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Abstract

Probably the most interesting, original and controversial indexing system of the 1970s has been PRECIS (the Preserved Context Index System) developed by Derek Austin. It attracted such attention that the short courses given at the British National Bibliography offices in London -initiated as a means of teaching the staff of BNB who did subject indexing-were repeated all over the world, in the form of three-day or three-week sessions. A few fortunate non-BNB indexers were taken into London classes, taught by Austin and Digger, and had the advantage of a week's work with the regular staff theresomething that could not be done elsewhere. In addition, descriptions of the system were published from time to time as progress was made an the system itself and clarification or updating were needed. This article has been selected as an example of clarification. A second edition of the original 1974 Manual describing PRECIS appeared in early 1984, so the system may now be regarded as fully mature. PRECIS, which is the culmination of Austin's research for the Classification Research Group plus considerable additional input from the science of linguistics, is a highly sophisticated indexing system, deliberately created to be used with a computer. The motive behind it was that the indexer should do the brain work and the computer the nitty-gritty work of manipulating index terms. Chosen combinations and permutations are used as head words under strict rules to ensure that the context which defines the specific meaning of these words is always present, thus avoiding the ambiguity problem in indexing. The system also has classifi ation features, in part drawn from the Group's long interest in faceted classification. The actual index produces a large number of index entries, each carrying with it the basic context of the item being described. A weIl-defined terminology, in addition to contextdependency, is a feature of PRECIS. Role operators define the types of terms used and control the manipulations of the terms, which are originally entered as a string. The indexer is able to analyze and reduce to string form any document, process, or material that can be described in words, making it ideal for most audiovisual material.