L'influence du mode de financement des médias audiovisuels sur le cadrage des campagnes: le cas des élections canadiennes de 2005–2006 et 2008
Bibliographic record
Abstract
Résumé.Une analyse de contenu de la couverture médiatique des campagnes électorales fédérales 2005–2006 et 2008 par des réseaux de radio et de télévision de langue française montre un effet du financement des médias sur le mode de cadrage de la campagne et sur le ton, plus ou moins critique, que les journalistes adoptent vis-à-vis des politiciens et des partis politiques qu'ils couvrent. Ainsi, plus un média est imperméable à la concurrence, plus ses journalistes s'attardent à la couverture des enjeux et plus le ton de leur couverture est descriptif. Nous constatons aussi que c'est lorsqu'ils cadrent la campagne sous l'angle de la course et des stratégies que les journalistes sont les plus critiques à l'endroit des politiciens, par opposition au cadrage orienté vers les enjeux. Abstract.A content analysis of media coverage during the 2005–2006 and 2008 Canadian federal elections by French-language radio and television networks provides evidence of a significant impact of funding mode on campaign framing and journalists' tone towards politicians and political parties. The more a media outlet is shielded from market competition, the more likely are its journalists to frame the campaign through an issue schema and to feature a descriptive tone. We also present evidence that journalists are less descriptive as they cover the campaign through horse-race journalism rather than issue journalism.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".