L’éducation aux médias : contributions, pratiques et perspectives de recherche en sciences de la communication
Bibliographic record
Abstract
Cet article1 positionne l’éducation aux médias à la fois comme champ et comme objet d’étude privilégié de mise en application de perspectives critiques en communication et en éducation. À cet égard, il synthétise et problématise le champ théorique de l’éducation aux médias et en identifie les spécificités, les visées et les difficultés. De surcroît, il présente de manière critique le concept de « littératie médiatique », principale proposition théorique de l’éducation aux médias. Finalement, cet article souligne les problématiques associées à ces éléments spécifiques, discute des enjeux actuels liés à l’éducation aux médias et présente des avenues pouvant guider la recherche au cours des prochaines années. Il positionne ainsi l’éducation aux médias au sein des études en communication et fait la démonstration de ses contributions à la discipline.
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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 | 0.053 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".