Formation par concordance de raisonnement pour les directions d’établissement scolaire novices : une recherche-développement
Bibliographic record
Abstract
Cet article présente les résultats d’une recherche-développement appuyée sur un modèle de leadership pédagogique (Hallinger et Wang, 2015). Nous visions à 1) adapter les principes méthodologiques de la formation par concordance (Charlin et Fernandez, 2016), pour offrir une occasion de développement professionnel à des directions d’établissement scolaire novices, 2) documenter leurs perceptions sur la pertinence de ce type de formation et de ses effets sur la pratique et 3) énoncer certaines indications pour son utilisation. Nos résultats montrent que recourir à la formation par concordance pour les directions d’établissement scolaire est possible et pertinent dans le cadre de leur socialisation professionnelle. Pour les personnes formatrices en gestion scolaire, mais aussi dans d’autres champs des sciences de l’éducation ou domaines (comme les sciences de la santé), utiliser ce type d’approche pédagogique est avantageux et facilité par la disponibilité d’un guide pour les accompagner.
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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 | 0.046 | 0.118 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".