L’explicitation et la reconstruction des arguments pratiques dans l’enseignement
Bibliographic record
Abstract
Les auteurs examinent s’il y a moyen d’élargir la capacité de l’enseignante ou de l’enseignant à faire un usage réfléchi de la recherche et de l’enseignement, en se fondant sur une conception normative de ce qu’une éducation significative et humaine peut être. L’argument pratique (cette notion remonte à Aristote) est décrit ici en termes de stratégie de formation auprès des enseignants, pour les aider à articuler et à reconstruire leurs convictions relatives à leur propre action en classe, de sorte qu’ils puissent décider de modifier leur raisonnement pratique et leurs pratiques subséquentes. Une étude de cas est alors présentée, celle d’une enseignante qui a participé à ce processus d’explication d’arguments pratiques. Les auteurs en induisent que ce processus d’explication permet aux enseignants de mieux contrôler leurs justifications personnelles et, ainsi, d’agir de façon plus responsable.
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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 | 0.027 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.048 |
| Scholarly communication | 0.020 | 0.021 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".