La technologie prescrite à l’école en France et au Québec
Bibliographic record
Abstract
Dans une perspective de didactique curriculaire, l’investigation présente les transformations de l’éducation technologique en France et au Québec et les points majeurs du mouvement de refondation de cette discipline scolaire et de recomposition du curriculum dans lequel elle s’inscrit. Les implications sur les contenus et les pratiques d’enseignement sont examinées. L’analyse menée principalement à partir des textes prescriptifs, adossée aux recherches disponibles, révèle les influences des tendances internationales des évolutions de cet enseignement dans les deux systèmes éducatifs. Elle permet de mettre en évidence les similitudes et les contrastes de cet enseignement et de ses évolutions récentes, notamment l’équilibre délicat entre connaissances et compétences, la fragilité des contenus disciplinaires prescrits, l’incomplétude ou l’inachèvement des réformes contemporaines et les particularités d’une tradition disciplinaire française comparativement à une tradition pédagogique québécoise.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".