Comment accompagner des leaders scolaires engagés dans une recherche-action : création d’un modèle d’agir compétent et conscient d’accompagnement en psychopédagogie du bien-être
Bibliographic record
Abstract
Une préoccupation pour le bien-être des élèves et du personnel scolaire est de plus en plus présente dans les organisations. Qu’en est-il cependant du bien-être des leaders scolaires qui soutiennent leur équipe au quotidien? Comment les accompagner dans la recherche et le maintien de leur bien-être? Dans le cadre d’une recherche-action effectuée dans un centre de services scolaire, nous avons accompagné des leaders engagés dans un projet de développement professionnel visant le maintien ou l’amélioration de leur bien-être en contexte de changement continu. Cet article présente le modèle d’agir compétent et conscient d’accompagnement en psychopédagogie du bien-être déployé et formalisé auprès d’eux.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.034 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.024 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".