Notre agir professionnel de leaders chercheures-praticiennes pour contribuer au bien-être dans une organisation envisagée apprenante
Bibliographic record
Abstract
Notre agir professionnel de leaders chercheures-praticiennes pour contribuer au bien-être dans une organisation envisagée apprenante.Formation et profession 33(3), 2025 • ésumé Actuellement, plusieurs initiatives et outils sont développés et diffusés pour appuyer le bien-être des enseignants et autres personnels scolaires.Or, peu d' écrits détaillent ce que les leaders praticiens et chercheurs croient, veulent et font pour soutenir le bien-être chez eux et autour d' eux.Cet article présente un agir professionnel de leaders chercheures-praticiennes engagées à contribuer au bien-être des enseignants et autres personnels scolaires dans des organisations envisagées apprenantes.Généré par une méthodologie de type « étude de soi », ce construit veut inspirer des actions compétentes, conscientes et systémiques de soutien au bien-être par et pour les leaders praticiens et chercheurs en éducation.
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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 | 0.011 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.006 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.016 | 0.005 |
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".