Re: RT list: Relevance Theory and Information Science (2nd re-submission)

This is an attempt to post web addresses for Howard White's article.

Here (I hope) is part 1:

http://tinyurl.com/ybfocym

And part 2:

http://tinyurl.com/yzrypom

I don't understand what went wrong before, nor do I know whether it
happened at Professor White's university's email servers or at UCL's,
so it's entirely possible that these links will get blocked too. In
case that happens, I am also posting the Google Scholar pages for
Professor White's paper.

Part 1:

http://scholar.google.com/scholar?cluster=5049123151681584403

Part 2:

http://scholar.google.com/scholar?cluster=4614732856719597234

On each of these Google Scholar pages, the top link currently is the
one Dr. White has been trying to post here.

Best,
Nick

Nicholas Allott
Postdoctoral research fellow
CSMN
University of Oslo

n.e.allott@csmn.uio.no
nicholas.allott@gmail.com

>
> Having just received another copy of today's submission to the list,
> I still don't know
> why the two URLs I added for pdf copies of my articles caused such
> automated,
> red-flagged consternation. I have removed the URLs here; they are
> not sites of
> mine; I found them by Googling my title "Combining bibliometrics,
> information
> retrieval, and relevance theory." Presumably anyone who wants pdfs
> can do
> the same; they worked fine for me this afternoon. The Journal of
> the American Society
> for Information Science and Technology is also held in many academic
> libraries.
> My text is otherwise unchanged:
>
>
>
> Hello, All,
>
> Dan Sperber was kind enough to announce on this list my recent
> article:
>
> White, Howard D. "Some new tests of relevance theory in information
> science."
>
> The journal Scientometrics has published this online; it will appear
> in print later this year.
> The slides from my presentation at the International Society for
> Scientometrics and
> Informetrics (Rio de Janeiro, 2009) are on the Web, but not all are
> well reproduced.
> In any case, they are hard to follow without a textual accompaniment.
>
> For anyone interested, I would suggest the two long articles below,
> in which I try to mesh
> relevance theory with information science across a number of
> fronts. The "new tests"
> of the article above are made with reference to the "old tests" in
> these two articles. I
> realize that the abstracts posted below may make them seem
> outlandish, but I assure those
> on this list that they are easy enough to read, with all technical
> terms and quantification
> explained at length and illustrated with copious examples. Applying
> relevance theory
> to data from bibliometrics (the quantitative study of literatures)
> allows me to explain why
> a formula long used in computerized information retrieval has been
> popular--a formula
> for ranking documents by their predicted relevance to a user's
> query. However, I also
> show that RT can underpin a unified explanation of many things in
> library-related
> information science that have heretofore not been explained, let
> alone integrated. Those
> on this list may not be aware that information scientists commonly
> hold relevance to be
> the central concept of their field. What they usually have in mind
> is ARTIFICIAL relevance:
> the algorithmic responses of a system in delivering appropriate
> writings to its users. But
> information scientists have done not nearly as good a job of
> analyzing "relevance" as S&W
> and their followers. So in my work I am attempting to move insights
> from RT into the
> study of artificial relevance in dialogues between literature-based
> information systems
> and human beings. I use numeric measures in the SYSTEM's predictions
> of what
> items persons will perceive as most relevant, but, with S&W, I
> believe that PERSONS
> can only judge relevance in crude degrees of "more" or "less." And,
> yes, Peter and Mary
> do appear.
>
> White, Howard D. (2007) Combining bibliometrics, information
> retrieval, and
> relevance theory. Part 1: First examples of a synthesis. Journal
> of the American
> Society for Information Science 58: 536-559.
>
> Abstract: In Sperber and Wilson’s relevance theory (RT),
> the ratio Cognitive Effects / Processing Effort defines the
> relevance of a communication. The tf*idf formula from
> information retrieval is used to operationalize this ratio for
> any item co-occurring with a user-supplied seed term in
> bibliometric distributions. The tf weight of the item predicts
> its effect on the user in the context of the seed
> term, and its idf weight predicts the user’s processing effort
> in relating the item to the seed term. The idf measure,
> also known as statistical specificity, is shown to
> have unsuspected applications in quantifying interrelated
> concepts such as topical and nontopical relevance,
> levels of user expertise, and levels of authority. A
> new kind of visualization, the pennant diagram, illustrates
> these claims. The bibliometric distributions visualized
> are the works cocited with a seed work (Moby
> Dick), the authors cocited with a seed author (White HD,
> for maximum interpretability), and the books and articles
> cocited with a seed article (S.A. Harter’s “Psychological
> Relevance and Information Science,” which introduced
> RT to information scientists in 1992). Pennant diagrams
> use bibliometric data and information retrieval techniques
> on the system side to mimic a relevance-theoretic
> model of cognition on the user side. Relevance
> theory may thus influence the design of new visual information
> retrieval interfaces. Generally, when information
> retrieval and bibliometrics are interpreted in light of
> RT, the implications are rich: A single sociocognitive theory
> may serve to integrate research on literature-based
> systems with research on their users, areas now largely
> separate.
>
> White, Howard D. (2007) Combining bibliometrics, information
> retrieval, and
> relevance theory. Part 2: Some implications for information science.
> Journal of
> the American Society for Information Science 58: 583-605.
> 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.
>
Received on Thu Jan 14 15:42:42 2010