Conciliation du leadership des directions d’école en contexte francophone minoritaire et en contexte de diversité ethnoculturelle, linguistique et religieuse
Bibliographic record
Abstract
Les directions d’école font face à de nombreux enjeux en contexte francophone minoritaire au Canada. Dans cet environnement, il va sans dire que les directions d’école doivent exercer un leadership particulier. De plus, une diversité ethnoculturelle, linguistique et religieuse grandissante s’observe dans ces écoles. Dès lors, plusieurs études affirment que les directions d’école doivent aussi exercer un leadership particulier afin de permettre l’inclusion de la diversité ethnoculturelle, linguistique et religieuse. Nous argumentons que les directions des écoles qui se trouvent à la fois en contexte francophone minoritaire et en contexte de diversité ethnoculturelle, linguistique et religieuse doivent, en plus des tâches administratives et de nature pédagogique, concilier leur rôle de valorisation, transmission et protection de la langue française et des cultures francophones et l’inclusion de la diversité ethnoculturelle, linguistique et religieuse. Nous proposons donc une discussion théorique sur la nature du leadership à exercer pour assurer cette conciliation. Nous proposons finalement un modèle théorique de la conciliation du leadership des directions d’école en contexte francophone minoritaire et en contexte de diversité ethnoculturelle, linguistique et religieuse.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".