La recherche documentaire par vedettes-matière dans une bibliothèque de collège
Bibliographic record
Abstract
Après avoir fait état des principales difficultés de la recherche documentaire par vedettes-matière, l’auteur présente les résultats d’une enquête effectuée auprès d’étudiants qui consultaient le catalogue sujets d’une bibliothèque de collège. Le taux de concordance entre les termes utilisés par les étudiants et les vedettes-matière est très élevé, mais la principale difficulté reste l’atteinte du niveau de spécificité requis. Une analyse du taux de rappel et du taux de précision des vedettes-matière révèle également un degré d’efficacité élevé. Quelques recommandations sont faites pour améliorer les performances des étudiants et l’efficacité des vedettes-matière.
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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 | 0.030 | 0.156 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.026 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.016 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.004 |
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".