Contextes de formation formel, non formel ou informel: développement de compétences de direction d’école de langue française au Canada
Bibliographic record
Abstract
Pour parfaire les compétences des nouvelles directions d’école, nous constatons l’émergence de programmes de formation proposés par des universités, des districts scolaires, etc. Le but de notre étude est d’identifier les contextes de formation formel, non formel ou informel qui ont le plus aidé les nouvelles directions d’école dans le développement de leurs compétences d’une part, et ceux qui seraient mieux à même de les aider à l’avenir. Dans le cadre de cette recherche qualitative, 101 acteurs-trices de l’éducation ont été interrogé-es. Les résultats montrent que les trois contextes de formation (formel, non formel et informel) semblent avoir contribué au développement des compétences des nouvelles directions, alors que le contexte non formel, et plus particulièrement les ateliers et le soutien du district scolaire, s’avère être celui pouvant le plus aider les nouvelles directions à développer leurs compétences dans le futur.
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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".