Développement participatif d’un référentiel de compétences pour favoriser l’utilisation de la recherche en éducation : une analyse critique
Bibliographic record
Abstract
Cet article decrit la demarche d’elaboration d’un referentiel de competences visant a favoriser l’utilisation des connaissances issues de la recherche en education (CIR). Cette demarche visait a identifier les competences des intervenants scolaires afin de favoriser l’utilisation des connaissances issues de la recherche dans leur ecole. Une analyse critique de cette demarche en regard des ecrits scientifiques portant sur de telles demarches est aussi presentee. Cette analyse permet de constater que la demarche menee presente plusieurs forces, mais aussi quelques limites. Les principales forces de cette demarche resident dans le fait qu’elle a permis de tenir compte du contexte organisationnel, d’aller au-dela du statuquo pour decrire ce qui est, mais aussi ce qui devrait etre, et cela dans un langage et un format faciles d’approche. Toutefois, l’analyse mene au constat que la demarche n’a pas permis le developpement d’indicateurs permettant de juger du niveau de competence des professionnels cibles.
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 | 0.069 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.008 | 0.015 |
| Scholarly communication | 0.020 | 0.013 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".