Explorer les REL : comment libérer pour mieux partager
Bibliographic record
Abstract
*Support de présentation pour animer un atelier, 2e version* Cet atelier permettra aux personnes participantes d’explorer les différentes options de partage de leur matériel pédagogique, en survolant le spectre des licences : du copyright en passant par le libre accès et les ressources éducatives libres. Elles seront aussi invitées à « libérer une ressource » et à discuter des avantages et des points de vigilance à considérer dans le choix d'une licence ouverte. Les personnes participantes pourront découvrir différentes ressources disponibles, ainsi que les services et outils d’accompagnement qui leur sont offerts pour la création de REL. Objectifs de l'atelier : Permettre aux personnes participantes de comprendre ce que sont les REL; Explorer les différentes options de partage (licences) pour leur matériel pédagogique; Libérer une de ses ressources (mains sur les touches).
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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 | 0.005 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.014 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.220 | 0.103 |
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".