Le b.a.-ba des REL : Pourquoi partager ses ressources?
Bibliographic record
Abstract
Objectifs : Distinguer les caractéristiques d’une REL d’une ressource propriétaire, s’initier aux modalités et aux différentes options de partage de ses ressources. Description : Premier d’une série de deux courts webinaires, cette session permettra aux personnes participantes de biens comprendre ce qu’est une REL, ses caractéristiques inhérentes, ses avantages et ses enjeux. Il sera aussi question des principes en appui aux REL et aux différentes. Plusieurs exemples de REL seront mis de l’avant. Une grande partie de la session sera consacrée pour échanger avec les personnes participantes et répondre aux questions. Public cible : Toutes personnes qui souhaitent s’informer sur les REL en vue de soumettre un projet dans le cadre de la sollicitation de la fabriqueREL 2026 Documents relatifs à cette sollicitation : https://fabriquerel.org/financement/#1757532861844-4ed1238e-6a73
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.127 | 0.083 |
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".