Apprendre à différencier les pratiques d’enseignement au secondaire autour d’un objet transversal : la lecture
Bibliographic record
Abstract
Dans un contexte d’enseignement secondaire québécois où la diversité des élèves pose un nouveau défi, des enseignants se sont engagés dans une démarche de recherche-action-formation (RAF) pour apprendre à différencier leurs pratiques. La lecture étant une compétence mobilisée dans toutes les disciplines, les protagonistes s’entendent pour travailler autour de cet objet de savoir transversal. Le texte présente les résultats de la première année de ce projet. Ces derniers suggèrent que le modèle américain Reading Apprenticeship (Schoenbach, Greenleaf, Cziko et Hurwitz, 1999) facilite les expérimentations et une certaine distanciation des enseignants par rapport à une vision plus traditionnelle et transmissive de l’enseignement.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".