Dans les règles de l’art : nouvelles normes de documentation pour les musées canadiens
Bibliographic record
Abstract
Les musées doivent gérer la documentation de leurs collections dans un monde de plus en plus réseauté. Ils font face à une évolution constante des attentes des utilisateurs en ce qui touche l’accès à l’information et l’interaction avec les contenus. Dans ce contexte, le rôle et l’importance des normes de documentation pour les musées se sont accrus, puisque la normalisation et l’échange des données sont devenus essentiels. Les normes de documentation à l’intention des musées ont connu récemment de nombreux développements, et les musées canadiens, ainsi que le Réseau canadien d’information sur le patrimoine (RCIP), ont largement contribué à leur élaboration. Le présent article propose un survol des principales normes de documentation muséale utilisées au Canada et présente les développements les plus récents.
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 | 0.021 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.013 | 0.027 |
| Scholarly communication | 0.023 | 0.010 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".