Rapport et recommandations pour les initiatives de ressources éducatives libres (REL) et solutions abordables à l’Université d’Ottawa
Bibliographic record
Abstract
Créé à la fin de 2019, le Groupe de travail sur le MDLA avait pour mandat 1) de faire mieux connaître le contenu de cours abordable et en promouvoir l’utilisation, 2) d’explorer les stratégies visant à établir un environnement universitaire qui favorise et récompense l’adoption de ressources éducatives libres et formuler des recommandations et 3) de coordonner les efforts des principales parties prenantes qui réalisent et soutiennent les initiatives d’éducation ouverte sur le campus. Le rapport conclut douze mois de travail et s’appuie sur les initiatives existantes en matière de libre accès et d’apprentissage en ligne. Il présente quatre catégories de recommandations pour guider les activités soutenant l’éducation ouverte et l’abordabilité des manuels scolaires à l’Université d’Ottawa au cours des cinq prochaines années.
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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 | 0.082 | 0.180 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.013 | 0.013 |
| Scholarly communication | 0.027 | 0.010 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.023 | 0.008 |
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".