The <em>Répertoire De Vedettes-Matière</em> de l’Université Laval Library 1946-1992: Francophone Subject Access in North America and Europe
Bibliographic record
Abstract
In 1946, the Université Laval in Quebec City, Quebec, Canada, started using Library of Congress Subject Headings (LCSH) in French. To do so, the librarians created an authority list in French, Répertoire de Vedettes-matière (RVM), whose first published edition appeared in 1962 (the first edition appeared with the title Répertoire des Vedettes-matière). Since then, RVM has had increasing importance in providing support for subject access in francophone countries around the world as other libraries, first in Canada and then in Europe, either adopted RVM, often with some modification, for subject access or used it as a resource for creating French subject terms. The following article will examine why and how the Université Laval adopted LCSH as a means to provide subject access at an acceptable cost for its own library. The next step included partnerships with the most important libraries in Canada with an interest in French-language cataloging--the Université de Montréal, the Bibliothèque Nationale du Canada (BNC) (the National Library of Canada), and the Bibliothèque Nationale du Quebec (BNQ).
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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.012 | 0.004 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".