La fabrique d’un héros déchu. Le Dr Arruda dans les médias québécois pendant la COVID-19
Bibliographic record
Abstract
OBJECTIVES: This research focuses on the process of heroization during epidemics. It analyzes the evolution of the media portrayal of Dr. Arruda, Quebec's former national director of public health, during the COVID-19 pandemic to identify the type of spokesperson valued and devalued at different moments during the virus outbreak. METHOD: We analyzed 728 articles published by five Quebec media outlets: Le Devoir, La Presse, Le Journal de Québec, Le Journal de Montréal, and 24 Heures. The data then underwent a rhetorical frame analysis, which identified fourteen heroization arguments and fourteen blame arguments, taking shape within four broader argumentative logics. RESULTS: Dr. Arruda was transformed into a fallen hero in a five-stage process: 1) heroization; 2) doubt; 3) accumulation of criticism; 4) minor acts of redemption; and 5) confirmation of the fallen hero status. The analysis illustrates the sinusoidal path during which Dr. Arruda was heroized and criticized several times over. It also shows how the same arguments were used at the beginning of the outbreak to heroize Dr. Arruda and, later, to criticize him. CONCLUSION: This research shows that media-exposed spokespeople must adapt to the public's changing needs relating to heroes. It also highlights the difficulty of occupying a highly visible position as public health director during a health crisis. Finally, this research draws insights on how to manage communications in times of health crises.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.010 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".