Évaluation de l’efficacité de la bibliothèque : analyse des études majeures
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.039 | 0.113 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.016 | 0.024 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".