Cadrage de l’épidémie de Zika de 2016 par la presse canadienne francophone
Bibliographic record
Abstract
Lorsqu’il s’agit de renseignements dépassant le quotidien des citoyens, ces derniers se tournent vers les médias comme principaux pourvoyeurs d’information. La relation de dépendance entre citoyens et médias revêt une délicate importance lorsqu’il s’agit de situations d’exception comme les épidémies. Nous nous intéressons à la manière dont la presse canadienne francophone a couvert et cadré l’épidémie de Zika de 2016 en nous inspirant des théories du cadrage. Dans cette étude exploratoire et qualitative, nous avons analysé 45 articles publiés entre octobre 2015 et mai 2017. Notre analyse thématique a permis d’identifier quatre cadres : le cadre informationnel, le cadre émotionnel, le cadre institutionnel et le cadre moralisateur. L’analyse a révélé que le cadrage de l’épidémie de Zika a été un cadrage en mouvance qui est passé du caractère thématique au caractère épisodique au fur et à mesure que l’épidémie a évolué.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".