Le cadrage politique et l’ethos de Justin Trudeau sur Instagram : un storytelling héroïque entre émotion et celebrity politics
Bibliographic record
Abstract
Face à l’omniprésence croissante de certains acteurs politiques sur Instagram, il semble essentiel d’étudier son usage par les leaders politiques. Cet article explore la façon dont les conseillers en communication ont cadré l’ethos de Justin Trudeau sur Instagram durant sa campagne et les 100 premiers jours de son gouvernement. Dans cette optique, une méthode largement quantitative fut adoptée, consistant en une analyse de contenu catégorielle, soutenue par l’analyse de discours. Les résultats obtenus lèvent le voile sur un ethos axé sur l’authenticité, la compassion et la compétence. Justin Trudeau est présenté grâce aux stratégies reposant sur le recours au pathos et au celebrity politics, cela à travers un storytelling héroïque et positif.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".