Connaissances métacognitives et stratégies d’autorégulation impliquées dans la révision de textes : construction et validation d’un instrument autorapporté
Bibliographic record
Abstract
La présente recherche a pour but de construire et de valider le Questionnaire des composantes métacognitives de la révision de textes (QCMR) en contexte universitaire. Au total, 451 étudiants de licence (trois premières années à l’université) appartenant à plusieurs domaines d’étude dans une université française ont participé à l’étude. Après une version initiale du questionnaire, qui comportait 41 items, une analyse factorielle exploratoire a révélé 5 facteurs correspondant aux dimensions théoriques envisagées. Une analyse factorielle confirmatoire a validé cette structure, qui mesure les variables suivantes : connaissances métacognitives déclaratives, gestion du temps, autosurveillance, connaissances métacognitives procédurales et recherche d’aide. Un bon niveau de fiabilité a été constaté : des coefficients alpha allant de 0,74 à 0,83 ainsi qu’un degré satisfaisant de stabilité temporelle. Le QCMR possède également une bonne validité prédictive.
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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 | 0.017 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".