RASUQAM : le thésaurus de descripteurs de l’Université du Québec à Montréal (UQAM)
Bibliographic record
Abstract
RASUQAM est un thésaurus encyclopédique développé depuis 1994 par le Service des bibliothèques de l’Université du Québec à Montréal. Conforme au format MARC 21, il compte aujourd’hui environ 40 000 descripteurs, dont près de 70 % sont des noms communs. Proche du langage couramment utilisé par les usagers, RASUQAM facilite la recherche et la navigation. Le présent article en retrace l’historique et décrit ses caractéristiques principales ainsi que sa structure, son état actuel et sa croissance. À l’aide d’exemples, il présente les différentes catégories de descripteurs qui s’y trouvent et fournit des précisions sur les zones et sous-zones MARC utilisées. Un parallèle est enfin établi avec le Répertoire des vedettes-matière (RVM) de l’Université Laval, langage documentaire réputé et très utilisé.
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 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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.019 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 0.005 |
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".