Changements climatiques à l'école : Pour une éducation sociopolitique aux sciences et à l'environnement
Bibliographic record
Abstract
Dans cet article, nous examinons la question de l'intégration scolaire de controverses socioscientifiques qui mobilisent et divisent scientifiques, experts, citoyens, et qui participent à la configuration de la société. Leur nature politique est peu examinée comme telle dans les recherches en éducation et en classe. Nous présentons une recherche empirique menée en collaboration interdisciplinaire avec des enseignants de sciences et de philosophie et portant sur l'étude, par des élèves de l'enseignement secondaire technologique français, des controverses sur les changements climatiques. Les résultats indiquent que les élèves s’intéressent à la dimension politique des changements climatiques, aux controverses qu’ils entraînent et aux expertises développées à leur propos. Nous discutons ensuite, d'un point de vue curriculaire, des potentialités de prise en charge de questions politiques en classe de sciences.
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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 0.003 |
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; both teacher heads agree on what is shown here.
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".