SOUTENIR ET FAVORISER LA DIVERSITÉ CHEZ LES APPRENANTS: LES ENJEUX DE L’ÉVALUATION DES APPRENTISSAGES EN CONTEXTE D’INCLUSION
Bibliographic record
Abstract
L’article porte un regard critique et rétrospectif sur les recherches présentées dans le cadre de ce numéro spécial de la revue internationale Educativa en vue de répondre de façon globale aux grandes questions soulevées en introduction et, de ce fait, recontextualiser la question de l’évaluation des apprentissages et la nécessité d’innover pour mieux soutenir la diversité chez tous les apprenants comme le suggère le titre du présent numéro. Une analyse transversale de ces recherches ayant à l’esprit les orientations fournies aux auteurs permet, d’une part, de mettre en évidence les principaux résultats et conclusions avancés par les études et, d’autre part, un certain nombre de défis qui émergent des recherches analysées qui suscitent de nombreuses pistes de réflexion pour la mise en œuvre de recherches futures.
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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 | 0.071 | 0.119 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.008 | 0.016 |
| Scholarly communication | 0.021 | 0.017 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".