Comment les recherches en didactique de l’histoire construisent-elles l’élève ?
Bibliographic record
Abstract
En didactique de l’histoire, le processus de construction de l’élève reste implicite. Cet article en vise une explicitation et une réflexion sur les catégorisations à l’oeuvre à partir d’une analyse textuelle de 89 publications (1991-2018). Sur le modèle de la contribution de Daunay et Fluckiger (2011), les discours ont été classés selon trois catégories : sujet scolaire, sujet épistémique, sujet social, complétées de celle du sujet politique. Dans le corpus analysé apparait nettement la dominante du sujet épistémique, construit plus ou moins en contraste avec le sujet scolaire, en connexion au sujet politique et influençant la façon dont est envisagé le sujet social. Rien d’étonnant à cela pour des recherches en didactique, centrées sur la discipline, mais cette construction se fait aux dépens de certains questionnements, mentionnés dans le fil de cet article.
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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 | 0.023 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.005 | 0.023 |
| Scholarly communication | 0.017 | 0.019 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 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; 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".