Modèles de formation et professionnalisation de l’enseignement : analyse critique de tendances nord-américaines
Bibliographic record
Abstract
- L'enseignement nord-américain, primaire et secondaire, fait l'objet d'importantes remises en question. Dans ce contexte, sa professionnalisation est vue par plusieurs comme la meilleure façon de remédier aux faiblesses qui le caractérisent et de donner un nouvel essor à l'école. Dans la poursuite de cet objectif, l'accentuation du caractère professionnel de la formation initiale des enseignants ressort comme une condition nécessaire. Dans le cadre de cette problématique, la présente analyse examine la légitimité de ce mouve- ment de professionnalisation, critique son rapport aux modèles actuels de formation initiale et aux tendances qui émergent de cet univers et, finalement, suggère un profil de composantes pour une pratique professionnelle à venir.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".