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Record W2001124886 · doi:10.1300/j104v28n04_05

The Essential Elements of Faceted Thesauri

2000· article· en· W2001124886 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueCataloging & Classification Quarterly · 2000
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Text Analysis Techniques
Canadian institutionsDalhousie University
Fundersnot available
KeywordsComputer scienceInformation retrievalFacet (psychology)Construct (python library)Search engine indexingSelection (genetic algorithm)HomogeneousArtificial intelligenceMathematics

Abstract

fetched live from OpenAlex

ABSTRACT The goal of this study is to evaluate, compare, and contrast how facet analysis is used to construct the systematic or faceted displays of a selection of information retrieval thesauri. More specifically, the study seeks to examine which principles of facet analysis are used in the thesauri, and the extent to which different thesauri apply these principles in the same way. A measuring instrument was designed for the purpose of evaluating the structure of faceted thesauri. This instrument was applied to fourteen faceted information retrieval thesauri. The study reveals that the thesauri do not share a common definition of what constitutes a facet. In some cases, the thesauri apply both enumerative-style classification and facet analysis to arrange their indexing terms. A number of the facets used in the thesauri are not homogeneous or mutually exclusive. The principle of synthesis is used in only 50% of the thesauri, and no one citation order is used consistently by the thesauri.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.920
Threshold uncertainty score0.380

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.013
GPT teacher head0.272
Teacher spread0.259 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it