Dans un contexte de gestion axée sur les résultats, quels sont les différents moyens que les directions d’établissement utilisent pour soutenir leurs équipes face aux difficultés et aux défis auxquels elles sont confrontées?
Bibliographic record
Abstract
Cette présentation s’inscrit dans un projet de recherche plus large qui visait à identifier les stratégies des directions d’école pour mobiliser les enseignants ainsi que les facilitants et les obstacles à la mobilisation. \nÀ partir des entrevues réalisées avec 25 directions d’école au Québec,nous avons relevé plus particulièrement les pratiques de gestion des ressources humaines des directions d’école pour soutenir les enseignants autour de l’atteinte des objectifs de l’école. \nPar conséquent l’objectif de cette présentation est : \nIdentifier les pratiques de gestion des ressources humaines (GRH); \ndes directions d’école; \npour soutenir les enseignants; \nautour de l'atteinte des objectifs adoptés; \ndans le cadre de la mise en œuvre; \nde la gestion axée sur les résultats (GAR).
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 | 0.024 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.018 |
| Scholarly communication | 0.022 | 0.012 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.020 | 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".