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Record W2917906870 · doi:10.7202/1055163ar

Évaluation de l’efficacité de la bibliothèque : analyse des études majeures

2019· article· fr· W2917906870 on OpenAlexaffvenue
André Cossette

Bibliographic record

VenueDocumentation et bibliothèques · 2019
Typearticle
Languagefr
FieldDecision Sciences
TopicEfficiency Analysis Using DEA
Canadian institutionsCegep de Trois-Rivieres
Fundersnot available
KeywordsHumanitiesPolitical sciencePhilosophy

Abstract

fetched live from OpenAlex

L’évaluation scientifique de l’efficacité d’une bibliothèque exige la mesure de ses performances documentaires au moyen d’indicateurs de résultats. Les études d’évaluation en bibliothéconomie reposent sur l’approche systémique (recherche opérationnelle) et se servent de modèles analytiques pour quantifier la capacité des centres documentaires de répondre adéquatement aux multiples besoins des usagers. L’auteur effectue une analyse critique des principales études de performance ayant pour but de mesurer l’efficacité d’une bibliothèque qui doit répondre aux requêtes documentaires exprimées, soit par l’auteur ou le titre, soit par un sujet. L’emploi de méthodes quantitatives dans les projets d’évaluation des bibliothèques permettra d’améliorer la qualité des services documentaires et de modifier l’image de la bibliothéconomie.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.039
metaresearch head score (Gemma)0.113
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.961
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0390.113
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0160.024
Science and technology studies0.0020.002
Scholarly communication0.0110.005
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0070.003

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 head0.065
GPT teacher head0.417
Teacher spread0.352 · 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designObservational
DomainEvaluation
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".

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Citations0
Published2019
Admission routes2
Has abstractyes

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