Le Répertoire de vedettes-matière de la Bibliothèque de l’Université Laval : sa genèse et son évolution (1ère partie)
Bibliographic record
Abstract
Élaboré pour répondre d’abord aux besoins de la Bibliothèque de l’Université Laval, le Répertoire de vedettes-matière a connu au fil des ans une diffusion qu’ont favorisée sa reconnaissance comme norme canadienne par la Bibliothèque nationale du Canada et la mise en place d’un réseau de catalogage coopératif. À l’étranger, son adoption par de nombreuses bibliothèques d’envergure et, notamment, par la Bibliothèque nationale de France confirme son rayonnement et lui assure un rôle important dans la normalisation éventuelle de l’indexation-matière au sein de la francophonie. Le présent article qui sera suivi d’un second à paraître dans une livraison ultérieure retrace l’évolution de cet outil et les événements majeurs qui l’ont marquée.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.010 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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; a candidate call from one teacher head, 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".