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Record W2586528225 · doi:10.29173/cais727

An Exploratory Study into the Application and Interpretation of Frequency Tables Relative to a Search Retrieval Set

2013· article· en· W2586528225 on OpenAlexaffvenueabout
Patrick Gignac, Lynne C. Howarth

Bibliographic record

VenueProceedings of the Annual Conference of CAIS / Actes du congrès annuel de l ACSI · 2013
Typearticle
Languageen
FieldComputer Science
TopicInformation Retrieval and Search Behavior
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsRelevance (law)Computer scienceSet (abstract data type)Information retrievalRelevance feedbackSubject (documents)RestructuringTest (biology)Table (database)Process (computing)Interpretation (philosophy)Value (mathematics)Data miningArtificial intelligenceWorld Wide WebMachine learning

Abstract

fetched live from OpenAlex

From the 1994 CAIS Conference: The Information Industry in Transition McGill University, Montreal, Quebec. May 25 - 27, 1994.Hidreth (1989:22) observed that subject searchers likely require a more interactive subject searching approach which allows for term and document appraisal and relevance feedback during the search process. In order to assess aspects of a user's evaluative skills, and of relevance feedback, a laboratory test was initiated to examine: 1) a procedure for evaluating a retrieved set: 2) a mechanism - a frequency table - to be used for that evaluation; and 3) the relationship between the evaluation procedure and the relevance feedback mechanism. Contrary to expectations that participants would use the frequency distribution tables to assist in evaluating a retrieved set, tables were used, instead, to modify the subsequent search strategy, to restructure the query. This suggests that frequency distribution tables may have some utility or value as interactive navigational aids to searchers.

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.

How this classification was reachedexpand

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)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesScholarly communication
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.639
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.016
Open science0.0020.001
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 head0.025
GPT teacher head0.284
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

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designQualitative
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2013
Admission routes3
Has abstractyes

Explore more

Same venueProceedings of the Annual Conference of CAIS / Actes du congrès annuel de l ACSISame topicInformation Retrieval and Search BehaviorFrench-language works237,207