LITTÉRATIE MÉDIATIQUE À L’ÉCOLE ET MODÉLISATION DIDACTIQUE : QUELLES PRÉOCCUPATIONS COMMUNES POUR LA RECHERCHE ET POUR L’ENSEIGNEMENT ?
Bibliographic record
Abstract
Cette contribution propose d’étudier les problèmes se posant aux chercheurs qui conçoivent des tâches, simples et complexes, destinées à évaluer les savoirs et savoir-faire relatifs à la littératie médiatique multimodale d’élèves du secondaire (13-15 ans) en contexte de recherche d’information et de production d’un hypertexte multimodal. Ces problèmes amènent par ailleurs toute une réflexion sur la modélisation didactique de la lecture et sur la production de textes numériques et leurs enseignables, à partir d’une tentative de reconstitution de ce qui pose ou peut poser problème pour l’élève. Les interrogations soulevées dans cet article ont émergé dans le cadre d’un programme de recherche collaboratif international (2018-2022) entre quatre pays francophones (Belgique, Canada, France et Suisse) portant sur l’évaluation des compétences en littératie médiatique d’adolescents (13-15 ans) de chacun de ces pays.
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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 | 0.017 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.018 |
| Scholarly communication | 0.018 | 0.014 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 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".