Comment l’ecriture des rapports d’autoevaluation de stage des futurs enseignants evolue-t-elle en fonction de leur profil?
Bibliographic record
Abstract
Dans cet article, nous nous sommes intéressés à l’évolution de l’écriture des futurs enseignants dans leurs rapports d’autoévaluation de stage. Spécifiquement, nous nous sommes demandé (1) de quoi parlent les étudiants dans ces rapports, (2) comment leur écriture évolue au fil de la formation et (3) si des indicateurs de réflexivité sont présents dans ces écrits. Pour y répondre, nous avons analysé tous les rapports d’autoévaluation que six étudiants ont produits pendant leur formation. L’analyse de contenu opérée a permis de mettre en évidence des liens entre la progression de l’écriture et l’écriture réflexive de l’étudiant et sa progression de stage en stage.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.018 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".