Étude préliminaire à l’élaboration d’un vocabulaire contrôlé en langue française pour le catalogue matière des bibliothèques publiques et scolaires
Bibliographic record
Abstract
Les outils traditionnels de repérage thématique en langue française répondent-ils aux besoins des usagers des bibliothèques publiques et scolaires ? Cette étude répond, par la négative, à cette question en s’appuyant sur une évaluation des listes de vedettes-matière disponibles et un examen critique de l’analyse de 100 monographies récentes établie par les Services documentaires multimedia. Une rétrospective de l’évolution des méthodes de création des catalogues par matière en Amérique du Nord ouvre cette étude que l’auteur conclut par quelques recommandations devant guider l’élaboration d’un vocabulaire mieux adapté à la clientèle considérée.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.047 | 0.141 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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; both teacher heads agree on what is shown here.
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".