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Record W2597412777 · doi:10.29173/cais247

Delineation of Information Retrieval Research Area Using Input-Output Model

2013· article· fr· W2597412777 on OpenAlex
Nahid Tabatabaei, Jamshid Beheshti

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.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueProceedings of the Annual Conference of CAIS / Actes du congrès annuel de l ACSI · 2013
Typearticle
Languagefr
FieldComputer Science
TopicWeb visibility and informetrics
Canadian institutionsMcGill University
Fundersnot available
KeywordsIdentifierLibrary scienceComputer scienceInformation scienceInformation retrievalHumanitiesPhilosophy

Abstract

fetched live from OpenAlex

Information Retrieval (IR) research has been one of the core research areas of Information Science (IS) and also a major source of interdisciplinary relations between IS and other disciplines, constituting a disjointed research area with fuzzy boundaries. The main purpose of this paper is to identify disciplines that contribute to IR research and to map the main features of the interdisciplinary structure of IR research area as a whole.La recherche dans le domaine du repérage d’information (RI) a toujours été un domaine-clé des sciences de l’information (SI) et également une source majeure de relations interdisciplinaires entre les SI et d’autres disciplines, constituant un domaine de recherche incohérent aux frontières floues. Le principal objectif de cette communication est d’identifier les disciplines qui contribuent à la recherche en RI et de mettre en correspondance les principales caractéristiques de la structure interdisciplinaire de la recherche en RI, de manière à constituer un tout.

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)0.004
metaresearch head score (Gemma)0.052
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Scholarly communication
Consensus categoriesScholarly communication
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.901
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.052
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.003
Science and technology studies0.0000.001
Scholarly communication0.0050.062
Open science0.0030.002
Research integrity0.0000.001
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 head0.122
GPT teacher head0.313
Teacher spread0.191 · 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