L’évaluation dynamique en littératie : collaborer pour soutenir la formation des personnes étudiantes en orthopédagogie en contexte clinique
Bibliographic record
Abstract
Face aux défis engendrés par le nombre grandissant d’élèves en situation de handicap ou en difficulté d’adaptation ou d’apprentissage (MEQ, 2023) et la complexité des difficultés en lecture (Ukrainetz, 2015), il apparaît essentiel de revoir les méthodes d’évaluation en orthopédagogie. L’évaluation dynamique en littératie offre une approche novatrice pour soutenir la formation des personnes étudiantes en contexte clinique. La présente recherche explore son importance pour l’évaluation orthopédagogique, soulignant ses avantages dans la compréhension des capacités d’apprentissage des élèves (Aldama, 2022). Cette recherche vise à souligner son potentiel pour enrichir les pratiques orthopédagogiques et favoriser la réussite en littératie des élèves en difficulté.
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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 | 0.033 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".