Combining bibliometrics, information retrieval, and relevance theory, Part 2: Some implications for information science

Howard Dalby White

40 citations0/0 files storedJournal of the American Society for Information Science and Technology

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scientometrics and bibliometrics research Library Science and Information Information Retrieval and Search Behavior

Abstract

Abstract When bibliometric data are converted to term frequency (tf) and inverse document frequency (idf) values, plotted as pennant diagrams, and interpreted according to Sperber and Wilson's relevance theory (RT), the results evoke major variables of information science (IS). These include topicality, in the sense of intercohesion and intercoherence among texts; cognitive effects of texts in response to people's questions; people's levels of expertise as a precondition for cognitive effects; processing effort as textual or other messages are received; specificity of terms as it affects processing effort; relevance, defined in RT as the effects/effort ratio; and authority of texts and their authors. While such concerns figure automatically in dialogues between people, they become problematic when people create or use or judge literature‐based information systems. The difficulty of achieving worthwhile cognitive effects and acceptable processing effort in human‐system dialogues explains why relevance is the central concern of IS. Moreover, since relevant communication with both systems and unfamiliar people is uncertain, speakers tend to seek cognitive effects that cost them the least effort. Yet hearers need greater effort, often greater specificity, from speakers if their responses are to be highly relevant in their turn. This theme of mismatch manifests itself in vague reference questions, underdeveloped online searches, uncreative judging in retrieval evaluation trials, and perfunctory indexing. Another effect of least effort is a bias toward topical relevance over other kinds. RT can explain these outcomes as well as more adaptive ones. Pennant diagrams, applied here to a literature search and a Bradford‐style journal analysis, can model them. Given RT and the right context, bibliometrics may predict psychometrics.

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References

1973Steps to an Ecology of Mind 1986Relevance: Communication and Cognition 1949Human behavior and the principle of least effort 1989Relevance: Communication and Cognition 1961Love and Death in the American Novel.1984Meaning, form, and use in context : linguistic applications 1997The Language of Metaphors 2015Question-Negotiation and Information Seeking in Libraries

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2017Pennants for Garfield: bibliometrics and document retrieval 2014Co-cited author retrieval and relevance theory: examples from the humanities 2016Generation of topic evolution trees from heterogeneous bibliographic networks 2014Modeling uncertainty in bibliometrics and information retrieval: an information fusion approach 2014The symbiotic relationship between information retrieval and informetrics 2009Some new tests of relevance theory in information science

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