Élaboration du Gabarit d’évaluation de l’environnement des programmes : le cas d’une unité universitaire de formation continue au Québec
Bibliographic record
Abstract
L’évaluation du contexte professionnel dans lequel s’inscrit un programme universitaire demeure un phénomène peu couvert par la littérature en éducation. Il n’en demeure pas moins que la pertinence sociale des programmes fait partie des critères d’évaluation au Québec (CREPUQ, 2004). Qu’en est-il des unités universitaires de formation continue qui offrent des formations professionnalisantes à des étudiants aux modes d’apprentissage non traditionnels ? Quel est le rôle du conseiller en évaluation de programme au sein de ces unités ? La démarche scientifique ayant servi d’assise à la conception d’un gabarit d’évaluation de l’environnement des programmes sera ici justifiée (Rossi, Lipsey et Freeman, 2003 ; Nadeau, 1988 ; Jorro, 2009 ; Scriven, 1996 ; Newcomer, Hatry et Wholey, 2015). Cet outil vise l’aide à la prise de décision (Dubois et Marceau, 2005) et à l’amélioration continue des programmes par voie d’une évaluation formative (Scriven, 1996 1 ; Rossi et al., 2003 ; Newcomer et al., 2015). Il sera également fait mention des critères d’évaluation retenus afin de mesurer l’écart entre la formation offerte et les attentes des milieux (savoir, savoir-faire, savoir-être, savoir-devenir et normes professionnelles).
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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 | 0.035 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.014 | 0.019 |
| Scholarly communication | 0.017 | 0.006 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 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".