Naviguez dans l'océan des REL : astuces et stratégies de recherche
Bibliographic record
Abstract
Naviguez dans l'océan des REL : astuces et stratégies de recherche ! Cette présentation, réalisée en partenariat avec la fabriqueREL, Nantes Université et l’Université catholique de Louvain, s’inscrit dans le cadre du Mois de l’éducation ouverte. Ces trois institutions sont engagées dans le développement et la promotion des ressources éducatives libres afin de favoriser un accès ouvert au savoir. L’éducation ouverte repose sur l’accès libre au savoir grâce aux ressources éducatives libres (REL), qui incluent des manuels, cours et exercices. Ces ressources sont disponibles sur des plateformes de dépôt ou de diffusion. Cette présentation propose un tour d’horizon de ces outils tout en offrant des conseils pratiques pour les utiliser efficacement. Pour citer : Bozio, E., Depoterre, S., Escobar, M., Roy, M. et Villeneuve, N. (2025, mars). Naviguez dans l’univers des REL : astuces et stratégie de recherche ! [présentation]. fabriqueREL, sous licence CC BY 4.0
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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 | 0.040 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.015 | 0.028 |
| Scholarly communication | 0.030 | 0.038 |
| Open science | 0.003 | 0.025 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.019 | 0.007 |
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".