Cartographier en 3D avec MapIt : une plus-value pour un parcours de professionnalisation selon la perspective étudiante
Bibliographic record
Abstract
Énoncé des implications de la recherche Durant la pandémie, l’application MapIt a été intégrée dans un programme d’ergothérapie pour soutenir l’apprentissage à distance de l’adaptation de l’environnement bâti. MapIt permet de cartographier des pièces d’un domicile, puis d’en générer un modèle en 3D pour la visualisation et la prise de mesures virtuelles. Les étudiantes expriment que le recours à MapIt durant leur formation mène à incarner les rôles attendus d’une ergothérapeute. Pour inspirer d’autres bonnes idées pédagogiques, cet article présente comment MapIt peut soutenir l’apprentissage en situations authentiques, un élément clé d’un parcours de professionnalisation, s’approchant des réalités vécues par les personnes patientes, clientes ou intervenantes
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.025 | 0.005 |
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".