Écriture réflexive et subjectivation de savoirs chez les futurs enseignants
Bibliographic record
Abstract
L’article présente une intervention auprès d’instituteurs en formation initiale visant à développer leur maîtrise et leurs représentations de la lecture-écriture dans le sens de la complexité, à partir d’interactions sociales et de démarches d’écriture réflexive. Une analyse ultérieure des textes réflexifs a permis de dégager des pistes pour décrire des démarches de subjectivation de savoirs didactiques. Elle montre l’importance des tensions vécues par les étudiants au fil des tâches en tant que constitutives du processus par lequel chacun peut parvenir à décliner les savoirs en « je » pour les incorporer dans son projet professionnel. L’identification d’opérations mentales et langagières au fil des discours écrits met également en évidence cinq types de parcours réflexifs menant de manière variable à la subjectivation.
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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 | 0.011 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.013 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".