Est-ce que les ressources éducatives libres (REL) peuvent nous faire sauver du temps?
Bibliographic record
Abstract
Cette présentation tente d'abord d'expliquer ce qu'est une ressource éducative libre (REL) et en quoi consiste le mouvement de l'éducation ouverte. On analyse ensuite ensemble la question du temps accordé à la création de nos ressources éducatives : Est-ce qu'une REL prend plus de temps à créer qu'une ressource éducative standard? Qu'est-ce qui demande du temps dans la création de ressources éducatives? Est-ce que le temps, c’est de l’argent, et si oui, qui encaisse? Qu’en est-il du temps versus la qualité des ressources produites? Qu'entend-on par document "vivant" quand on parle de REL? Au final, est-ce que la question du temps est la bonne question à se poser? Enfin, le webinaire nous permet de prendre connaissance de la perspective sur les REL et le temps de 2 personnes créatrices de REL à l'Université Laval, la professeure titulaire à la Faculté de droit, Marie-Claire Belleau, et le conseiller en pédagogie universitaire Jean-François Proteau.
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 | 0.011 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.028 | 0.025 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.036 | 0.013 |
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".