Les défis de la formation initiale des enseignants du primaire en milieu à risque
Bibliographic record
Abstract
Les enseignants débutant leur carrière dans les écoles accueillant une forte proportion d’élèves issus des milieux à risque (MR) seraient plus susceptibles d’abandonner la profession. La formation initiale qui leur est offerte ne leur permet possiblement pas de s’adapter au contexte d’enseignement en MR. Grâce à trois entrevues réalisées auprès de 21 futurs enseignants en stage et de leurs 21 enseignants associés, cette recherche a permis d’identifier trois défis de la formation initiale. Ainsi, il serait important pour les futurs enseignants : 1) d’acquérir une compréhension critique de leur cheminement culturel et des limites de ce dernier ; 2) d’intégrer leurs connaissances des particularités des MR dans leurs pratiques ; et 3) de réguler celles-ci au contexte de pauvreté.
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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 | 0.017 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.013 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| 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".