[Compte-rendu de :] Granger, N., Portelance, L., & Messier, G. (Éds.). (2020). Planifier son enseignement au secondaire. Montréal (Québec): JFD Editions
Bibliographic record
Abstract
Cet ouvrage collectif regroupe huit chapitres qui dessinent les principes, les processus et les enjeux de la planification de l'enseignement pour le secondaire, cette dernière indispensable à un enseignement plus équitable pour tous les élèves.Dans le premier chapitre, Léna Bergeron et Geneviève Bergeron (Université du Québec à Trois-Rivières) mettent en évidence l'importance d'avoir un fil conducteur comme « le fil d'Ariane » dans la planification à long terme, et ne plus être constamment cantonné en tant qu'enseignant dans la planification « au jour le jour ».Ce qui est essentiel mais à la fois très difficile pour l'enseignant, comme professionnel, c'est de savoir identifier précisément ses intentions d'apprentissage vis-à-vis de l'élève.Pour l'enseignant, il s'agit d'agencer un « alignement curriculaire », « une planification à rebours » et prioriser les notions
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.014 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.182 | 0.105 |
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".