L’implication des détenteurs d’enjeux (stakeholders) au sein de la démarche d’évaluation de programme: problème et/ou solution ?
Bibliographic record
Abstract
Considérant le peu d’articles publiés dans la revue Mesure et évaluation en éducation qui portent sur l’évaluation de programme, nous avons décidé de traiter de l’implication des détenteurs d’enjeux, compte tenu de l’intérêt qu’elle soulève. La première partie de l’article développe cette thématique, ce qui permet de distinguer deux problèmes, soit la sélection des participants et les modalités d’inclusion. La seconde partie consiste en une analyse des articles pertinents dans la revue en vue d’apporter des éléments de solution. Ainsi, si l’implication des détenteurs d’enjeux s’avère une solution que personne ne nie afin d’assurer la crédibilité de la démarche évaluative, il n’en demeure pas moins que son application pose plusieurs problèmes, ce qui nous amène à conclure qu’elle constitue certes une solution et aussi un problème.
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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 | 0.151 | 0.178 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.006 | 0.017 |
| Scholarly communication | 0.024 | 0.026 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".