M-XITÉ: MaXimiser l’agentIvité de l’élève et des adulTes qui l’accompagnent pendant le processus NumÉr-actif d’un plan d’intervention.
Bibliographic record
Abstract
Best practices in the development of Individual Education Plans (IEPs) agree that the more involved the student, the more involved the parents, and the more successful the student will be. In doing so, the collaborative practice fostered during the IEP cycle must maximize the active participation of the student and his or her parents. Although these recommendations are not new, their implementation remains challenging. Thus, this article presents the results of an intersectoral collaborative exploratory research study that we have entitled M-XITY. This study follows the initiative of field actors in a school wishing to strengthen active student participation through a more collaborative practice during the IEP cycle. Thus, a Numer-Active IEP device and a selection of collaborative practice skills to be mobilized during the IEP cycle show how student agentivity can be fostered. These two strategies presented in this article will be tested jointly during the second year of the project. They aim at improvement in various aspects as well as the recognition and urgency of, for example: using the IEP as a pedagogical rather than administrative tool; developing practices and strategies that allow the student to demonstrate self-determination and agenticity during the phases of the IEP cycle; and strengthening collaborative practices between the student, parents, and school and health and social service providers
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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 | 0.004 | 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 | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".