Contribution critique au développement des programmes d’études : compétences, constructivisme et interdisciplinarité
Bibliographic record
Abstract
Se basant sur l’appui méthodologique offert aux concepteurs de programmes d’études et sur plusieurs analyses récentes, les auteurs examinent les difficultés liées à l’utilisation du concept de compétences. Ils les analysent et proposent un recadrage théorique d’une logique de compétences pour les programmes d’études. Montrant l’insuffisance théorique du concept de compétence et soulignant la rareté des recherches relatives à ce concept, les auteurs décrivent les ambiguïtés auxquelles sont confrontés les rédacteurs des programmes d’études. À ce jour, l’imprécision du concept de compétence n’en facilite pas une utilisation adéquate comme organisateur de programmes. Le recours hâtif à ce concept au moment du développement curriculaire provoque une série d’incohérences, alors même que le concept de compétence demeure fécond.
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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 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.003 | 0.024 |
| Scholarly communication | 0.015 | 0.009 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".