Un aperçu de la violence et des comportements agressifs auxquels sont confrontées sept directions d’école de langue française au Canada
Bibliographic record
Abstract
Peu de recherches examinent comment les directions d’école au Canada exercent leur leadership pour inclure les élèves ayant des besoins éducatifs particuliers (EBEP). Notre étude a examiné les expériences influençant le leadership de 285 directions d’école dans six provinces. En particulier, cet article a comme objectif d’aider les chercheur.e.s et les praticien.ne.s à mieux comprendre : 1) les incidents critiques en matière d’inclusion scolaire auxquels sont confrontées sept directions d’école de langue française (DELF) du Québec, du Nouveau-Brunswick et de l’Ontario, et 2) comment la violence et les comportements agressifs des EBEP influencent leur leadership. Les données font partie d’une étude pancanadienne bilingue; elles ont été recueillies au moyen d’un questionnaire et d’entrevues semi-dirigées. Les DELF favorisant l’inclusion des EBEP sont celles sachant comment prévenir les effets de la violence et les comportements agressifs sur le climat scolaire et possédant une formation universitaire avancée en matière d’adaptation scolaire en français.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".