Des incertitudes socio-épistémiques aux incertitudes professionnelles
Bibliographic record
Abstract
Nous soumettons dans cet article l’hypothèse selon laquelle les Questions Socialement Vives sont sources de déstabilisation pour les enseignants, en raison de la visibilité qu’elles donnent à des incertitudes propres aux controverses sciences-sociétés. À l’aide d’une méthode socio-anthropologique, nous avons analysé des discussions avec 4 enseignantes-stagiaires de Sciences et Vie de la Terre afin de caractériser leurs rapports aux incertitudes de la question de la transition agroécologique et la façon dont cela influence l’idée qu’elles se font de leur rôle. Des phénomènes de saturation informationnelle et de perception contraignante des incertitudes ont ainsi été caractérisés, avec des implications fortes sur leur façon d’envisager leur enseignement. Nous nous interrogeons alors sur la capacité de la formation professionnelle des enseignants à favoriser l’émergence d’une éducation aux incertitudes au sein des classes de sciences.
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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 | 0.028 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.007 | 0.046 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.005 |
| 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".