Du fact-checking au debunking: un cadrage médiatique en question. L'exemple d'une campagne européenne de lutte contre la désinformation alimentaire
Bibliographic record
Abstract
Contexte : Cet article examine les enjeux médiatiques et démocratiques liés aux pratiques de fact-checking et de debunking à travers l’analyse d’une campagne menée par l’European Digital Media Observatory (EDMO), à la suite de la décision de l’Union européenne d’autoriser les insectes dans l’alimentation humaine. Analyse : En adoptant une approche qualitative, l’étude explore les réactions des publics sur les réseaux sociaux, les contenus de debunking et les discours métajournalistiques diffusés par trois médias du réseau EDMO. Conclusions et implications : Les résultats suggèrent certaines limites dans la mise en œuvre des méthodologies de vérification, lesquelles sont replacées dans le contexte des tensions structurelles qui traversent les pratiques de debunking.
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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 | 0.022 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.009 | 0.027 |
| Scholarly communication | 0.012 | 0.016 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".