http://www.blakearchive.org. 

35. 

Intermediation and its Malcontents: Validating 

Professionalism in the Age of Raw Dissemination 

Michael Jensen 

Over the last few years it has become clear that digital publishing is far more than just 

making a digital version of paper available online. Rather, we are (finally) beginning to 

view networked e-content as a new form of publication in its own right, with its own 

strengths and weaknesses, its own imperatives, its own necessary logics. What I hope to 

do in the next few thousands of words is to explore the issues of networked publishing 

from a nonprofit publisher's perspective, having been involved in computers and digital 

publishing within the nonprofit publishing world since the late 1980s. 

I'll make a case, I hope, for encouraging a broader engagement between the many sectors 

within scholarly communication (publishers, scholars, libraries, technologists, editors, 

collaborators) by dint of common fundamental mission, audiences, and funding sources. 

Further, I hope to survey some of the themes that are important to consider as networked 

publishing matures from its infancy, matters of significance to anyone considering the 

issues raised by digital publications in the new century. Finally, I hope to address the 

value of professionalism in the digital publishing arena. 

The Nonprofit's Metamission 

At an Online Computer Library Center (OCLC) conference in the late 1990s, the new 

President of the University of Arizona, Peter. W. Likins, made an intellectually 

compelling distinction that I've tried to promote ever since: 

A for-profit's mission is to create as much value for its stockholders as possible, within 

the constraints of society. The non-profit's mission is to create as much value for society 

as possible, within the constraints of its money. 

The academic and educational communication sectors have much in common, which 

ought to be driving many of the trends in online communication. How do we take 

advantage of this moment of unparalleled interconnectivity to "create as much value for 

society as possible", given the cultural constraints on our money? Do we maintain the 

same structures as before? Do the new capabilities militate for a revolution? 

Networked publishing is going through its toddler phase, it seems to me – stumbly, never 

quite balanced, uncertain of its size and abilities, and amazed and delighted by itself. One

key to toddlerhood is an utter ignorance of one's own ability to be ignorant. To counter 

that, let me first outline what I think are some of the constraints and capabilities of the 

current system – from the perspective of a nonprofit publisher. 

The Current System 

University presses, scholarly societies, academic publishers, and other nonprofit 

publishers currently perform a set of societal and scholarly roles, as well as performing a 

set of practical roles. 

The societal and scholarly roles include identifying promising (and often nascent) 

scholarly research, encouraging the development of scholarly work, and enhancing the 

accessibility, readability, and comprehensibility of that work. In the context of scholarly 

communications in general, these publishers validate scholarly material, integrate and 

coordinate scholarship in discipline-specific collections, and (not least) provide authority 

for humanities and social sciences scholarship for tenure and promotion. They are also 

frequently publishers of what are called "midlist" publications – works which may sell 

between 500 and 5,000 copies, indicating a small (if committed) audience. It also can be 

the scholarly publisher's job both to provide a promotional context for a work, and to put 

the works into the largest context within the discipline or the field. 

Everything Old is New Again 

For an author, scholar, or scholarly publisher in the twenty-first century, the seemingly 

obvious approach to the new capabilities – and the seeming imperative – is to perform all 

these roles in the "e-arena." It's becoming ever more clear that this seeming simplicity is 

anything but simple. Let me lay out some of the complexities before moving on. 

In the world of print publishing, there is a truism: that "journals publishing can't be done 

small" – that developing the skill-set and software for maintaining and applying 

individual subscription mechanisms is so costly that you need at least six or eight journals 

to share the costs. Otherwise, the publishing requirements and concomitant costs cannot 

be amortized across enough projects to make it affordable. Because of this principle, 

among the 120 university presses, only a handful have journals programs. "Stay with 

your strengths", says conventional wisdom. 

That truism applies to technology as well. When the implications of the revolutionary 

changes of the last few years are taken into account, it becomes evident that entirely new 

systems have to be created to handle the realities of the New Publishing. It's not as if the 

set of problems a publisher must face is only transformed into a new set of problems 

when you factor in networked publishing. Rather, the diversity of problems – and the 

diversity of skills necessary to address them – blossoms. Entirely new problems, 

unconnected to anything from the print world, must be confronted.

A print publisher must deal with physical returns of books from bookstores, while 

strategizing about how much content to give away on the Press website. A print publisher 

must determine print run sizes (because most books are still generally more economic to 

print in quantities of 600 at a time than they are to "print on demand" – at least as of 

January 2004), while deciding whether to include a CD in the book and adding $10 to the 

retail price. A print and digital publisher must not only produce product, record-keep, and 

generate monthly reports on the order fulfillment of paper books (an inventory-based 

system), but may also need to produce, record-keep, and generate monthly reports on 

inventoryless material (e-books), and may need to do so on potential-inventory material 

(print-on-demand), and may even need to produce, record-keep, and the rest on 

subscription-based access methodologies. This is not a decision like "paper or plastic." 

It's rather "cash or coins or check card or credit card or check or voucher or foodstamps 

or green-stamps or goldstamps or barter." It's not only "transformed", it's 

metamorphosed. 

The electronic enterprises themselves hold danger: it is painfully easy to choose the 

wrong preferred technology for an audience or discipline by misunderstanding their 

readiness for new technology or misreading their desire for alternative presentation 

modes. Or choose the wrong technology for material by force-fitting new tools on old 

content, or locking older accommodations to print restrictions into new presentations. Or 

choose the wrong technology for a changing environment and by so doing lock the 

content into a non-adaptive format. Or spend the publication budget on cutting-edge culde-sac 

software, or confuse "cool" with "valuable", or conflate readers with purchasers, 

or audience with customer, such as by presuming that by developing a site aimed at 

students to promote one's texts, one can entice the libraries serving those markets to 

purchase what their readers are acquiring for free. Or depend overly much on the novelty 

value of e-publishing, and believe that simply by building it, you will attract business. 

Any single one of these threatens to either beggar the publisher or compromise the 

project, and shouts "Danger!" to most publishers. 

Those who have deep and broad knowledge of the processes of publication – publishing 

professionals – are vital in many ways. Who better than a professional publisher to help 

navigate these dangerous waters? 

Appropriate Presentational Models 

Every living reader today has grown up in a world in which paper dominated the 

informationscape in terms of authority, validity, and value. The primary models of 

argumentation and presentation in scientific and scholarly discourse are still 

predominantly linear, partly because of the nature of exposition and presentation in a 

linear-publishing, paper-based world. 

This makes it likely that a large proportion of the output of scholars and specialists can be 

expected, whether consciously or unconsciously, to mimic the modalities of linear 

expression. Does that mean that most publications are served best by paper? Perhaps - 

though the answer to that question depends on many factors. Some material is ideal for

online publication (reference works, huge resource bases, highly interconnected and 

changing material, multimedia collections), though there may be no simple cost-recovery 

system that can be implemented effectively for such publications. Other material may not 

be well suited to digital publication – novels, traditional monographs, linear 

argumentation of thesis/promotion/conclusion, poetry, art (still), typographically 

challenging material, etc. 

Different media serve different content types more or less effectively; it's important to 

recognize what a medium is best suited for. A million JPGs couldn't tell the story of 

Moby Dick like the words do; a million words can't thrill like the Ninth Symphony. We 

are just discovering the potential of networked presentation, but shouldn't be seduced into 

thinking that Javascript or Flash is the inevitable best (or only eventual) means of 

communication. 

Appropriate Cost-recovery Models 

Just as there are media appropriate for particular communications, there are economic 

models appropriate for particular media, content, and audience. 

"Cost recovery is destiny", and an organization's mission should dictate cost-recovery 

models. For nonprofit publishers with whom dissemination is the priority, new models 

are more easily implementable than with nonprofit publishers for whom fiscal stability is 

the prime directive. For associations, cost-recovery models are different than for corporately 

owned publishers. Fiction publishers have different uses of the medium than 

reference publishers do. 

Distinguishing between the possible publishing choices is complicated. To make the best 

choices, one must understand appropriate (even available) technologies, appropriate cost 

recovery and maintenance plans, and what the appropriate media (and cost recovery 

mechanisms) are for the particular audience. 

What model will work best with your project? Institutional subscription? Print-ready PDF 

for a fee? Print-on-demand? Time-based access? Free old material, charged new? Free 

new material, charged archive? Making these choices ain't easy. 

"Free" Publishing 

I once realized that a project I was directing was spending more on developing accessrestriction 

mechanisms (for subscription-only access) than it was on developing deeperexploration 

mechanisms. Over 60 percent of our development dollars were being spent 

on keeping people from reading our material. That seemed silly, and helped convince me 

that I should be a bit more open to alternative models for scholarly and academic 

publishing.

At the National Academies Press, we make more than 3,000 reports of the National 

Academy of Sciences, the Institute of Medicine, and the National Academy of 

Engineering freely available to any browser, hoping to encourage browsers to purchase 

what we consider to be an "optimal version" – the print book, or multi-page PDF. We 

recognize that nearly all of our publications are designed for print reading (being 

predominantly linear exposition of how the best science may inform issues of public 

policy), and so see the online versions as only a minor "threat" to traditional print sales. 

That's one model of "free" publishing – using the Web to promote the content, while 

selling the container. 

In the new networked environment, there are many mechanisms of nearly "outlay-free 

publishing" – though it's worth discriminating outlay-free publishing from the mythical 

"cost-free" publishing. With enough volunteer labor, or hierarchical control, or 

browbeating, or passion, today virtually anything can be made available "for free" via 

online presentation. The work may not be best served by this model, but it is a viable 

means of presentation. 

The Web makes such raw dissemination possible, and it's an exciting prospect. Today, 

we're still in the wonderful volunteerism phase, where individual labors of love and 

institutional experimentation into electronic publishing are going on. My worry is that 

volunteerism is reaching its limits as the true complexities of online publishing begin to 

demand cost-recovery mechanisms for sustenance. 

Like many other volunteer efforts, e-projects have proven hard to sustain. Countless 

worthwhile volunteer projects have died when a central figure moves on, or when the 

project reaches that first grand plateau (after which the real work begins), or when the 

grant funding for the "e-project" is two years gone. 

Promoting a Sustainable Scholarly Information 

Infrastructure 

Especially in an online world where disintermediation is at play, it's my contention that 

there is still a need for some kinds of intermediation – for publishers to be involved in the 

"making public the fruits of scholarly research" (as Daniel Quoit Oilman, first director of 

the oldest University Press in the country, Johns Hopkins, so famously said). I'd further 

contend that it generally makes more sense for our culture to support nonprofit, 

networked publishing through its institutional mechanisms (university presses, 

association publishing, NGO publishing, and the like) than to either relinquish it to the 

commercial sector, or to presume that volunteerism will enable educated discourse and 

debate based on a rich, robust set of resources disseminated online. 

To accomplish that, it's important to have cultural cost-recovery systems that support the 

right processes – that promote experimentation, expand dissemination and access, yet 

prevent disruption. Without an appropriate means of supporting the institutional

acquisition, digitization, and presentation of significant resources, real dangers accrue, 

the likes of which I've personally witnessed. 

The "Publishing Revolution" in Eastern Europe 

I saw the results of those dangers first-hand, in another place and time. During the period 

1990 to 1995, during and after the fall of the Soviet Union, I was involved in a sequence 

of educational endeavors helping publishers in Estonia, Latvia, Lithuania, Poland, 

Hungary, and Czechoslovakia adapt to new technologies, understand how nonprofit 

publishing worked in a free-market capitalist system, and helping university presses adapt 

to the new realities of technology. 

By far the deepest experience I had was in Czechoslovakia, before it split into two 

countries. Throughout this period I worked with publishers and scholars, frequently 

visiting Prague and other towns, and watching as the totalitarian socialist model – which 

had strengths as well as significant weaknesses – was replaced by systems constructed by 

amateur capitalists who had at best a naive understanding of the nature of capitalism. 

This created a publishing system with significant weaknesses, as well as (perhaps) a few 

strengths, especially in arenas like public-value, scholarly, and educational publishing. 

The Soviet model of scholarly publishing was tremendously inefficient. Every university 

published its own material for its own students: introductory biology coursebooks, 

collections of essays, lecture notes, monographs, specific research, all were printed and 

bound at the professor's behest. There was heavy subvention from the universities, which 

were (in turn) heavily subvened by the state. There was virtually no economic feedback 

in this system of scholarly publishing, because there were virtually no cost-recovery 

mechanisms at all, apart from a token fee of the equivalent of about 25 cents or so per 

"scripta", as the class publications were called. Hardbacks cost the equivalent of a pack of 

Czech cigarettes. 

Editorial selection hardly entered into the matter at the "university publishing" level. 

Every year, a few works from university publishing were designated as worthy of being 

published in hardback, usually in order to give their universities a medium of exchange 

for publications from the outside, non-Soviet world (librarians still remember the 

awkward near-barter system between Soviet institutions and Western ones). 

Often these hardbacks were lavishly produced, but there was rarely any relationship 

between "list price" and publication costs. The system separated all indirect costs to the 

point of immeasurability, by massive bureaucracy, and in fact discouraged any costcontainment 

systems based on merit or audience. Instead, decisions were based mostly on 

"old-boy" status. An important professor, for example, could insist on having 50,000 

copies of his book on the anatomy and aerodynamics of bat wings printed (in Czech, 

without translation); this was to his advantage because his personal royalty was based (by 

the Soviet diktat) on number of pages printed, rather than number of copies sold.

Other state-run publishing houses also published scholarly work in philosophy, science, 

metaphysics, etc.; they had more freedom of choice of what to publish than university 

presses, but their work was also heavily subsidized, and prices and print runs were often 

at the whim of "important" people. Nonetheless, publishing was considered a social 

value, and publishers operated in that way. 

When the Soviet Union collapsed, there were countless warehouses with hundreds of 

thousands of copies of the writings of Stalin which nobody would buy, even at the 

minuscule prices charged in the Soviet days, much less in the post-Soviet economic 

crises. And those warehouses also contained about 49,800 copies of that book on the 

aerodynamics of the bat wing. In Czech. 

But that same socially supported, exceedingly expensive publishing industry produced a 

broad variety of valuable, diverse, and very inexpensive books, which deeply affected the 

habits of the Czech culture. New books came out every Wednesday, and book sales 

bloomed like flowers in a field – every square had bookstores, every tram stop had a 

cardtable with someone selling books. 

When I first spent time in Prague, just after the November 1989 revolution, I saw 

everyone – and I mean the butcher and the hardhat and the professor alike – reading 

books. On the trams, the metro, the streetcorners, and lining up to pay a few crowns for 

new titles, the citizenry read books – not True Romance, or escapist potboilers but, rather, 

philosophy, history, science, metaphysics. The inefficient system of subvention had 

created a highly literate, exceedingly well-read populace, who read for fun, and read in 

depth, all the time. 

When the revolution set in fully, suddenly universities, whose subventions were being 

completely reconsidered by new governments, were telling their "presses" that they had 

to become self-sufficient in two years, and many were told they had to start giving money 

back to their universities – that is, make a profit – in the third year (much like several 

major US university presses have been asked recently to do). For most of the Czech 

university policy-makers, their recipe for capitalism was: add a pinch of slogan-level 

ideas picked up from reruns of Dallas, blend with the flour of the Voice of America, add 

a dash of Hayek, and finally spice with an understanding gleaned from dinner-table 

conversations. 

The resulting policies gave little consideration to the realities of publishing costs and cost 

recovery; had no understanding of the infrastructure needs of the industry (like 

distribution, and warehousing, not to mention computers, databases, editorial experience, 

knowledge); had no understanding of the place of scholarly publishing in the educational 

system; and no recognition that in a revolutionary economy, nobody would have spare 

money to make discretionary purchases. 

Three years after the revolution, the prices for books were often ten to fifty times what 

they were in 1990. The publishers who were managing to survive did so by subsidizing 

their continuing translations of Derrida and Roethke with pornography. During that

period, bookstores closed down everywhere. Publishers closed down across the country. 

Citizens stopped reading every day. By 1995, nobody was reading metaphysics on the 

tram. A quarter of the university presses I knew of were closed, well over half of the 

bookstores I knew of in Prague were closed, and the scholars I'd befriended were telling 

me that they couldn't get anything published any more – there were fewer outlets than 

ever. 

This transformation was a sad thing to watch, especially when I'd been so delighted by 

the intense literacy I'd seen initially. The culture turned to free or cheap content 

(television, newspapers, advertising, radio, Walkmen) rather than to the new – and more 

complex – ideas contained in books. Promotion and branding became the watchwords of 

the day, rather than subtlety of analysis or clarity of presentation. 

Let me be clear that I think that neither model was fully right: the absurd redundancies 

and inefficiencies of the Soviet system were far too costly for me to support. However, 

the result was frequently a marvelously high level of intellectual discourse. Regardless, 

the follow-on quasi-capitalist system was far too brutal, and had consequences that they 

are still feeling in the Czech Republic to this day: far fewer high-level publications in 

their own language, far fewer high-quality scholarly publications in general (a significant 

problem in a small language group), and cultural costs that are hard to quantify but easy 

to identify as causing a kind of intellectual hunger. 

What this has to do with the current revolution of digital presentation should be selfevident, 

though the parallels are somewhat indirect. We must recognize that we are in a 

revolutionary period, and we must be careful not to damage the valuable qualities of the 

current system based on inexperienced premises – that is, based on a naive understanding 

of the coming state of scholarly communication, which is based on a few visionaries' 

description of what is "inevitable." As users and creators of scholarly communication, we 

must carefully assess what qualities we want to maintain from the current system, and be 

sure that we create evolutionary pressures that encourage a "scholarly communication 

biosystem" which serves scholarship well. 

My fear is that without some care, we may undercut (if not destroy) the valuable 

characteristics of our current nonprofit publishing system by revolutionizing it without 

understanding it. That is, if we revolutionize ourselves out of the ability to effectively 

support a nonprofit industry disseminating public value, we will have done more harm 

than good. 

The Value of Publishers 

Currently, scholarly publishers – specialists in the selection, preparation, presentation, 

and dissemination of scholarly material – are a valuable and necessary part of scholarly 

communication. There are some who are still under the false impression that in the new 

environment, publishers aren't necessary, just as there are others under the false 

impression that libraries are soon to be moot, or that universities are outmoded 

institutions.

In our "disintermediated world" there is some truth to all of those points. Every 

intermediary is potentially erasable by the direct producer-to-end-user contact made 

possible by the existence of the Internet. 

It's true, for example, that some professors could teach students directly, without a 

university's intervention, or with only one big university's accreditation (and conceivably 

only a handful of professors); similarly, scholars could spend the time producing the 

complicated presentational structures required for effective online publication, without 

the need for a publisher. Universities could conceivably contract with mega-information 

providers for just-in-time provision of scholarly content using contractual agents, 

replacing the need for an active library. 

But I maintain that having specialists do what they do best is the most efficient model in 

general, and that intermediaries, though not required, will tailor the intermediation for 

particular audiences, and will identify and produce material preferable to non-selected, 

non-edited, non-vetted, non-enhanced material. None of the intermediaries I mentioned – 

the universities, the scholarly community, the scholarly publishers – will become moot in 

our lifetime, if for no other reason than the inertia of the credentialing culture combined 

with the inertia of presentation systems. 

Enough Naive to Go Around 

For well over a decade I've watched members of the computer scientist community and 

the librarian community naively discuss publishing as either a networked-database 

problem or as a distribution and classification problem, and conclude that they each can 

do the job of publishing better than the existing publishing institutions. 

And while some very interesting and valuable projects have come out of both 

communities (SPARC, the physics preprint server, UVAs Etext Center, etc.), many of the 

rest were diversions of resources that I believe might have been more usefully applied to 

supporting and expanding the scholarly world's existing strengths in the scholarly/ 

academic publishing arena. The shortcomings in the perspectives of the various interest 

groups are worth noting: 

• Librarians too often think of scholarly content as primarily something to purchase, 

categorize, metatag, and archive (and provide to patrons), independent of the content's 

quality or utility. 

• Technologists too often see the world of scholarly content as a superlarge reference set, 

crying out for full-text indexing and automated interconnections. "It's really just a bunch 

of XML content types", you can almost hear them muttering. 

• Publishers – even nonprofit publishers – too often see the world of scholarly content as 

primarily composed of products, each requiring a self-sustaining business model to be 

deemed "successful." Unless a publication is self-supporting, it's suspect.

• And status-conferring enterprises (like tenure committees) seem to see innovative work 

engaging digital tools in the service of scholarship as being "interesting exercises" akin 

to, say, generating palindromic verse, or writing a novel without an "o" in it. 

The humanities/social sciences scholar generally sees his/her work as an abstract and 

tremendously personal effort to illuminate something previously hidden. S/he sees 

technology simultaneously as a hindrance to, a boon to, an obvious precondition for, and 

utterly inconsequential to, his/her work. Scholarly communication is all of these, and 

even more. There are other players in the game of scholarly publishing: intermediaries, 

agents, public information officers, sycophants, supporting scholars and scholarship, 

student engagement, pedagogical application, external validation, etc. Each has its own 

interests and agendas, and sees the world through its own interest-lens. 

The question scholarly publishing should be answering is "how can we most 

appropriately support the creation and presentation of intellectually interesting material, 

maximize its communicative and pedagogical effectiveness, ensure its stability and 

continual engagement with the growing information universe, and enhance the 

reputations and careers of its creators and sustainers?" If that question is asked, we are 

likely to answer it. 

Without a mission driven by that question, the evolutionary result will be driven by the 

interests of the enterprises initiating the publication: library, scholar, nonprofit press, 

academic departments, associations – or, if the publication is initiated in the commercial 

sector, by the potential for profit. 

Mission-driven Publishing 

The projects with sustainable futures, it seems to me, are those which are joint 

partnerships between stakeholders – such as scholar/publisher enterprises, 

publisher/library, scholar/technologist, association/publisher, 

association/scholar/publisher, etc. Hybrid vigor is a wonderful thing, and makes for 

hearty crops. 

The History Cooperative (), for example, is a joint 

project between the two major historical associations (the American Historical 

Association and the Organization of American Historians) and the University of Illinois 

Press, with technology participation from the National Academies Press. These groups 

form the executive core of a project enabling the integrated networked publication of 

History journals, on terms established by the executive body. Currently consisting of nine 

journals and growing, the two-year-old project is increasing its specialized tools, its 

interdisciplinary value, and its pedagogical utility. More importantly, it has helped these 

groups understand each other in a collegial environment of shared goals. 

The National Academies Press is another example () – a publisher 

"run by its authors" in the sense that it is a dual-mission-driven publisher, expected to 

maximize dissemination of the 180 reports annually produced by the Academies, and

sustain itself through print sales. This expert/publisher/technologist project has resulted in 

3,000+ reports being browsed by 7 million visitors for free in 2002. Every page is 

accessible for free, yet tens of thousands of book orders annually support the enterprise. 

Project Muse (), a joint publisher/library/journal project, uses 

institutional subscription to make itself available online. Within subscribed organizations, 

free unlimited browsing of over 100 journals is enabled. 

CIAO, the Columbia International Affairs Online (), is a joint 

publisher/technologist/library project, providing a resource pertaining to International 

Affairs that combines formal, informal, primary, secondary, and "gray" literature together 

in a coherent, institutional-subscription-based context. 

The University of Virginia Press's Electronic Imprint () is 

leveraging the strengths of its University's electronic-publishing capabilities (IATH, the 

E-text Center, the Press), to craft sustainability models for significant electronic 

publishing projects. The Press's institutional skill (and ability) in processing income 

enables that craftsmanship, which may end up maintaining and growing important works 

of scholarship. 

These and other examples of multiple institutions recognizing their common goals give 

strength to the hope that coordination is possible among the various stakeholders in the 

scholarly publishing arena. 

The Big C 

If the nonprofit stakeholders can coordinate, even slowly and gently, to achieve that 

mission to "support the creation and presentation of intellectually interesting material, 

maximize its communicative and pedagogical effectiveness, ensure its stability and 

continual engagement with the growing information universe, and enhance the 

reputations and careers of its creators and sustainers", then perhaps their shared interests 

will become clearer in confronting the Big C of educational and scholarly publishing: 

Copyright. 

"The Big C" usually means Cancer, and in this context is intended to, since the principles 

of copyright are currently metastasizing in ways unhealthy for our culture. The principles 

of author's rights have been shanghaied for the benefit of the vested interests of 

copyright-holders: publishers, museums, rights-holders, and other potential parasites. 

I'm generally disgusted by the intellectual and academic boat-anchor represented by 

intellectual property law in the newly networked world. So far, copyright law has done 

more to preclude full intellectual analysis of our digital condition than anything else in 

society. As an author, I certainly am glad to assert rights to my own work, but the blade 

honed to cut paper doesn't necessarily cut wood well, nor styrofoam, nor tin. That is, 

intellectual property law differentiates quite poorly between use types, which means that 

I can't glancingly allude (even transclusively) to someone else's work without risking

lawsuits; I can't sample Snoop Dogg's samples without that risk; without risking legal 

action, I can't quote my own words (that is, if I've contractually signed away rights to 

those words). I certainly cannot harvest the video footage of CNN or the articles of the 

New York Times and the Washington Post, and then use that footage in a multimedia 

monograph, analyzing Bush's strategies for forcing a war attitude toward Iraq in the two 

pre-election months of 2002 – even if it does not threaten CNN's or the Times's or the 

Post's cost-recovery mechanisms. 

Intellectual property restrictions, compounded by the attitudes of publishers, 

technologists, librarians, and academic bureaucracies, have left many writers and scholars 

dispirited. To acquire tenure, the scholar must have work published in print. To do that, 

great labor is required on many counts, but developing rich Webliographies is not one of 

them. Integrating intellectual-property-controlled material into one's own work is 

dangerous, unless it's done in the "dead" form of print. 

In light of these barriers to full intellectual analysis of our own cultural and social 

condition, it's clear to me that something needs to change. Since the 1990s, nonprofit 

publishers have been kneecapping themselves, by routinely siding with the for-profit 

publishers on intellectual property and copyright issues, instead of working to develop 

more culture-friendly standards and practices by engaging more fully and flexibly with 

the authors, scholars, societies, associations, libraries, and academic institutions upon 

which nonprofit publishing depends. 

By making these choices (often passively, by making no choice), the nonprofit publishers 

have allowed themselves to be tarred with the same brush that is painting the commercial 

publishers, who are seen as parasites feeding off the authors, rather than as participants in 

the process of scholarly communication. In this way the nonprofit publishing sector is 

impeding its own ability to be recognized as a resource; instead, it has allowed the meme 

that "all publishers are the same" to prevail. From my perspective, nonprofit publishers – 

academic, nonprofit, scholarly publishers – are very different from commercial 

publishers. 

The Needful Intermediary 

The naive belief that publishers are a needless intermediary permeates a great deal of the 

intellectual culture, mostly because so many publishers can be seen to be parasitic freeriders. 

Most truly aren't, I believe – we may be parasites, but it's hard work doing it. The 

value of the dissemination a good publisher can provide is rarely understood, and the 

ways in which nonprofit, academic publishers can assist the pursuit of knowledge have 

been poorly presented. 

Remember the Likins quotation from earlier – "a for-profit's mission is to create as much 

value for its stockholders as possible, within the constraints of society." Commercial 

publishers are by definition trying to make money. But the nonprofit publishing world is 

by definition about something different, even if it's forced to confront the constraints of 

budgets and risk. In Likins's words, the goal is to "create as much value for society as

possible, within the constraints of its money." That's what the National Academies Press 

tries to do for its institution – which itself is trying to create as much value for society as 

possible. We are part service, part business, part entrepreneur, part inventor, and an 

interesting model to explore. 

As the publisher for the National Academies (the National Academy of Sciences, the 

National Academy of Engineering, the Institute of Medicine, and the National Research 

Council), we produce an actual physical entity – a book. Print is far from dead. In fact, it 

is our belief that for at least the next decade, without such an artifact, a National 

Academies publication would be perceived as something akin to a long, very erudite 

memo. We produce something that is a pleasure to read, thereby providing the prestige of 

a real book, an item that can be purchased and possessed, seen in bookstores, promoted in 

the world, and read in an office, a plane, a bathroom, or a tram. 

The Press makes every page of its publications browsable online, and is almost certainly 

the most open book publisher in the world. We had almost 9 million visitors in 2003; 

reading almost 53 million report pages. That dissemination allows the recommendations 

of the reports to be communicated throughout the world to anyone wanting to read them. 

The Press also disseminates and promulgates the works of the National Academies 

through traditional means. A colleague recently sent an e-mail to me and said, "I ran into 

an NAP title at Powell's yesterday. It was in the Agriculture section and had a picture of a 

field on the cover." That small impact – of being found serendipitously while performing 

concrete topical searches – is easy to underestimate, when the goal is to provide value for 

society. In contrast, that impact is meaningless – in fact not desired – for a commercial 

publisher. 

The Press's business model provides an economic incentive at the institutional level for 

"push" marketing – catalogues, direct mail, e-mail marketing, targeted contacts of all 

kinds. Without a business underlying the enterprise, it's hard to justify the expense of, for 

example, space ads in a particular journal. 

The Press also provides an economic brake for political influence from "big swaggerers." 

Without a business underpinning, there is little to prevent the influence of a loud 

institutional voice from directing limited resources to his/her own pet project – like 

translating a Czech bat wing book. Similar waste is just as easy to generate via other "bad 

publishing decisions": bad online Webwork, insistence on "multimedia" or interactivity 

or animation for no communicative reason, poor design of a print component, insistence 

on four-color everywhere, dismal titling, and on and on. 

We provide a business reason for good judgment, which can save huge amounts of 

money. A "print-on-demand" (POD) book may have a fixed unit cost – say $6 per unit – 

regardless of the print run. A unit cost for an offset print run can be as little as $1.50 per 

unit. The advantages of POD are certain, within certain constraints; the financial benefit 

of good judgment (predicting demand sufficiently to decide between traditional offset

and POD) is not encouraged by a POD-only model. Without a business-based motivator, 

routine internal expedience will be the intrinsically rewarded model. 

We also provide a business reason to generate "pull" – to enhance the search value and 

search visibility of National Academies texts within the context of the world's search 

engines. Without professional attention at that level, there would at best be spotty "search 

engine optimization" by those within the institution with the funds and staff to do so. 

There would be no systematic, institution-wide strategy to continuously strengthen the 

online visibility of all Academies reports. 

Finally, we provide a business motivator that can justify underwriting the development of 

publishing enhancements. Since 1999, the NAP has underwritten the development of 

"special collections" software, the development of novel search and information 

discovery projects, and much more. These sorts of tools are justified because, on our 

website, they do a better job of engaging the value of our unique resources to readers. We 

want to promote that value, because any researcher might be the one visitor in two 

hundred who might decide to buy the book. 

The Press is far from perfect, but it's a successful model of alternative modes of 

publishing, responding to the unique needs of its parent institution and authors, and to the 

media requirements of its primary audiences. 

Conclusions 

If there are four things I hope the reader takes away from this article, they are the 

following. First, the depiction of the collapse of a vibrant state of public literacy in 

Prague between 1990 and 1995, in which too-sudden changes in the economics of 

publishing resulted in the loss of cultural and societal habits that encouraged educational 

engagement. Naive revolutionaries are usually the most dangerous. To editorialize, it's 

important to recognize that revolutions can too often end up in dictatorships of one kind 

or another. In this digital revolution, we in the USA are running the risk of ceding (by 

passive acceptance) the rights to the intellectual property of the public, nonprofit, and 

educational sectors to commercial interests, whose metric of value is commercial, not 

societal. 

Next, I hope I made clear the need to acknowledge the limits of volunteerism, and to see 

that some projects require a foundation beyond the passionate involvement of a few 

people; that cost-recovery models can dictate the possibilities. A cost-recovery model can 

enable or inhibit more development, may encourage or discourage dissemination and 

access, or may allow gymnastics on the high wire, rather than simply forcing a static 

balance on the tightrope of survival. 

I hope I made the point for a broader engagement between the many sectors within 

scholarly communication (publishers, scholars, libraries, technologists, editors, 

collaborators), and made a convincing argument that because of our common

fundamental mission, audiences, and funding sources, we should be collaborating more 

with each other, and making accommodations for each other more often. 

Finally, I hope that the underlying theme of publishing as a set of choices, with which 

professional help is useful, was clear. There are ever more potential traps that every 

publishing project of scope and depth must now recognize and elude. Choosing 

appropriate partners – outside institutions, intermediaries, disseminators – can make the 

difference between a project of scholarly/academic/educational significance and a 

publishing novelty. 

Networked publishing will begin to mature in the next decade. I hope that we can work to 

ensure that it matures within a rich community of support and engagement, and that the 

public sector finds ways to add value to society through a robust culture of publishing. 

36. 

The Past, Present, and Future of Digital Libraries 

Howard Besser 

Digital libraries will be critical to future humanities scholarship. Not only will they 

provide access to a host of source materials that humanists need in order to do their work, 

but these libraries will also enable new forms of research that were difficult or impossible 

to undertake before. This chapter gives a history of digital libraries. It pays particular 

attention to how they have thus far failed to incorporate several key elements of 

conventional libraries, and discusses current and future digital library developments that 

are likely to provide these missing elements. Developments of concern to humanists 

(such as preservation, the linking of collections to one another, and standards) are 

discussed in detail. 

Why are Digital Libraries Important to Humanists? 

Historically, libraries have been critical to humanities scholarship. Libraries provide 

access to original works, to correspondence and other commentary that helps 

contextualize those works, and to previous humanities commentaries. 

As we enter the twenty-first century, digital libraries appear to be as critical to humanities 

scholarship as brick-and-mortar libraries were to scholarship in previous centuries. Not 

only do digital libraries provide access to original source material, contextualization, and 

commentaries, but they also provide a set of additional resources and service (many of 

them closely matching humanities trends that emerged in the late twentieth century). 

Digital libraries allow scholars to engage in a host of activities that were difficult or 

impossible to do before. Libraries, archives, and museums have been gathering together 

high-quality digital surrogates of original source material from many different

epositories so that they appear to be a single repository to users (for example, see the 

Online Archive of California ) or Artstor 

(). Researchers can now consult online facsimiles of rare works 

residing in a host of different institutions without having to visit each one. Students now 

have the opportunity to explore facsimiles of rare works and correspondence. Researchers 

who engage in lexical analysis now have the opportunity to count word/phrase 

occurrences or do syntactical analysis not just on a single work, but across a whole body 

of works. Digital libraries permit instructors and repository managers to reflect multiple 

interpretations of works, authors, or ideas alongside each other – a realization of 

decentered critical authority, one of the tenets of postmodernism. 

But it would be a mistake to see digital libraries as primarily providing ways to access 

material more quickly or more easily, without having to visit a repository across the 

country. Though the promise of digital technology in almost any field has been to let one 

do the same things one did before but better and faster, the more fundamental result has 

often been the capability of doing entirely new things. It is very possible that digital 

libraries will enable future humanities scholars to engage in new activities that we haven't 

yet envisioned. 

What is a Library? 

Traditionally, libraries have been more than just collections. They have components 

(including service to a clientele, stewardship over a collection, sustainability, and the 

ability to find material that exists outside that collection) and they uphold ethical 

traditions (including free speech, privacy, and equal access). In the last decade of the 

twentieth century, both technological changes and reduced funding for the public sector 

led to significant changes in libraries. For the first time in history it became possible to 

divorce the physical aspects of a library from the (digital) access and services that a 

library provides. This has led to much discussion of the past and possible future role of 

libraries, and some speculation as to whether they have a future. In her discussion of why 

conventional libraries will not disappear simply because we develop online collections, 

Christine Borgman states that the conventional library's role is to "select, collect, 

organize, preserve, conserve, and provide access to information in many media, to many 

communities of users." I have argued, elsewhere, that the four core characteristics of a 

public library are: that it is a physical place, that is, a focus spot for continuous 

educational development, that it has a mission to serve the underserved, and that it is a 

guarantor of public access to information (Besser 1998). 

Almost all conventional libraries have a strong service component. All but the smallest 

libraries tend to have a substantial "public service" unit. Library schools teach about 

service (from "public service" courses to "reference interviews"). And the public in 

general regard librarians as helpful people who can meet their information needs. Many 

libraries also deliver information to multiple clienteles. They are very good at using the 

same collection to serve many different groups of users, each group incorporating 

different modalities of learning and interacting, different levels of knowledge of a certain 

subject, etc. Public libraries serve people of all ages and professions, from those barely

able to read, to high school students, to college students, to professors, to blue-collar 

workers. Academic libraries serve undergraduates who may know very little in a 

particular field, faculty who may be specialists in that field, and non-native English 

speakers who may understand detailed concepts in a particular domain, but have 

difficulty grasping the language in which those concepts are expressed. 

Most libraries also incorporate the component of stewardship over a collection. For some 

libraries, this is primarily a matter of reshelving and circulation control. But for most 

libraries, this includes a serious preservation function over at least a portion of their 

collection. For research libraries and special collections, preservation is a significant 

portion of their core responsibilities, but even school, public, and special libraries are 

usually responsible for maintaining a core collection of local records and works over long 

periods of time. 

Libraries are organizations that last for long periods of time. Though occasionally a 

library does "go out of business", in general, libraries are social entities that have a great 

deal of stability. Though services may occasionally change slightly, people rely on their 

libraries to provide a sustainable set of services. And when services do change, there is 

usually a lengthy period when input is solicited from those who might be affected by 

those changes. 

Another key component of libraries is that each library offers the service of providing 

information that is not physically housed within that library. Libraries see themselves as 

part of a networked world of libraries that work together to deliver information to an 

individual (who may deal directly only with his or her own library). Tools such as union 

catalogues and services such as inter-library loan have produced a sort of interoperable 

library network that was used to search for and deliver material from afar long before the 

advent of the World Wide Web. 

Libraries also have strong ethical traditions. These include fervent protection of readers' 

privacy, equal access to information, diversity of information, serving the underserved, 

etc. (see resolutions of the American Library Association – American Library 

Association 1995). Librarians also serve as public guardians over information, advocating 

for these ethical values. 

The library tradition of privacy protection is very strong. Librarians have risked serving 

jail time rather than turn over whole sets of patron borrowing records. Libraries in the 

USA have even designed their circulation systems to only save aggregate borrowing 

statistics; they do not save individual statistics that could subsequently be data-mined to 

determine what an individual had borrowed. 

Librarians believe strongly in equal access to information. Librarians traditionally see 

themselves as providing information to those who cannot afford to pay for that 

information on the open market. And the American Library Association even mounted a 

court challenge to the Communications Decency Act because it prevented library users 

from accessing information that they could access from venues outside the library.

Librarians have been in the forefront of the struggle against the privatizing of US 

government information on the grounds that those steps would limit the access of people 

who could not afford to pay for it. 

Librarians also work to ensure diversity of information. Libraries purposely collect 

material from a wide variety of perspectives. Collection development policies often stress 

collection diversity. And librarians pride themselves on being able to offer patrons a rich 

and diverse set of information. 

Librarians are key public advocates for these ethical values. As guardians of information, 

they try to make sure that its richness, context, and value do not get lost. 

As more and more information is available in digital form, a common misperception is 

that a large body of online materials constitutes a "digital library." But a library is much 

more than an online collection of materials. Libraries (either digital or brick-and-mortar) 

have both services and ethical traditions that are a critical part of the functions they serve. 

The digital collections we build will not truly be digital libraries until they incorporate a 

significant number of these services and ethical traditions. 

Brief Digital Library History 

The first major acknowledgment of the importance of digital libraries came in a 1994 

announcement that $24.4 million of US federal funds would be dispersed among six 

universities for "digital library" research (NSF 1994). This funding came through a joint 

initiative of the National Science Foundation (NSF), the Department of Defense 

Advanced Research Projects Agency (ARPA), and the National Aeronautics and Space 

Administration (NASA). The projects were at Carnegie Mellon University, the University 

of California-Berkeley, the University of Michigan, the University of Illinois, the 

University of California-Santa Barbara, and Stanford University. 

These six well-funded projects helped set in motion the popular definition of a "digital 

library." These projects were computer science experiments, primarily in the areas of 

architecture and information retrieval. According to an editorial in D-Lib Magazine, 

"Rightly or wrongly, the DLI-1 grants were frequently criticized as exercises in pure 

research, with few practical applications" (Hirtle 1999). 

Though these projects were exciting attempts to experiment with digital collections, in no 

sense of the word did they resemble libraries. They had little or no service components, 

no custodianship over collections, no sustainability, no base of users, and no ethical 

traditions. We will call this the "experimental" stage of digital library development (see 

Table 36.1). Because efforts during this experimental stage were the first to receive such 

widespread acknowledgment under the term "digital library", they established a popular 

understanding of that term that has persisted for many years.

36.1 Table Stages of digital library development 

Stage Date Sponsor What 

I 

Experimental 

1994 NSF/ARPA/NASA 

II Developing 1998/99 NSF/ARPA/NASA, 

DLF/CLIR 

III Mature ? 

Funded through normal 

channels? 

Experiments on collections of digital 

materials 

Begin to consider custodianship, 

sustainability, user communities 

Real sustainable interoperable digital 

libraries 

By 1996, social scientists who had previously worked with conventional libraries began 

trying to broaden the term "digital libraries" (Bishop and Star 1996; Borgman et al. 

1996). But the real breakthrough came in late 1998 when the US federal government 

issued their highly funded DL-2 awards (Griffin 1999) to projects that contained some 

elements of traditional library service, such as custodianship, sustainability, and 

relationship to a community of users. Also around that time, administrators of 

conventional libraries began building serious digital components. 

As librarians and social scientists became more involved in these digital projects, efforts 

moved away from computer science experiments into projects that were more 

operational. We shall call this the "developing" stage of digital libraries. By the late 

1990s, particularly under the influence of the US Digital Library Federation, projects 

began to address traditional library components such as stewardship over a collection and 

interoperability between collections. But even though digital library developers have 

made great progress on issues such as real interoperability and digital preservation, these 

are far from being solved in a robust operational environment. In order to enter the 

"mature" stage where we can really call these new entities "digital libraries", they will 

need to make much more progress in moving conventional library components, such as 

sustainability and interoperability, into the digital realm. And developers need to begin to 

seriously address how they can move library ethical traditions (such as free speech, 

privacy, and equal access) into the digital realm as well. The remainder of this chapter 

examines important efforts to move us in those directions. 

Moving to a More User-centered Architecture 

Both the early computer science experiments in digital libraries and the earlier initial 

efforts to build online public access catalogues (OPACs) followed a model similar to that 

in Figure 36.1. Under this model, a user needed to interact with each digital repository 

independently, to learn the syntax supported by each digital repository, and to have 

installed on his or her own computer the applications software needed to view the types 

of digital objects supported by each digital repository. 

So, in order for a user to search Repository A, s/he would need to first adjust to 

Repository As specialized user interface, then learn the search syntax supported by this

epository. (For example, NOTIS-based OPACs required search syntax like A=Besser, 

Howard, while Inovative-based OPACs required search syntax like FIND PN Besser, 

Howard.) Once the search was completed, s/he could retrieve the appropriate digital 

objects, but would not necessarily be able to view them. Each repository would only 

support a limited number of encoding formats, and would require that the user have 

specific software installed on their personal computer (such as viewers for Microsoft 

Word 98, SGML, Adobe Acrobat, TIFF, PNG, JPEG, or specialized software distributed 

by that repository) in order to view the digital object. Thus users might search and find 

relevant works, but not be able to view them. 

The user would then have to repeat this process with Repository B, C, D, etc., and each of 

these repositories might have required a different syntax and different set of viewers. 

Once the user had searched several different repositories, he or she still could not 

examine all retrieved objects together. There was no way of merging sets. And because 

different repositories supported different viewing software, any attempt to examine 

objects from several repositories would likely require going back and forth between 

several different applications software used for display. 

Obviously the model in Figure 36.1 was not very user-friendly. Users don't want to learn 

several search syntaxes, they don't want to install a variety of viewing applications, and 

they want to make a single query that accesses a variety of different repositories. Users 

want to access an interoperable information world, where a set of separate repositories 

looks to them like a single information portal. A more user-friendly model is outlined in 

Figure 36.2. Under this model, a user makes a single query that propagates across 

multiple repositories. The user must only learn one search syntax. 

The user doesn't need to have a large number of software applications installed for 

viewing, and retrieved sets of digital objects may be looked at together on the user's 

workstation. The model in Figure 36.2 envisions a world of interoperable digital 

repositories, and is a model we need to strive for. 

Over the years, developers have made some significant progress towards the Figure 36.2 

model, particularly in the area of OPACs. Web browsers have provided a common "lookand-feel" 

between different repository user interfaces. The Z39–50 protocols have 

allowed users to employ a single, familiar search syntax, even when the repository's 

native search syntax appears foreign. Z39–50 has also promised to let user queries 

propagate to different repositories. But when one leaves the world of OPACs and enters 

the world of digital repositories, much work still needs to be done to achieve real 

interoperability. Most of this work involves creation and adoption of a wide variety of 

standards: from standards for the various types of metadata (administrative, structural, 

identification, longevity), to ways of making those metadata visible to external systems 

(harvesting), to common architectures that will support interoperability (open archives).

General processes and stages of technological development 

The automation of any type of conventional process often follows a series of pragmatic 

steps as well as a series of conceptual stages. 

Pragmatic implementation steps usually begin by using technology to experiment with 

new methods of performing some function, followed by building operational systems, 

followed by building interoperable operational systems. And at the later stages of this, 

developers begin trying to make these systems useful for users. We have seen this pattern 

(experimental systems to operational systems to interoperable systems to useful systems) 

repeated in the development of OPACs, Indexing and Abstracting services, and image 

retrieval. The automation of each of these has begun with experiments, followed by 

implementations that envisioned closed operational systems (with known bodies of users 

who need to learn particular user interfaces and syntaxes to interact with the system), 

followed by implementations that allowed the user to more easily interact with multiple 

systems (and sometimes to even search across various systems). Today's "digital 

libraries" are not much beyond the early experimental stage, and need a lot more work to 

make them truly interoperable and user-centered. 

The conceptual steps typically include first trying to replicate core activities that 

functioned in the analogue environment, then attempting to replicate some (but not all) of 

the non-core analogue functions, then (after using the systems for some time) discovering 

and implementing new functions that did not exist within the previous analogue 

environment. Only with this final step do we actually realize the new functional 

environment enabled by the new technology. So, for example, word processors were 

initially built as typewriters with storage mechanisms, but over time grew to incorporate 

functions such as spell-checking and revision-tracking, and eventually enabled very 

different functions (such as desktop publishing). Our early efforts at creating MARC 

records began as ways to automate the production of catalogue cards, then moved to the 

creation of bibliographic utilities and their union catalogues, then to OPACs. 

Functionally, our OPACs began as mere replicas of card catalogues, then added Boolean 

searching, then title-word searching capabilities, and now are poised to allow users to 

propagate distributed searches across a series of OPACs. Today's digital collections are 

not much past the initial stage where we are replicating the collections of content and 

cataloguing that existed in analogue form, and just beginning to add minor functions. In 

the future we can expect our digital libraries to incorporate a variety of functions that 

employ the new technological environments in ways we can hardly imagine today. 

The Importance of Standards 

In moving from dispersed digital collections to interoperable digital libraries, the most 

important activity developers need to focus on is standards. This includes standards and 

protocols for open archives and metadata harvesting. But most important is the wide 

variety of metadata standards needed. The most extensive metadata activities have 

focused on discovery metadata (such as the Dublin Core), but metadata also include a 

wide variety of other functions: structural metadata are used for turning the pages of a

digital book, administrative metadata are used to ensure that all the individual pages of a 

digital book are kept together over time, computer-based image retrieval systems employ 

metadata to help users search for similar colors, shapes, and textures, etc. Developers 

need to widely employ descriptive metadata for consistent description, discovery 

metadata for finding works, administrative metadata for viewing and maintaining works, 

structural metadata for navigation through an individual work, identification metadata to 

determine that one has accessed the proper version of a work, and terms and conditions 

metadata for compliance with use constraints. 

Having consensus over metadata and other standards is important for a variety of reasons. 

Administrative and longevity metadata are needed to manage digital files over time, to 

make sure all the necessary files are kept together, and to help view these files when 

today's application software becomes unusable. Because of the mutability of digital 

works, developers need standards to ensure the veracity of a work, and to help assure 

users that a particular work has not been altered, and is indeed the version of the work 

that it purports to be. And developers need a variety of types of metadata and standards to 

allow various digital collections to interoperate, and to help users feel that they can 

search across groups of collections. One side benefit of reaching consensus over metadata 

that will be recorded in a consistent manner is that vendors will have an economic 

incentive to re-tool applications to incorporate this metadata (because they can spread 

their costs over a wide variety of institutions who will want to employ these standards). 

The various metadata types 

Libraries have had agreements on metadata standards for many decades. The Anglo- 

American Cataloguing Rules defined a set of descriptive metadata for bibliographic (and 

later, other) works, and the MARC format gave us a syntax for transporting those 

bibliographic records. Likewise, Library of Congress Subject Headings and Sears Subject 

Headings have for many years provided us with discovery metadata to help users find 

relevant material. In the last quarter of the twentieth century, other types of discovery 

metadata emerged to serve specialized fields, including the Art and Architecture 

Thesaurus (AAT) and the Medical Subject Headings (MeSH). Though both AAT and 

MeSH envisioned use in an online environment, both were developed in an era when 

indexing and cataloguing records might sit on a computer, but the works they referred to 

would not. And both were developed at a point in time when the merging of records from 

these specialized fields with records for more general works was unlikely to take place on 

a widespread basis. 

The rapid acceptance of the World Wide Web led a number of us to consider how one 

might allow users to search across a variety of online records and resources, particularly 

when some of those resources received extensive cataloguing, while others received little 

or none. This led to the March 1995 meeting that defined the Dublin Core as a type of 

discovery metadata that would allow users to search across a wide variety of resources 

including both highly catalogued (often legacy) material, and material (much of it new 

and in electronic form) that was assigned only a minimal amount of metadata. We 

envisioned the Dublin Core as a kind of unifying set of metadata that would permit

discovery across all types of digital records and resources. Library cataloguing records or 

museum collection management records could be "dumbed down" to look like Dublin 

Core (DC) records, while DC records for resources like an individual's research paper 

might either be easy enough for the individual to create, or might even be automatically 

generated by the individual's word processor. The not-yet-realized promise of the Dublin 

Core (see next section on Harvesting) was to provide discovery-level interoperability 

across all types of online indexes and resources, from the highly-catalogued OPACs to 

the websites and webpages of individuals and organizations. And because the Dublin 

Core has been in existence for approximately seven years (and has recently been 

designated as NISO Standard Z39–85), it is more developed and better known than any 

of the other types of metadata created for electronic resources. 

Though the Dublin Core was developed as a form of digital metadata to be applied to 

works in both digital and non-digital form, a variety of other metadata types have more 

recently been developed specifically for collections of works in digital form. Below we 

will briefly discuss efforts to define structural metadata, administrative metadata, 

identification metadata (particularly for images), and longevity metadata. All these 

metadata types are critical for moving from a set of independent digital collections to real 

interoperable digital libraries. Hence they all incorporate functions likely to lead either to 

increased interoperability, or to the fuller and more robust services that characterize a 

library rather than a collection. 

Structural metadata recognize that, for many works in digital form, it is not enough 

merely to display the work; users may need to navigate through the work. Structural 

metadata recognize that users expect certain "behaviors" from a work. For example, 

imagine a book that is composed of hundreds of digital files, each one the scan of a single 

book page. Structural metadata are needed for users to perform the normal behaviors they 

might expect from a book. Users will expect to be able to view the table of contents, then 

jump to a particular chapter. As they read through that chapter, they will expect to turn 

the page, and occasionally go back to re-read the previous page. When they come to a 

citation, they will want to jump to the bibliography to read the citation, then jump back. 

And when they come to a footnote marker, they may want to jump to where they can read 

the footnote contents, then jump back. These are all just normal behaviors we expect 

from any type of book, but these behaviors all require structural metadata. Without them, 

the book would just be a series of individual scanned pages, and users would have a great 

deal of difficulty trying to even put the pages in the correct order, let alone read the book. 

Structural metadata have a role in any kind of material that would benefit from internal 

navigation (including diaries and journals). 

Administrative metadata maintain the information necessary in order to keep a digital 

work accessible over time. In the case of a digitized book, the administrative metadata 

would note all the individual files needed to assemble the book, where the files were 

located, and what file formats and applications software would be necessary in order to 

view the book or its individual pages. Administrative metadata become particularly 

important when moving files to a new server, or engaging in activities related to digital 

longevity such as refreshing or migration.

Instead of employing open standards for structural and administrative metadata, many 

individuals and organizations choose to encode their documents within commercial 

products such as Adobe Acrobat. Although this is highly convenient (particularly given 

the proliferation of Acrobat readers), it could be a dangerous practice for libraries and 

similar repositories. Commercial products are proprietary, and focus on immediate 

convenience rather than long-term access. Hence, there is no guarantee of continued 

compatibility or future access to works encoded in earlier versions of commercial 

software. In order to cope with long-term preservation and access issues, as well as to 

provide a higher level of structural functionality, in 1997 a group of US libraries began 

the Making of America II Project to define structural and administrative metadata 

standards for library special collection material (Hurley et al. 1999). These standards 

were further refined within the Technology Architecture and Standards Committee of the 

California Digital Library (CDL 200la), and have since been renamed the Metadata 

Encoding and Transmission Standards (METS). METS was adopted by the US Digital 

Library Federation and is now maintained by the US Library of Congress 

(). The Research Libraries Group recently 

announced plans to lead the METS development effort. 

Identification metadata attempt to address the proliferation of different versions and 

editions of digital works. In the print world, the publishing cycle usually both enforced an 

editorial process and created time lags between the issuance of variant works. But for a 

highly mutable digital work, those processes and time lags are often eliminated, and 

variants of the work are created quickly and with very little thought about their impact on 

what we used to call bibliographic control. In addition, the networked digital environment 

itself leads information distributors to provide a variety of different forms of a given 

work (HTML, Postscript, Acrobat, XML, and Microsoft Word forms of documents to 

support different user capabilities and needs; thumbnail, medium-sized, and large images 

to support image browsing, viewing, and study). 

To illustrate the identification metadata problem, let us turn to Figure 36.3, an illustration 

of variant forms of images. The original object (a sheep) is shown in the upper left 

corner. There are four photographs of the original object, taken from three different 

angles. Two of the photographs (A and D) are taken from the same angle, but photograph 

D has captured a fly on the side of the sheep. The images to the right of photograph D are 

variant forms of that photograph after image processing was done to remove the fly from 

the side of the sheep, while the images below photograph D show variant forms that 

include the fly. Spread throughout the figure are variant forms including different-sized 

images (thumbnail to high-resolution), different compression ratios (including both lossy 

and lossless), and different file encoding formats (PICT, TIFF, JFIF). Certain users may 

only want particular resolutions or compression ratios (e.g., a serious researcher may 

require an uncompressed high-resolution image). All the images illustrated in this figure 

share a base set of metadata that refer to the initial object of origin (the sheep), and 

adapting Leazer's idea of Bibliographic Families (Leazer and Smiraglia 1999), we can 

say that all the images in this illustration form an "Image Family" which shares a 

common set of metadata. Each image instantiation in the family also inherits metadata 

from its parents, and knowledge about that inheritance can often be critical to someone

viewing a particular instantiation (for example, someone using one of the lower-right 

corner instantiations to study wool characteristics should be able to ascertain that image 

processing was done on a parent or grandparent of this image [to remove the fly], and 

that this image processing might affect the matting of the wool). Thus, it is critical that 

any image inherits important metadata from its lineage, or that systems at least provide 

ways that a researcher can trace upwards in lineage to discover metadata that might affect 

their use of the work. A first step in this direction is in the US National Information 

Standards Organization efforts at creating a Technical Imaging Metadata Standard that 

incorporates "change history" and "source data" (NISO 2002). But our community has 

much more work to do in order to provide the type of identification of variant forms that 

users (particularly researchers) have come to expect from libraries of analogue materials. 

And we still need to come to grips with the problem of how to preserve dynamic 

documents – documents that are essentially alive, and changing on a daily basis. 

As digital library developers construct large collections of material in digital form, we 

need to consider how digital works will provoke changes in long-standing practices that 

have grown up around analogue works. Elsewhere this author has outlined how electronic 

art will likely provoke changes in conservation and preservation practices (Besser 200la) 

and how the growing body of moving image material in digital form is beginning to 

reshape the role of film archives and archivists (Besser 200Ib). But at a very pragmatic 

level, all repositories of digital works need to worry about the persistence of those works 

over time. Longevity metadata are necessary in order to keep digital material over long 

periods of time. While saving bits may be fairly straightforward, saving digital works is 

not. Digital works are very fragile, and pro-active steps need to be taken in order to make 

sure that they persist over time (for more on this subject, see the Digital Longevity 

website maintained by this author at ). 

Elsewhere, I have outlined five key factors that pose digital longevity challenges (the 

Viewing Problem, the Scrambling Problem, the Interrelation Problem, the Custodial 

Problem, and the Translation Problem), and have suggested that community consensus 

over metadata can be a key factor in helping digital works persist over time (Besser 

2000). Recently, the major US bibliographic utilities have begun serious efforts to reach 

consensus on preservation metadata for digital works (OCLC/RLG 2001a, 2001b). 

Widespread adoption of this type of standard will make the challenge of digital 

persistence much more tractable. And late in 2001 the Library of Congress, with the help 

of the Council on Library and Information Resources, began a planning process for a 

National Digital Information Infrastructure and Preservation Program (see 

). Widespread support for the emerging 

metadata standards mentioned here will greatly improve interoperability between 

collections and the sustainability of those collections over time. This will help us move 

away from isolated experiments with digital collections towards sustainable digital 

libraries.

36.3 Figure Image families 

Metadata philosophies and harvesting: Warwick vs. MARC 

Though agreement on a variety of metadata standards is a necessary prerequisite for 

interoperable digital collections, implementation of interoperability also requires a set of 

architectures and a common approach to making that metadata available to other 

collections, middleware, and end users. In this section we will discuss two philosophical 

approaches to metadata, as well as methods for sharing metadata with applications and 

individuals outside the designer's home collection. 

Libraries have traditionally employed the MARC/AACR2 philosophical approach to 

metadata. This approach employs a single overarching schema to cover all types of works

and all groups of users. As new types of works arise, new fields are added to the MARC/ 

AACR2 framework, or rules for existing fields are changed to accommodate these new 

works. And as communities emerge with new metadata needs, these are also incorporated 

into the existing schema. The MARC/AACR2 philosophy maintains that one big schema 

should serve all user needs for all types of works. Critics of this approach point out that 

the schema has become so overly complex that only highly trained specialists (library 

cataloguers) are able to assign metadata using it, and that the system is too slow to adapt 

to emerging types of works. They also claim that groups of users often have sets of 

metadata needs that the controllers of MARC/AACR2 are unwilling to accommodate. 

In recent years, a rival philosophy has emerged from within the Dublin Core community. 

This philosophy, based upon the Warwick Framework, relies upon interlocking 

containers and packages of metadata, each maintained by a particular community. 

According to this philosophy, each community can support the packages of metadata it 

needs for its own particular uses, while still interoperating with the metadata packages 

from other communities. Under this philosophy, the Dublin Core serves as a unifying set 

of metadata to allow discovery across all communities. And even within the Dublin Core 

(DC), certain communities can employ qualifiers that meet their own detailed needs, 

while still providing useful metadata to other communities. (For example, the library 

community could use qualifiers to reflect the nuances of differences between main title, 

alternate title, transliterated title, and translated title, while other communities could find 

any of these as part of a search under unqualified title.) This philosophy supports 

metadata packages that are modular, overlapping, extensible, and community-based. 

Advocates believe that they will aid commonality between communities while still 

providing full functionality within each community. This approach is designed for a 

networked set of communities to interrelate to one another. 

No matter which philosophical approach one follows, any collection faces the pragmatic 

issue of how to make their metadata available to other collections and external searching 

software. The traditional library model was to export MARC records to a bibliographic 

utility (like OCLC or RLIN) and to have all external users search through that utility. 

While this works fine for MARC-based records, increasingly users want to search across 

a much wider base of information from a world not circumscribed by bibliographic 

records (such as web pages and sites, PDF documents, images, databases, etc.). 

Therefore, most digital collections are beginning to consider how to export reduced 

records into a space where they can be picked up by Internet search engines. For records 

following the MARC/AACR2 approach, this means extracting simple records (probably 

in DC format) from complex MARC records, and exporting these. For both the Warwick 

and the MARC/AACR2 approaches, this means developing methods for metadata 

harvesting that allow the appropriate exported records to be found by Internet search 

engines. A number of projects are currently under way to test metadata harvesting. 

Best practices 

Along with standards and architectures, community agreement on best practices is 

another important ingredient in helping make collections of digital materials more

interoperable and sustainable. Best practices ensure that content and metadata from 

different collections will meet minimum standards for preservation purposes, and that 

users can expect a baseline quality level. 

A key best practice principle is that any digital project needs to consider users, potential 

users, uses, and actual characteristics of the collections (Besser and Trant 1995). This 

means that decision-making both on digital conversion of analogue material and on 

metadata assignment needs to be carefully planned at the start of a digital project. The 

pioneering best practices for digital conversion developed by the Technology 

Architecture and Standards Committee of the California Digital Library (CDL 2001b) 

introduced important concepts designed to aid in the longevity and sustainability of 

digital collections. These concepts included differentiating between masters and 

derivatives, inclusion of greyscale targets and rulers in the scan, using objective 

measurements to determine scanner settings (rather than matching the image on a nearby 

monitor to the original object), storing in common formats, and avoiding compression 

(particularly lossy compression). This document also suggested that collections strive to 

capture as much metadata as is reasonably possible (including metadata about the 

scanning process itself). The relationship of best practices for scanning to the longevity of 

digital works is more fully explained in the Digital Library Federation's draft benchmarks 

for digital reproductions (DLF 2001). 

The Making of America II Project (Hurley et al. 1999) introduced the idea that metadata 

could begin in fairly raw form, and, over time, move toward being seared and eventually 

cooked. This notion of incrementally upgrading metadata appears to have assuaged the 

fears of some groups that metadata schemes like METS were too overblown and 

complicated for them to undertake. In effect, this notion appears to have increased the 

adoption level of more complicated metadata schemes. 

Other standards issues 

A number of other standards issues need to be addressed in order to bring interoperability 

and other conventional library services to our emerging digital libraries. These include 

open archives, metadata harvesting, persistent identification, helping users find the 

appropriate copy, and user authentication. 

Metadata stored in local systems are often not viewable by external applications or users 

that may be trying to discover local resources. This seriously inhibits interoperability, and 

the ability of a user to search multiple collections. The Open Archives Initiative 

() is tackling this problem by developing and testing 

interoperability protocols that will allow applications to harvest metadata, even those 

residing in deep archives. The success of a project like this is critical to providing users 

with the type of access outlined in Figure 36.2. In addition, this project's focus on e-print 

archives should provide users with a diverse body of content free of onerous constraints. 

Persistent naming is still an important issue for building real digital libraries. Though the 

World Wide Web has brought us increased access to works, Web architecture has

violated traditional library practices of providing relative location information for a work 

by instead providing a precise location address. Failures in the Web's precise locationaddressing 

system (the URL) produce the most common error message that Web users 

encounter: 404 – File Not Found. Most of these error messages result from normal 

maintenance of a website (renaming higher-order folders or directories, re-organizing file 

locations). Librarians would never consider telling a user that to find the book they're 

seeking they must go to the third tier of the stacks, in the eighth row, the fifth bookcase, 

the third shelf, and grab the seventh book from the left; they know that once someone 

removes the third book from the left, the entire system of locating will break down. Yet, 

this is the type of system that URLs are based upon. In recent years there has been much 

work done on indirect naming (in the form of PURLS, URNs, and handles). But to 

replicate the power that libraries have developed, we need truly persistent naming. This 

means more than just the indication of a location for a particular work. Sophisticated 

persistent naming would include the ability to designate a work by its name, and to 

distinguish between various instantiations of that work and their physical locations. Just 

as conventional libraries are able to handle versions and editions and direct users to 

particular copies of these, our digital libraries will need to use identification metadata to 

direct users to an appropriate instantiation of the work they are seeking. 

Ever since the advent of indexing and abstracting services, conventional libraries have 

had to face the problem of answering a user's query with a list of works, some of which 

may not be readily available. Conventional libraries have striven to educate users that 

some sources are not physically present in the library, and they have also developed both 

interlibrary loan and document delivery services to help get material to users in a timely 

fashion. But the recent proliferation of licensed full-text electronic resources has greatly 

complicated this problem. For a variety of reasons, it may be very difficult to match a 

user with an appropriate document they are licensed to use: certain users may be covered 

by a given license while others are not; the same document may be provided by several 

aggregators under different licenses; much of the online content is physically stored by 

the licensor (content provider) rather than by the licensee (library). Recent 

standardization efforts have begun to address part of this problem; the US National 

Information Standards Organization has formed the OpenURL Standard Committee AX 

() to allow a query to carry context-sensitive 

information. This will help a library authenticate their licensees to a remote content site. 

But there are still many more problems to solve in getting users appropriate copies 

(particularly when content licenses are complex and overlapping). 

Still another critically important standards and protocols area that is under development is 

that of authentication of users. With more and more licensed content being physically 

stored by content providers, those providers want assurances that users accessing the 

content are indeed covered by valid licenses to do so. Yet conventional methods of user 

authentication (such as password or IP addressing) would allow content providers to track 

what an individual reads, and develop complex profiles of user habits. Legal scholars 

have warned of the dangers this poses (Cohen 1996), and it flies in the face of the 

important library ethical tradition of privacy. Work has begun on a project that lets an 

institution authenticate users to a resource provider without revealing individual

identities. It still remains to be seen whether the Shibboleth project 

() will be acceptable to resource providers, 

yet still provide the privacy and anonymity that libraries have traditionally assured users. 

Success becomes more questionable in the wake of the September 11, 2001, US building 

destructions, as the US federal government has increased the pressure to eliminate 

anonymous library access (ALA et al. 2001). 

The Next Stage: Moving from Isolated Digital 

Collections to Interoperable Digital Libraries 

Conventional libraries have both functional components and ethical traditions. The digital 

collections currently under construction will not truly be "digital libraries" until they 

incorporate a significant number of the components of conventional libraries, and adhere 

to many of the important ethical traditions and values of libraries. And though our digital 

collections have made significant progress in these areas in the past seven years, they still 

have a long way to go. 

For the component of interoperability, projects such as open archives, metadata 

harvesting, and structural and administrative metadata hold great promise. For the 

component of stewardship over collections, digital preservation projects have finally 

begun, but it will be some time before we will be able to say with confidence that we can 

preserve the portion of our culture that is in digital form. Additionally, developers have 

only recently begun to grapple with the issue of economic sustainability for digital 

libraries (CLIR 2001). For other components such as service to clientele, they have 

barely scratched the surface in one important area where conventional libraries perform 

very well – delivering information to different groups of users (by age level, knowledge 

base, particular need, etc.) in ways appropriate to each group. The California Digital 

Library and the UCLA/ Pacific Bell Initiative for 21st Century Literacies have recently 

completed a project to explore this problem 

(). 

Those constructing digital collections have spent less energy trying to build library 

ethical traditions into our systems, and in many cases have relied on those outside the 

digital library community (such as the American Library Association filing lawsuits on 

privacy and free speech, or the Internet Engineering Task Force developing protocols to 

preserve privacy) to work on upholding library ethical traditions such as free speech, 

privacy, and equal access. But as Lawrence Lessig has made clear, the choices we make 

in the architecture and design of our systems will limit the social choices we can make 

around use of those systems in the future (Lessig 1999). For example, some of our online 

public-access circulation systems purposely saved only aggregate user data so that no one 

in the future could attempt to track individual reading habits. While recent projects such 

as Shibboleth are trying to design library ethical traditions into the technological 

infrastructure, the digital libraries we're building still have not addressed many of our 

important library ethical traditions.

As designers build digital collections they will also need to uphold library ethical 

traditions of equal access and diversity of information. Both of these are threatened by 

the commercialization of intellectual property. As we see the increased commodification 

of information and consolidation of the content industry into fewer and fewer hands, less 

and less creative work enters the public domain and more requires payment to view 

(Besser 2002a). Commodification and consolidation also bring with them a concentration 

on "best-seller" works and a limiting of diversity of works (Besser 1995, 1998). The 

builders of digital collections need to go beyond content that is popular and become 

aggressive about collecting content that reflects wide diversity; instead of using the nowfamiliar 

opportunistic approach to converting content to digital form, they will need to 

develop carefully planned digital collection development policies. Developers will find 

that they need to closely collaborate with others both to leverage resources, and to ensure 

that the efforts of different organizations together look like virtual digital libraries to our 

users. Developers and scholars also need to be involved in these efforts, to ensure that a 

broad range of content eventually leaves the marketplace and enters the public domain. 

We need to involve ourselves in struggles such as the American Library Association's 

current effort to build a coalition to protect the "information commons" in cyberspace. 

Digital library developers also will find that they need to maintain the role of the library 

as guardian over individuals' rights to access a rich variety of information, and to see that 

information within its context. They will need to continue to be vigilant about making 

sure that other forms of "equal access to information" extend to the new digital world. 

They would also do well to extend the "library bill of rights" into cyberspace, and they 

will find themselves having to struggle to keep the digital world from increasing the 

distance between "haves" and "have-nots." 

Finally, in the move towards constructing digital libraries, we need to remember that 

libraries are not merely collections of works. They have both services and ethical 

traditions and values that are a critical part of their functions. Libraries interoperate with 

each other to serve the information needs of a variety of different user groups today, and 

expect to sustain themselves and their collections so that they can serve users 100 years 

from now. They defend their users' rights to access content, and to do so with some 

degree of privacy or anonymity. The digital collections being built will not truly be 

digital libraries until they incorporate a significant number of these services and ethical 

traditions. 


Extensive portions of this chapter were previously published in the online journal First 

Monday as "The Next Stage: Moving from Isolated Digital Collections to Interoperable 

Digital Libraries" (Besser 2002c) and in the Victorian Association for Library 

Automation's E-Volving Information Futures 2002 conference proceedings (Besser 

2002a). This chapter also involves a synthesis of a number of different talks and 

workshops the author delivered between 1996 and 2002, and he wishes to thank the many 

individuals who offered critical comments or encouragement after those presentations.

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36.1 Figure "Traditional" digital collection model

36.2 Figure 

37. 

Preservation 

Abby Smith 

What is Preservation and Why Does it Matter? 

The purpose of preserving cultural and intellectual resources is to make their use possible 

at some unknown future time. A complete and reliable record of the past is important for 

many reasons, not the least of which is to provide an audit trail of the actions, thoughts, 

deeds, and misdeeds of those who have gone before us. For the humanities – a field of 

open-ended inquiry into the nature of humankind and especially of the culture it creates – 

access to the recorded information and knowledge of the past is absolutely crucial, as 

both its many subjects of inquiry and its methodologies rely heavily on retrospective as 

well as on current resources. Preservation is a uniquely important public good that 

underpins the health and well-being of humanistic research and teaching. 

Preservation is also vitally important for the growth of an intellectual field and the 

professional development of its practitioners. Advances in a field require that there be 

ease of communication between its practitioners and that the barriers to research and 

publishing be as low as possible within a system that values peer review and widespread 

sharing and vetting of ideas. Digital technologies have radically lowered the barriers of 

communication between colleagues, and between teachers and students. Before e-mail, it 

was not easy to keep current with one's colleagues in distant locations; and before 

listservs and search engines it was hard to learn about the work others were doing or to 

hunt down interesting leads in fields related to one's own. Those who aspire to make a 

mark in the humanities may be attracted to new technologies to advance their research 

agenda, but those who also aspire to make a career in the humanities now feel hampered 

by the barriers to electronic publishing, peer review, and reward for work in advanced 

humanities computing. The common perception that digital creations are not permanent is

among the chief obstacles to the widespread adoption of digital publishing, and few 

scholars are rewarded and promoted for their work in this area. 

In research-oriented institutions such as libraries, archives, and historical societies, 

primary and secondary sources should be maintained in a state that allows – if not 

encourages – use, and therefore the concept of "fitness for use" is the primary principle 

that guides preservation decisions, actions, and investments. (This is in contrast to 

museums, which seldom loan objects to patrons or make them available for people to 

touch and manipulate.) Of course, fitness for use also entails describing or cataloguing an 

item, ensuring that it is easily found in storage and retrieved for use, and securing the 

object from mishandling, accidental damage, or theft (Price and Smith 2000). Imagine a 

run of journals that contains information a researcher wants to consult: the researcher 

must be able to know what the title is (it is often found in a catalogue record in a 

database) and where it is held. He or she must be able to call the journals up from their 

location, whether on site, in remote storage, or through inter-library loan or document 

delivery. Finally, the researcher must find the journals to be the actual title and dates 

requested, with no pages missing in each volume and no volume missing from the run, 

for them to be of use. 

In the digital realm, the ability to know about, locate and retrieve, and then verify (or 

reasonably assume) that a digital object is authentic, complete, and undistorted is as 

crucial to "fitness for use" or preservation as it is for analogue objects – the manuscripts 

and maps, posters and prints, books and journals, or other genres of information that are 

captured in continuous waves, as opposed to discrete bits, and then recorded onto 

physical media for access (see chapter 32, this volume). 

The general approach to preserving analogue and digital information is exactly the same 

– to reduce risk of information loss to an acceptable level – but the strategies used to 

insure against loss are quite different. In the analogue realm, information is recorded on 

to and retrieved from a physical medium, such as paper, cassette tapes, parchment, film, 

and so forth. But as paper turns brittle, cassette tapes break, or film fades, information is 

lost. Therefore, the most common strategy for preserving the information recorded onto 

these media is to ensure the physical integrity of the medium, or carrier. The primary 

technical challenges to analogue preservation involve stabilizing, conserving, or 

protecting the material integrity of recorded information. Physical objects, such as books 

and magnetic tapes, inevitably age and degrade, and both environmental stresses such as 

excess heat or humidity, and the stresses of use tend to accelerate that loss. Reducing 

stress to the object, either by providing optimal storage conditions or by restricting use of 

the object in one way or another (including providing a copy or surrogate to the 

researcher rather than the original object), are the most common means to preserve fragile 

materials. Inevitably, preservation involves a trade-off of benefits between current and 

future users, because every use of the object risks some loss of information, some 

deterioration of the physical artifact, some compromise to authenticity, or some risk to 

the data integrity.

In the digital realm, there are significant trade-offs between preservation and access as 

well, but for entirely different reasons. In this realm information is immaterial, and the bit 

stream is not fixed on to a stable physical object but must be created ("instantiated" or 

"rendered") each time it is used. The trade-offs made between long-term preservation and 

current ease of access stem not from so-called data dependencies on physical media per 

se, but rather from data dependencies on hardware, software, and, to a lesser degree, on 

the physical carrier as well. 

What is archiving? 

The concept of digital preservation is widely discussed among professional communities, 

including librarians, archivists, computer scientists, and engineers, but for most people, 

preservation is not a common or commonly understood term. "Archiving", though, is 

widely used by computer users. To non-professionals, including many scholars and 

researchers unfamiliar with the technical aspects of librarianship and archival theory, 

digital archiving means storing non-current materials some place "offline" so that they 

can be used again. But the terms "archiving", "preservation", and "storage" have 

meaningful technical distinctions – as meaningful as the difference between "brain" and 

"mind" to a neuroscientist. To avoid confusion, and to articulate the special technical 

needs of managing digital information as compared with analogue, many professionals 

are now using the term "persistence" to mean long-term access – preservation by another 

name. The terms "preservation", "long-term access", and "persistence" will be used 

interchangeably here. 

Technical Challenges to Digital Preservation and Why 

They Matter 

The goal of digital preservation is to ensure that digital information – be it textual, 

numeric, audio, visual, or geospatial – be accessible to a future user in an authentic and 

complete form. Digital objects are made of bit streams of 0s and 1s arranged in a logical 

order that can be rendered onto an interface (usually a screen) through computer 

hardware and software. The persistence of both the bit stream and the logical order for 

rendering is essential for long-term access to digital objects. 

As described by computer scientists and engineers, the two salient challenges to digital 

preservation are: 

• physical preservation: how to maintain the integrity of the bits, the Os and Is that reside 

on a storage medium such as a CD or hard drive; and 

• logical preservation: how to maintain the integrity of the logical ordering of the object, 

that code that makes the bits "renderable" into digital objects. 

In the broader preservation community outside the sphere of computer science, these 

challenges are more often spoken of as:

• media degradation: how to ensure that the bits survive intact and that the magnetic tape 

or disk or drive on which they are stored do not degrade, demagnetize, or otherwise result 

in data loss (this type of loss is also referred to as "bit rot"); and 

• hardware/software dependencies: how to ensure that data can be rendered or read in the 

future when the software they were written in and/or the hardware on which they were 

designed to run are obsolete and no longer supported at the point of use. 

Because of these technical dependencies, digital objects are by nature very fragile, often 

more at risk of data loss and even sudden death than information recorded on brittle paper 

or nitrate film stock. 

And from these overarching technical dependencies devolve nearly all other factors that 

put digital data at high risk of corruption, degradation, and loss – the legal, social, 

intellectual, and financial factors that will determine whether or not we are able to build 

an infrastructure that will support preservation of these valuable but fragile cultural and 

intellectual resources into the future. It may well be that humanities scholars, teachers, 

and students are most immediately affected by the copyright restrictions, economic 

barriers, and intellectual challenges to working with digital information, but it will be 

difficult to gain leverage over any of these problems without a basic understanding of the 

ultimate technical problems from which all these proximate problems arise. Without 

appreciating the technical barriers to preservation and all the crucial dependencies they 

entail, humanists will not be able to create or use digital objects that are authentic, 

reliable, and of value into the future. So a bit more detail is in order. 

Media degradation: Magnetic tape, a primary storage medium for digital as well as 

analogue information, is very vulnerable to physical deterioration, usually in the form of 

separation of the signal (the encoded information itself) from the substrate (the tape on 

which the thin layer of bits reside). Tapes need to be "exercised" (wound and rewound) to 

maintain even tension and to ensure that the signal is not separating. Tapes also need to 

be reformatted from time to time, though rates of deterioration are surprisingly variable 

(as little as five years in some cases) and so the only sure way to know tapes are sound is 

by frequent and labor-intensive examinations. CDs are also known to suffer from 

physical degradation, also in annoyingly unpredictable ways and time frames. 

(Preservationists have to rely on predictable rates of loss if they are to develop 

preservation strategies that go beyond hand-crafted solutions for single-item treatments. 

Given the scale of digital information that deserves preservation, all preservation 

strategies will ultimately need to be automated in whole or in part to be effective.) 

Information stored on hard drives is generally less prone to media degradation. But these 

media have not been in use long enough for there to be meaningful data about how they 

have performed over the decades. Finally, a storage medium may itself be physically 

intact and still carry information, or signal, that has suffered degradation – tape that has 

been demagnetized is such an example. 

Hardware/software obsolescence: Data can be perfectly intact physically on a storage 

medium and yet be unreadable because the hardware and software – the playback

machine and the code in which the data are written – are obsolete. We know a good deal 

about hardware obsolescence and its perils already from the numerous defunct playback 

machines that "old" audio and visual resources required, such as Beta video equipment, 

16 mm home movie projectors, and numerous proprietary dictation machines. The 

problem of software obsolescence may be newer, but it is chiefly the proliferation of 

software codes, and their rapid supersession by the next release, that makes this computer 

software concern intractable. In the case of software, which comprises the operating 

system, the application, and the format, there are multiple layers that each require 

attending to when preservation strategies, such as those listed below, are developed. 

There are currently four strategies under various phases of research, development, and 

deployment for addressing the problems of media degradation and hardware/software 

obsolescence (Greenstein and Smith 2002). 

• Migration. Digital information is transferred, or rewritten, from one hardware/software 

configuration to a more current one over time as the old formats are superseded by new 

ones. Often, a digital repository where data are stored will reformat or "normalize" data 

going into the repository; that is, the repository will put the data into a standard format 

that can be reliably managed over time. As necessary and cost-effective as this process 

may be in the long run, it can be expensive and time-consuming. In addition, digital files 

translated into another format will lose some information with each successive 

reformatting (loss similar to that of translations from one language to another), ranging 

from formatting or presentation information to potentially more serious forms of loss. 

Migration works best for simple data formats and does not work well at all for 

multimedia objects. It is the technique most commonly deployed today and it shows 

considerable reliability with ASCII text and some numeric databases of the sort that 

financial institutions use. 

• Emulation. Emulation aims to preserve the look and feel of a digital object, that is, to 

preserve the functionality of the software as well as the information content of the object. 

It requires that information about the encoding and hardware environments be fully 

documented and stored with the object itself so that it can be emulated or essentially 

recreated on successive generations of hardware/software (though, of course, if that 

information is itself digital, further problems of accessibility to that information must also 

be anticipated). Emulation for preservation is currently only in the research phase. People 

are able to emulate retrospectively recently deceased genres of digital objects – such as 

certain computer games – but not prospectively for objects to be read on unknown 

machines and software programs in the distant future. Many in the field doubt that 

proprietary software makers will ever allow their software code to accompany objects, as 

stipulated by emulation, and indeed some in the software industry say that documentation 

is never complete enough to allow for the kind of emulation 100 or 200 years out that 

would satisfy a preservation demand (Rothenberg 1999; Bearman 1999; Holdsworth and 

Wheatley 2000). Perhaps more to the point, software programs that are several decades or 

centuries old may well be no more accessible to contemporary users than medieval 

manuscripts are accessible to present-day readers who have not been trained to read

medieval Latin in a variety of idiosyncratic hands. Nonetheless, emulation remains a 

tantalizing notion that continues to attract research dollars. 

• Persistent object preservation. A relatively new approach being tested by the National 

Archives and Records Administration for electronic records such as e-mails, persistent 

object preservation (POP) "entails explicitly declaring the properties (e.g., content, 

structure, context, presentation) of the original digital information that ensure its 

persistence" (Greenstein and Smith 2002). It envisions wrapping a digital object with the 

information necessary to recreate it on current software (not the original software 

envisioned by emulation). This strategy has been successfully tested in its research phase 

and the Archives is now developing an implementation program for it. At present it 

seems most promising for digital information objects such as official records and other 

highly structured genres that do not require extensive normalization, or changing it to 

bring it into a common preservation norm upon deposit into the repository. Ironically, 

this approach is conceptually related to the efforts of some digital artists, creating very 

idiosyncratic and "un-normalizable" digital creations, who are making declarations at the 

time of creation on how to recreate the art at some time in the future. They do so by 

specifying which features of the hardware and software environment are intrinsic and 

authentic, and which are fungible and need not be preserved (such things as screen 

resolution, processing speed, and so forth, that can affect the look and feel of the digital 

art work) (Thibodeau 2002; Mayfield 2002). 

• Technology preservation. This strategy addresses future problems of obsolescence by 

preserving the digital object together with the hardware, operating system, and program 

of the original. While many will agree that, for all sorts of reasons, someone somewhere 

should be collecting and preserving all generations of hardware and software in digital 

information technology, it is hard to imagine this approach as much more than a 

technology museum attempting production-level work, doomed to an uncertain future. It 

is unlikely to be scalable as an everyday solution for accessing information on orphaned 

platforms, but it is highly likely that something like a museum of old technology, with 

plentiful documentation about the original hardware and software, will be important for 

future digital archaeology and data mining. Currently, digital archaeologists are able, 

with often considerable effort, to rescue data from degraded tapes and corrupted (or 

erased) hard drives. With some attention now to capturing extensive information about 

successive generations of hardware and software, future computer engineers should be 

able to get some information off the old machines. 

The preservation and access trade-offs for digital information are similar to those for 

analogue. To make an informed decision about how to preserve an embrittled book 

whose pages are crumbling or spine is broken, for example, one must weigh the relative 

merits of aggressive and expensive conservation treatment to preserve the book as an 

artifact, versus the less expensive option of reformatting the book's content onto 

microfilm or scanning it, losing most of the information integral to the artifact as physical 

object in the process. One always needs to identify the chief values of a given resource to 

decide between a number of preservation options. In this case, the question would be 

whether one values the artifactual or the informational content more highly. This

consideration would apply equally in the digital realm: some of the technologies 

described above will be cheaper, more easily automated, and in that sense more scalable 

over time than others. Migration appears to be suitable for simpler formats in which the 

look and feel matters less and in which loss of information at the margins is an acceptable 

risk. The other approaches have not been tested yet in large scale over several decades, 

but the more options we have for ensuring persistence, the likelier we are to make 

informed decisions about what we save and how. There is no silver bullet for preserving 

digital information, and that may turn out to be good news in the long run. 

Crucial Dependencies and Their Implications for the 

Humanities 

Preservation by benign neglect has proven an amazingly robust strategy over time, at 

least for print-on-paper. One can passively manage a large portion of library collections 

fairly cheaply. One can put a well-catalogued book on a shelf in good storage conditions 

and expect to be able to retrieve it in 100 years in fine shape for use if no one has called it 

from the shelf. But neglect in the digital realm is never benign. Neglect of digital data is a 

death sentence. A digital object needs to be optimized for preservation at the time of its 

creation (and often again at the time of its deposit into a repository), and then it must be 

conscientiously managed over time if it is to stand a chance of being used in the future. 

The need for standard file formats and metadata and the role of data 

creators 

All the technical strategies outlined above are crucially dependent on standard file 

formats and metadata schemas for the creation and persistence of digital objects. File 

formats that are proprietary are often identified as being especially at risk, because they 

are in principle dependent on support from an enterprise that may go out of business. 

Even a format so widely used that it is a de facto standard, such as Adobe Systems, Inc.'s 

portable document format (PDF), is treated with great caution by those responsible for 

persistence. The owner of a such a de facto standard has no legal obligation to release its 

source code or any other proprietary information in the event that it goes bankrupt or 

decides to stop supporting the format for one reason or another (such as creating a better 

and more lucrative file format). 

Commercial interests are not always in conflict with preservation interests, but when they 

are, commercial interests must prevail if the commerce is to survive. For that reason, the 

effort to develop and promote adoption of non-proprietary software, especially so-called 

open source code, is very strong among preservationists. (Open source, as opposed to 

proprietary, can be supported by non-commercial as well as commercial users.) But to the 

extent that commercial services are often in a better position to support innovation and 

development efforts, the preservation community must embrace both commercial and 

non-commercial formats. While preservationists can declare which standards and formats 

they would like to see used, dictating to the marketplace, or ignoring it altogether, is not a 

promising solution to this problem. One way to ensure that important but potentially

vulnerable proprietary file formats are protected if they are orphaned is for leading 

institutions with a preservation mandate – national libraries, large research institutions, or 

government archives – to develop so-called fail-safe agreements with software makers 

that allow the code for the format to go into receivership or be deeded over to a trusted 

third party. (For more on file formats, see chapter 32, this volume.) 

Metadata schemas – approaches to describing information assets for access, retrieval, 

preservation, or internal management – is another area in which there is a delicate 

balance between what is required for ease of access (and of creation) and what is required 

to ensure persistence. Extensive efforts have been made by librarians, archivists, and 

scholars to develop sophisticated markup schemes that are preservation-friendly, such as 

the Text Encoding Initiative (TEI) Guidelines, which were first expressed in SGML, or 

Encoded Archival Description, which, like the current instantiation of the TEI, is written 

in XML, and open for all communities, commercial and non-commercial alike. The 

barriers to using these schemas can be high, however, and many authors and creators who 

understand the importance of creating good metadata nevertheless find these schema too 

complicated or time-consuming to use consistently. It can be frustrating to find out that 

there are best practices for the creation of preservable digital objects, but that those 

practices are prohibitively labor-intensive for most practitioners. 

The more normalized and standard a digital object is, the easier it is for a digital 

repository to take it in (a process curiously called "ingest"), to manage it over time, and to 

provide the objects back to users in their original form. Indeed, most repositories under 

development in libraries and archives declare that they will assume responsibility for 

persistence only if the objects they receive are in certain file formats accompanied by 

certain metadata. This is in sharp contrast to the more straightforward world of books, 

photographs, or maps, where one can preserve the artifact without having to catalogue it 

first. Boxes of unsorted and undescribed sources can languish for years before being 

discovered, and once described or catalogued, they can have a productive life as a 

resource. Although fully searchable text could, in theory, be retrieved without much 

metadata in the future, it is hard to imagine how a complex or multimedia digital object 

that goes into storage of any kind could ever survive, let alone be discovered and used, if 

it were not accompanied by good metadata. This creates very large up-front costs for 

digital preservation, both in time and money, and it is not yet clear who is obligated to 

assume those costs. 

For non-standard or unsupported formats and metadata schemas, digital repositories 

might simply promise that they will deliver back the bits as they were received (that is, 

provide physical preservation) but will make no such promises about the legibility of the 

files (that is, not guarantee logical preservation). Though developed in good faith, these 

policies can be frustrating to creators of complex digital objects, or to those who are not 

used to or interested in investing their own time in preparing their work for permanent 

retention. This is what publishers and libraries have traditionally done, after all. Some ask 

why should it be different now.

There is no question that a digital object's file format and metadata schema greatly affect 

its persistence and how it will be made available in the future. This crucial dependency of 

digital information on format and markup begs the question of who should pay for file 

preparation and what economic model will support this expensive enterprise. Who are the 

stakeholders in digital preservation, and what are their roles in this new information 

landscape? There is now an interesting negotiation under way between data creators and 

distributors on the one hand, and libraries on the other, about who will bear the costs of 

ingest. Various institutions of higher learning that are stepping up to the challenge of 

digital preservation are working out a variety of local models that bear watching closely 

(see below, the section on solutions and current activities). 

Need for early preservation action and the role of copyright 

Regardless of the outcome, it seems clear that those who create intellectual property in 

digital form need to be more informed about what is at risk if they ignore longevity issues 

at the time of creation. This means that scholars should be attending to the information 

resources crucial to their fields by developing and adopting the document standards vital 

for their research and teaching, with the advice of preservationists and computer 

scientists where appropriate. The examples of computing-intensive sciences such as 

genomics that have developed professional tracks in informatics might prove fruitful for 

humanists as more and more computing power is applied to humanistic inquiry and 

pedagogy. Such a function would not be entirely new to the humanities; in the nineteenth 

century, a large number of eminent scholars became heads of libraries and archives in an 

age when a scholar was the information specialist par excellence. 

One of the crucial differences between the information needs of scientists and those of 

humanists is that the latter tend to use a great variety of sources that are created outside 

the academy and that are largely protected by copyright. Indeed, there is probably no type 

of information created or recorded by human beings that could not be of value for 

humanities research at some time, and little of it may be under the direct control of the 

researchers who most value it for its research potential. The chief concern about 

copyright that impinges directly on preservation is the length of copyright protection that 

current legislation extends to the rights holders – essentially, the life of the creator plus 

70 years (or more) (Copyright Office, website). Why that matters to preservation goes 

back to the legal regime that allows libraries and archives to preserve materials that are 

protected by copyright. Institutions with a preservation mission receive or buy 

information – books, journals, manuscripts, maps – to which they may have no 

intellectual rights. But rights over the physical objects themselves do transfer, and the law 

allows those institutions to copy the information in those artifacts for the purposes of 

preservation. 

This transfer of property rights to collecting institutions breaks down with the new 

market in digital information. Publishers and distributors of digital information very 

seldom sell their wares. They license them. This means that libraries no longer own the 

journals, databases, and other digital intellectual property to which they provide access, 

and they have no incentive to preserve information that they essentially rent. Because

publishers are not in the business of securing and preserving information "in perpetuity", 

as the phrase goes, there is potentially a wealth of valuable digital resources that no 

institution is claiming to preserve in this new information landscape. Some libraries, 

concerned about the potentially catastrophic loss of primary sources and scholarly 

literature, have successfully negotiated "preservation clauses" in their licensing 

agreements, stipulating that publishers give them physical copies (usually CDs) of the 

digital data if they cease licensing it, so that they can have perpetual access to what they 

paid for. While CDs are good for current access needs, few libraries consider them to be 

archival media. Some commercial and non-commercial publishers of academic literature 

have forged experimental agreements with libraries, ensuring that in the event of a 

business failure, the digital files of the publisher will go to the library. 

It is natural that a bibliocentric culture such as the academy has moved first on the issue 

of what scholars themselves publish. The greater threat to the historical record, however, 

is not to the secondary literature on which publishers and libraries are chiefly focused. 

The exponential growth of visual resources and sound recordings in the past 150 years 

has produced a wealth of primary source materials in audiovisual formats to which 

humanists will demand access in the future. It is likely that most of these resources, from 

performing arts to moving image, photographs, music, radio and television broadcasting, 

geospatial objects, and more, are created for the marketplace and are under copyright 

protection (Lyman and Varian 2000). 

Efforts have barely begun to negotiate with the major media companies and their trade 

associations to make film and television studios, recording companies, news photo 

services, digital cartographers, and others aware of their implied mandate to preserve 

their corporate digital assets for the greater good of a common cultural heritage. As long 

as those cultural and intellectual resources are under the control of enterprises that do not 

know about and take up their preservation mandate, there is a serious risk of major losses 

for the future, analogous to the fate of films in the first 50 years of their existence. More 

than 80 percent of silent films made in the United States and 50 percent made before 

1950 are lost, presumably for ever. Viewing film as "commercial product", the studios 

had no interest in retaining them after their productive life, and libraries and archives had 

no interest in acquiring them on behalf of researchers. It is critical that humanists start 

today to identify the digital resources that may be of great value now or in the future so 

that they can be captured and preserved before their date of expiration arrives. 

Need to define the values of the digital object and the role of the 

humanist 

Among the greatest values of digital information technologies for scholars and students is 

the ability of digital information to transform the very nature of inquiry. Not bound to 

discrete physical artifacts, digital information is available anywhere at any time 

(dependent on connectivity). Through computing applications that most computer users 

will never understand in fine grain, digital objects can be easily manipulated, combined, 

erased, and cloned, all without leaving the physical traces of tape, erasure marks and 

whiteouts, the tell-tale shadow of the photocopied version, and other subtle physical clues

that apprise us of the authenticity and provenance, or origin, of the photograph or map we 

hold in our hands. 

Inquiring minds who need to rely on the sources they use to be authentic – for something 

to be what it purports to be – are faced with special concerns in the digital realm, and 

considerable work is being done in this area to advance our trust in digital sources and to 

develop digital information literacy among users (Bearman and Trant 1998; CLIR 2000). 

Where this issue of the malleability of digital information most affects humanistic 

research and preservation, beyond the crucial issue of authenticity, is that of fixity and 

stability. While the genius of digital objects is their ability to be modified for different 

purposes, there are many reasons why information must be fixed and stable at some point 

to be reliable in the context of research and interpretation. For example, to the extent that 

research and interpretation builds on previous works, both primary and secondary, it is 

important for the underlying sources of an interpretation or scientific experiment or 

observation to be accessible to users in the form in which it was cited by the creator. It 

would be useless to have an article proposing a new interpretation of the Salem witch 

trials rely on diary sources that are not accessible to others to investigate and verify. But 

when writers cite as their primary sources web-based materials and the reader can find 

only a dead link, that is, in effect, the same thing. 

To the extent that the pursuit of knowledge in any field builds upon the work of others, 

the chain of reference and ease of linking to reference sources are crucial. Whose 

responsibility is it to maintain the persistence of links in an author's article or a student's 

essay? Somehow, we expect the question of persistence to be taken care of by some vital 

but invisible infrastructure, not unlike the water that comes out of the tap when we turn 

the knob. Clearly, that infrastructure does not yet exist. But even if it did, there are still 

nagging issues about persistence that scholars and researchers need to resolve, such as the 

one known as "versioning", or deciding which iteration of a dynamic and changing 

resource should be captured and curated for preservation. This is a familiar problem to 

those who work in broadcast media, and digital humanists can profit greatly from the 

sophisticated thinking that has gone on in audiovisual archives for generations. 

The problem of persistent linking to sources has larger implications for the growth of the 

humanities as a part of academic life, and for the support of emerging trends in 

scholarship and teaching. Until publishing a journal article, a computer model, or a 

musical analysis in digital form is seem as persistent and therefore a potentially longlasting 

contribution to the chain of knowledge creation and use, few people will be 

attracted to work for reward and tenure in these media, no matter how superior the media 

may be for the research into and expression of an idea. 

Solutions and Current Activities 

There has been considerable activity in both the basic research communities (chiefly 

among computer scientists and information scientists) and at individual institutions 

(chiefly libraries and federal agencies) to address many of the critical technical issues of 

building and sustaining digital repositories for long-term management and persistence.

The private sector, while clearly a leading innovator in information technologies, both in 

the development of hardware and software and in the management of digital assets such 

as television, film, and recorded sound, has not played a leading public role in the 

development of digital preservation systems. That is primarily because the time horizons 

of the preservation community and of the commercial sectors are radically different. 

Most data storage systems in the private sector aim for retention of data for no more than 

five to ten years (the latter not being a number that a data storage business will commit 

to). The time horizon of preservation for libraries, archives, and research institutions must 

include many generations of inquiring humans, not just the next two generations of 

hardware or software upgrades. 

Because digital preservation is so complex and expensive, it is unlikely that digital 

repositories will spring up in the thousands of institutions that have traditionally served as 

preservation centers for books. Nor should they. In a networked environment in which 

one does not need access to a physical object to have access to information, the 

relationship between ownership (and physical custody) of information and access to it 

will be transformed. Within the research and nonprofit communities, it is likely that the 

system of digital preservation repositories, or digital archives, will be distributed among a 

few major actors that work on behalf of a large universe of users. They will be, in other 

words, part of the so-called public goods information economy that research and teaching 

have traditionally relied upon for core services such as preservation and collection 

building. 

Among the major actors in digital archives will be academic disciplines whose digital 

information assets are crucial to the field as a whole. Examples include organizations 

such as the Inter-university Consortium for Political and Social Research (ICPSR), which 

manages social science datasets, and the Human Genome Data Bank, which preserves 

genetic data. Both are supported directly by the disciplines themselves and through 

federal grants. The data in these archives are not necessarily complex, but they are highly 

structured and the depositors are responsible for preparing the data for deposit. JSTOR, a 

non-commercial service that preserves and provides access to digital versions of key 

scholarly journals, is another model of a preservation enterprise designed to meet the 

needs of researchers. It is run on behalf of researchers and financially supported by 

libraries through subscriptions. (Its start-up costs were provided by a private foundation.) 

Some large research university libraries, such as the University of California, Harvard 

University, Massachusetts Institute of Technology, and Stanford University, are 

beginning to develop and deploy digital repositories that will be responsible for some 

circumscribed portion of the digital output of their faculties. Another digital library 

leader, Cornell, has recently taken under its wing the disciplinary pre-print archive 

developed to serve the high-energy physics community and its need for rapid 

dissemination of information among the small but geographically far-flung members of 

that field. In the sciences, there are several interesting models of digital information being 

created, curated, and preserved by members of the discipline, among which arXiv.org is 

surely the best known. This model appears difficult to emulate in the humanities because 

arts and humanities disciplines do not create shared information resources that are then

used by many different research teams. Nevertheless, where redundant collections, such 

as journals and slide libraries, exist across many campuses, subscription-based services 

are being developed to provide access to and preserve those resources through digital 

surrogates. Examples include JSTOR, AMICO, and ARTstor, now under development. 

The economies of scale can be achieved only if the libraries and museums that subscribe 

to these services believe that the provider will persistently manage the digital surrogates 

and that they are therefore able to dispose of extra copies of journals or slides. 

Learned societies may be a logical locus of digital archives, as they are trusted third 

parties within a field and widely supported by members of the communities they serve. 

But they are not, as a rule, well positioned to undertake the serious capital expenditures 

that repository services require. That seems to be the reason why these subscription-based 

preservation and access services have appeared in the marketplace. 

The Library of Congress (LC), which is the seat of the Copyright Office and receives for 

inclusion in its collections one or more copies of all works deposited for copyright 

protection, is beginning to grapple with the implications of digital deposits and what they 

mean for the growth of the Library's collections. (At present, with close to 120 million 

items in its collections, it is several times larger than other humanities collections in the 

United States.) LC is developing a strategy to build a national infrastructure for the 

preservation of digital heritage that would leverage the existing and future preservation 

activities across the nation (and around the globe) to ensure that the greatest number of 

people can have persistent rights-protected access to that heritage. The National Archives 

is also working to acquire and preserve the digital output of the federal government, 

though this will entail an expenditure of public resources for preservation that is 

unprecedented in a nation that prides itself on its accountability to its people. 

The final word about major actors in preservation belongs to the small group of visionary 

private collectors that have fueled the growth of great humanities collections for 

centuries. The outstanding exemplar of the digital collector is Brewster Kahle, who has 

designed and built the Internet Archive, which captures and preserves a large number of 

publicly available sites. While the visible and publicly available Web that the Internet 

Archive harvests is a small portion of the total Web (Lyman 2002), the Archive has 

massive amounts of culturally rich material. Kahle maintains the Archive as a 

preservation repository – that is its explicit mission – and in that sense his enterprise is a 

beguiling peek into the future of collecting in the digital realm. 

Selection for Preservation 

While much work remains to ensure that digital objects of high cultural and research 

value persist into the future, many experts are cautiously optimistic that, with enough 

funding and will, technical issues will be addressed and acceptable solutions will be 

found. The issue that continues to daunt the most thoughtful among those engaged in 

preservation strategies is selection: how to determine what, of the massive amount of 

information available, should be captured and stored and managed over time.

In theory, there is nothing created by the hands of mankind that is not of potential 

research value for humanities scholars, even the humblest scrap of data – tax records, 

laundry lists, porn sites, personal websites, weblogs, and so forth. Technology optimists 

who believe that it will be possible to "save everything" through automated procedures 

have advocated doing so. Some teenager is out there today, they point out, who will be 

president of the United States in 30 years, and she no doubt already has her own website. 

If we save all websites we are bound to save hers. If completeness of the historical record 

is a value that society should support, then saving everything that can be saved appears to 

be the safest policy to minimize the risk of information loss. 

There may well be compelling social reasons to capture everything from the Web and 

save it forever – if it were possible and if there were no legal and privacy issues. (Most of 

the Web, the so-called Deep Web, is not publicly available, and many copyright experts 

tend to think that all non-federal sites are copyright-protected.) Certainly, everything 

created by public officials in the course of doing their business belongs in the public 

record, in complete and undistorted form. Further, there are the huge and expensive stores 

of data about our world – from census data to the petabytes of data sent back to Earth 

from orbiting satellites – that may prove invaluable in future scientific problem solving. 

On the other hand, humanists have traditionally valued the enduring quality of an 

information object as much as the quantity of raw data it may yield. There is reason to 

think that humanists in the future will be equally interested in the depth of information in 

a source as they are in the sheer quantity of it. Indeed, some historians working in the 

modern period already deplore the promiscuity of paper-based record making and 

keeping in contemporary life. 

But the digital revolution has scarcely begun, and the humanities have been slower to 

adopt the technology and test its potential for transforming the nature of inquiry than 

have other disciplines that rely more heavily on quantitative information. Some fields – 

history is a good example – have gone through periods when quantitative analysis has 

been widely used, but this was before the advent of computing power on today's scale. If 

and when humanists discover the ways that computers can truly change the work of 

research and teaching, then we can expect to see the growth of large and commonly used 

databases that will demand heavy investments in time and money and that will, therefore, 

beg the question of persistence. 

We will not be able in the future to rely on traditional assessments of value for 

determining what deserves preservation. In the digital realm, there will be no uniqueness, 

no scarcity, no category of "rare." There will remain only the signal categories of 

evidential value, aesthetic value, and associational value, the very criteria that are, by 

their subjectivity, best assessed by scholar experts. The role of humanists in building and 

preserving collections of high research value will become as important as it was in the 

Renaissance or the nineteenth century. Unlike those eras, however, when scholars could 

understand the value of sources as they have revealed themselves over time, there is no 

distinction between collecting "just in case" something proves later to be valuable, and 

"just in time" for someone to use now. Scholars cannot leave it to later generations to 

collect materials created today. They must assume a more active role in the stewardship

of research collections than they have played since the nineteenth century or, indeed, 

ever. 

Digital Preservation as a Strategy for Preserving Nondigital 

Collections 

There seems little doubt that materials that are "born digital" need to be preserved in 

digital form. Yet another reason why ensuring the persistence of digital information is 

crucial to the future of humanities scholarship and teaching is that a huge body of very 

fragile analogue materials demands digital reformatting for preservation. Most moving 

image and recorded sound sources exist on media that are fragile, such as nitrate film, 

audio tapes, or lacquer disks, and they all require reformatting on to fresher media to 

remain accessible for use. Copying analogue information to another analogue format 

results in significant loss of signal within a generation or two (imagine copying a video of 

a television program over and over), so most experts believe that reformatting onto digital 

media is the safest strategy. Digital reformatting does not result in significant (in most 

cases in any) loss of information. 

For those who prize access to the original source materials for research and teaching, the 

original artifact is irreplaceable. For a large number of other uses, an excellent surrogate 

or access copy is fine. Digital technology can enhance the preservation of artifacts by 

providing superlative surrogates of original sources while at the same time protecting the 

artifact from overuse. (See chapter 32, this volume.) 

All this means that, as creators and consumers of digital information, humanists are 

vitally interested in digital preservation, both for digital resources and for the abundance 

of valuable but physically fragile analogue collections that must rely on digitization. 

Given the extraordinary evanescence of digital information, it is crucial now for them to 

engage the copyright issues, to develop the economic models for building and sustaining 

the core infrastructure that will support persistent access, and, most proximate to their 

daily lives, to ensure that students and practitioners of the humanities are appropriately 

trained and equipped to conduct research, write up results for dissemination, and enable a 

new generation of students to engage in the open-ended inquiry into culture that is at the 

core of the humanistic enterprise. 


The author thanks Amy Friedlander, Kathlin Smith, and David Rumsey for their 

invaluable help. 

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http://www.clir.org/pubs/reports/publ07/contents.html.

Websites of Organizations and Projects Noted 

Art Museum Image Consortium (AMICO). http://www.amico.org. 

ARTstor. http://www.mellon.org/programs/otheractivities/ARTstor/ARTstor.htm. 

ArXiv.org. http://www.arXiv.org. 

Internet Archive, http://www.archive.org. 

Inter-university Consortium for Political and Social Research (ICPSR). 

http://www.icpsr.umich.edu. 

JSTOR. http://www.jstor.org. 

Library of Congress. The National Digital Information and Infrastructure Preservation 

Program of the. Library of Congress is available at http://www.digitalpreservation.gov. 

National Archives and Record Administration (NARA). Electronic records Archives, 

http://www.archives.gov/electronic_records_archives. 

National Archives and Record Administration (NARA). Electronic records Archives 

http://www.archives.gov/electronic_records_archives. 

For Further Reading 

On the technical, social, organizational, and legal issues related to digital preservation, 

the best source continues to be Preserving Digital Information: Report of the Task Force 

on Archiving of Digital Information (Washington, DC, and Mountain View, CA: 

Commission of Preservation and Access and the Research Libraries Group, Inc.). 

Available at http://www.rlg.org/ArchTF. 

Digital preservation is undergoing rapid development, and few publications remain 

current for long. To keep abreast of developments in digital preservation, see the 

following list, which themselves regularly digest or cite the latest information on the 

subject and present the leading research of interest to humanists on the subject:. 

D-Lib Magazine. http://www.dlib.org. 

Council on Library and Information Resources (CLIR). http://www.clir.org. 

Digital Library Federation (DLF). http://www.diglib.org. 

National Science Foundation (NSF), Digital Libraries Initiative (DLI). 

http://www.dli2.nsf.gov.

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