Bibliographic record
Abstract
Plusieurs chercheurs et observateurs du monde de l’administration de l’éducation ont décrit les champs de compétences identifiés chez les directions d’école performantes. Les publications depuis une vingtaine d’années convergent en général dans leurs descriptions de ce qui, d’après eux, faisait qu’une direction réussissait à mobiliser le personnel de l’école – les enseignants surtout – en vue de la réussite des élèves. On pourrait synthétiser toutes ces descriptions de la façon suivante. Le sens de l’organisation représente la base sur laquelle est bâtie la compétence du directeur. Sa vision embrasse l’ensemble de ses actions. Son sens politique lui permet de réconcilier l’autorité, le milieu et le personnel. Pour arriver à harnacher toutes les énergies de son personnel, il lui faudra ses qualités en relations humaines, son leadership et son habilité de communicateur. Centrales dans toutes les manifestations de ses compétences, sa capacité de prise de décision et son aptitude à vivre avec ses décisions constituent le coeur de sa fonction.
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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.027 | 0.008 |
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".