Computing in Humanities Education: A European Perspective,
edited by Koenraad de Smedt, Hazel Gardiner, Espen Ore, Tito Orlandi, Harold Short, Jacques Souillot, and William Vaughan, SOCRATES/ERASMUS thematic network
project on Advanced Computing in the Humanities, Bergen, University of Bergen,
1999, Ch. 2, European studies on formal methods in the humanities, pp. 25-34, URL =
<http://gandalf.aksis.uib.no/AcoHum/book/>.
This dependence on fashionable trends, as reflected by popular media, has not always been healthy. The discussions about artificial intelligence in the eighties are a good example. At one time it was almost impossible to exclude the necessity of an expert system in any project. This was unhealthy, not so much because very little ever came of that fad, but because it fundamentally discredited the notion of expert systems ever being of any relevance to the humanities at all. Today even to consider such a notion seems to be politically almost as unwise as it was unavoidable a decade or two ago.
The following section tries to clarify what we can actually say about the relationship between computer science and the humanities which remains valid while fashions change. We must exercise caution: traditionally, such discussions easily become unfocussed, because three roles of computers in the humanities are frequently intermingled. Computers can be used to gain scientific knowledge, to teach that knowledge and to disseminate it. These three sets of activities are of course related; but the challenges they pose and the problems they have to solve, are fundamentally quite different.
In the following, we intentionally restrict ourselves mainly to the first of the three, although at the end of the chapter we will also address the second. We are dealing with methods, that is, the canon (or set of tools) needed to increase the knowledge agreed to be proper to a particular academic field. And we restrict ourselves to those methods that can profit from the use of computational tools and concepts. Since this approach invariably requires the ability to make enquiries according to formally defined specifications, we speak about formal methods. This is a deliberate restriction on the field of discussion; we are not attempting to discuss how to use computers to teach a traditional subject, nor how to produce books more cheaply. We do however discuss the extent to which new media make information available in such quantities that traditional information-handling methods have to change in order to cope.
Finally, we note that information technology itself changes the world in which we are living in many ways. The arts and social sciences reflect and interpret of the world in which we are living. They must therefore tackle information technology, just as they must tackle other changes of the society in which we live. Since, however,the study of the humanities, in our understanding, is different from the production of art or the interpretation of social change, neither the artistic nor the sociological implications of a new generation of media is discussed here.
While we restrict our topic in this way, in another we would like to see it as broad as possible. While databases are not our topic, their use in history do form part of our agenda. Similarly, while statistics are not our topic, the special techniques needed to make use of them in literature and other disciplines are certainly part of our agenda. Summing up, our subject consists of the specific methods for the application of tools and techniques to the humanities fields, in so far as this application improves our capability of acquiring new knowledge.
Computer science is a very wide ranging field. At one extreme, it is almost indistinguishable from mathematics and logic; at another, it is virtually the same as electrical engineering. This, of course, is a consequence of the genealogy of the field. The Turing machine is an interesting scientific construct, independently of whether a physical machine with these properties has been constructed. Conversely, the electronic components of computers can be studied at a physical level, independently of their computing purposes.
Having widely different ancestors in itself, computer science in turn became parent to a very mixed crowd of offspring. Pseudo-disciplines such as medical computer science and juridical computer science have sprung up in recent years abundantly. Some of them, like forestry research computer science (Forstliche Biometrie und Informatik) for which a German university recently accepted a Habilitation, will probably continue to raise eyebrows for some time to come. Others, notably computational linguistics, have long been established as independent areas of research and self contained academic disciplines quite beyond dispute.
The existence of this wide variety of disciplines, related to or spun off from computer science in general, implies two things. First, there must be a core of computer science methods, which can be applied to a variety of subjects. Second, for the application of this methodological core, a thorough understanding of the knowledge domain to which it is applied is necessary. The variety of area specific computer sciences is understandable from the need for specialized expertise in the knowledge domain of each application.
In some countries there is even a preference to use a different term for computer science when applied to a knowledge domain. In the Netherlands, there is a tendency to speak not of documentaire informatica but of documentaire informatiekunde. Thus, informatica is reserved for the core of computer science which tries to understand abstract principles, while informatiekunde is always concerned with the the application of computational methods in some particular area distinct from computer science proper.
As in many other cases, what does not constitute that self-contained, yet applicable core of computer science is more easily specified than what does. Engineering topics are not part of it, although, of course, the construction of sensors in the bio-sciences may require knowledge which the construction of sensors in thermal physics does not. The self-contained core should also be independent from the disciplines in which it is applied; although, of course, there are some fields to which (say) fuzzy systems and their accompanying theory are intrinsically more relevant than others.
Leaving aside these subtle shades, for the purpose of a short introduction, the core of all applied computer sciences is more than the sum of its intellectual ancestors, which may themselves be inextricably associated with particular knowledge domains. Instead, we will attempt to define the core in terms of the traditional combination of data structures and algorithms, applied to the requirements of a discipline:
If we accept the assumption that the succesful application of computational methods, in the sense above, strongly depends on the domain of knowledge to which it is applied, then we also have to accept that applying computational methods without an understanding of that domain will be disastrous. To give an example, a German university in the early eighties introduced a study programme called Informatik für die Geisteswissenschaften, which required more course credits for numerical analysis than a computer science master at many other universities. The same course did not however require the students to apply their knowledge to a single topic within the humanities. After spectacular student interest in the first year, the course had to be withdrawn in the second, as no students could be found to take it.
We conclude that it is pointless to teach computer science to humanities scholars or students unless it is not directly related to their domain of expertise.
On the other hand, time and again, skills in computing are mistaken by humanities scholars for a qualification in computer science. A case in point is the plethora of word processing courses which arose among American universities during the early days of the personal computer. Few of these survived more than a few years, as students rapidly discovered that it was ultimately more convenient to learn their content at their own pace, from general-purpose manuals and introductions. Such courses, which are still taught at some European universities, rapidly become out of date; as each new generation of students arrives equipped with better practical skills than their teachers.
We conclude that humanities computing courses are likely to remain a transient phenomenon, unless they include an understanding of what computer science is all about.
Unfortunately, this problem is not restricted to individual courses, which might be taken simply as amusing consequences of un-informed enthusiasm. It can have more serious consequences. At another University, a Department for Computing for the Humanities was created in the eighties to provide computer literacy for each student of the arts faculty. Not too far into the nineties, at least one of the departments of that faculty threatened to train its students independently unless the Computing for the Humanities department brought its curriculum up to date, in step with current needs. More recently the department was closed down, since the arts faculty considered it no longer provided anything of value for its students.
One might wonder whether it is the task of a university to teach basic computer literacy at all. Students never used to get academic credit for typewriting skills before the invention of word processing, nor for looking up a book in a catalog before the advent of the Internet. Is it elitist to ask why they should get academic credit for acquiring skills in word processing and Internet information retrieval?
There are perhaps two important differences which suggest that they should. The more visible of the two is precisely to do with the rapid evanescence of information technology. Typewriting is a skill that remains stable between finishing secondary school and gaining a doctorate. By contrast, modern information technologies have a habit of changing so rapidly so that what was almost arcane knowledge for a freshman can easily have become computer literacy by the time that student acquires a PhD. Moreover, the PhD student is expected to use tools which could hardly be imagined when he or she began their course of study. If we take seriously the notion of lifelong learning, we might well claim that computer literacy should concern arts faculties, not for its own sake, but to help students update their own knowledge, and to impress upon them the constant need to do so.
The other, less visible, difference is that one may fully master word processing, spreadsheets, simple data bases and HTML authoring (all of which have recently been transformed from advanced knowledge to basic survival skills) and still be helpless, when trying to apply them to a humanities discipline. Even today, many people who use word processors routinely will find it challenging to include Cyrillic characters into their texts, let alone Arabic or Vietnamese (we refer also to Chapter 5 in this respect). A person can routinely submit his tax returns with the help of a spreadsheet and still despair of being able to do meaningful computations with a medieval tax document. A student can have a brilliant homepage but still be unable to encode a literary text in a way likely to remain useful beyond the lifetime of current full text retrieval packages. We conclude that even computer literacy should be taught in the humanities by concentrating on the specific problems posed by the disciplines. Word processing for literary disciplines should concentrate on peculiarities of the specific languages or editorial styles; quantitative packages should be taught to historians in a way that enables them to handle fuzzy data, Roman numerals, and so on; markup for text-based disciplines (see Chapter 3) should focus on general principles, not the peculiarities of the current generation of browsers.
To fulfil both these requirements, Humanities Computer Literacy should be taught to humanities students only by teachers who are themselves fully trained in Humanities Computing. Furthermore, rather than relying on a fixed canon of skills, courses (particularly those at the most introductory level) must be revised year by year to keep them at the shifting edge between what students can be expected to learn by themselves and what they can not.
In a nutshell, nobody should attempt to teach computing skills to a humanities student without experience in computer supported humanities research, preferably in a subject close to the one from which the student population of the course to be taught is being recruited. Exceptions always exist; but it is the problems of communication between 'pure' technicians and content-interested humanities students which, time and again, tend to dominate any discussion of common problems at each of the (many) conferences on one aspect or another of humanities computing taking place every year.
Humanities Computing, the second of our three levels, constitutes the sum of all available methods which can enhance the scientific validity of research results or facilitate the pursuit of otherwise impossible research strategies. It starts with methods adapted from other fields of study; for example, the canon of analytical statistics, which has been developed for various fields. To apply this canon to authorship studies, traditional sampling techniques have to be augmented in specific ways. It continues with methods which, although originated in other fields, have developed independently within specific humanities disciplines. In art history, for example, thesaurus-based systems were originally adapted from other disciplines, but have taken on a life of their own and started a discussion on the proper way to describe the content of images, which has no clear equivalent in any other field. Finally, there are computational methods which the humanities have not borrowed but which more or less originated within some field of the humanities. As an example, we mention the long and rich tradition of methods for identifying individuals in historical documents, despite variations in orthography, variable subsetting of name sets, property-based name shifts and other causes.
Humanities computing is most clearly in need of institutional stabilization. The tradition of the field is incredibly long. Many of today's perennial questions about the optimal representation of humanities information in a computer can already be found in such conference volumes as the Wartenstein conference of 1962 (Hymes 1965), which seems to have been one of the first attempts to survey the field. Indeed, among the major challenges for humanities computing is that few of its followers are sufficiently aware of its long and rich tradition. Every now and again, a fresh wave of discussion is ignited by authors or theoreticians who simply assume that they can ignore forty years of tradition and start from scratch.
This lack of perception is particularly unfortunate for the individual researcher, as it usually means that newcomers to the field have to painfully rediscover ancient solutions simply because they have not been adequately transmitted through the generations . This is unfortunate for the humanities as a whole, because it means that advances in methodology proceed much more slowly than they might. In most European countries, humanities computing is almost a label for a specific stage in the life of a scholar. The vast majority of practitioners are either at the stage of composing their PhD thesis or just after it. After working actively in the field for say five years, they either become computer specialists, (which means that they leave academia for industry), or they fall back upon more traditional areas of their home disciplines. It is scarcely surprising therefore that few permanent positions for humanities computing specialists exist.
As long as we stay with our original definition, that humanities computing is the application of computational tools for the benefit of the various humanities disciplines, there is nothing wrong with this situation. Still, it means that many researchers all over Europe are constantly rediscovering some of the basics of humanities computing, while few, if any, possibilities exist to hand on their discoveries further. To solve that situation, we propose, that, just as we required Humanities Computer Literacy to be taught by people with a Humanities Computing background, so Humanities Computing should in turn be taught by specialists in Humanities Computing Science.
This field of Humanities Computer Science is populated by persons who make the study and development of the possibilities of computer applications in the humanities their profession. With a solid background in one or more humanities fields, they understand the problems of these disciplines; with a strong background in computer science in general, they are able to contribute to the development of data structures and algorithms as defined initially. This is where the humanities have made some lasting contributions to computer science. For example, parsing algorithms which have been developed by computational linguists now form part of the canon of computer science methodology.
This field of Humanities Computer Science should be supra-national (in our case European) from the very start. The field itself profits from the strongest possible emphasis on internationalization. As with any other new discipline, it would otherwise be in danger of being influenced overly much by the idiosyncrasies and preferences of a few individuals from one national academic system.
Creating a European framework of reference has an added value. Very few institutions exist today offering training on a level which could be clearly identified as Humanities Computer Science in the terms defined above. There are many attempts, however, to offer humanities students introductory computational skills and appropriate background knowledge, bundled in a confusing plethora of degrees, add-on diplomas, sandwich courses, etc. This has two major drawbacks:
[Manfred Thaller]