École inclusive : Analyse didactique d’un dispositif d’autorégulation (DAR)
Bibliographic record
Abstract
En 2018, en France, un Dispositif d’AutoRégulation (DAR), inclusif, novateur et expérimental voit le jour. Il fait coopérer, au sein d’une école, une équipe médicosociale et enseignante, dont une enseignante en surnuméraire. Un cahier des charges (2021, 2024) explicite sa double visée : la scolarisation d’enfants avec autisme, à temps complet, dans les classes dites ordinaires et la mise en place de principes d’autorégulation pour tous les élèves (avec ou sans autisme). Cet article, fondé sur un cadre théorique didactique, tente de comprendre comment, dans une école, est organisé le DAR et ce que font, dans leurs pratiques, une enseignante en surnuméraire et une enseignante d’une classe.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".