<i>‘Enlightened ones who think they’re smarter than decades of research.’</i> Emotional-discursive analysis of epidemic narratives during the 2024 Montreal measles outbreak
Bibliographic record
Abstract
This research explores the emotionality of narratives on outbreaks of vaccine-preventable diseases following the COVID-19 pandemic. It relies on an emotional-discursive approach interested in outbreak narratives’ emotion orientations, and focuses more specifically on narratives’ potential to (re)direct conduct through the use of emotional notions. Taking the 2024 Montréal measles outbreak as a case study, we relied on a joint methodology combining the analysis of media items (n = 65) and social media conversations (n = 545 Reddit comments). Narrative and emotional-discursive analyses of the data identified four outbreak narratives, each describing the outbreak by relying on emotional notions promoting ways to (re)direct conducts: 1) an epidemiological narrative supported by the emotional notion of re-emerging disease and promoting acts of collaboration; 2) a vaccine hesitancy denunciation narrative supported by notions of anti-science and neglected children and promoting confrontation, exclusion and education; 3) a post-pandemic narrative supported by the emotional notion of COVID-19 and promoting preparation; and 4) a globalisation narrative supported by the notion of a borderless world and promoting self-protection. Our research points to the emotional aspects of perspectives on vaccination by exposing diverse emotional notions which constitute the discursive landscape surrounding vaccination, and by showing various emotional modes of (re)directing vaccine-related conduct. We argue that polarising discourses on vaccination prominent during the height of the COVID-19 outbreak have only slightly changed since the pandemic. The emotional-discursive complexity of the issue of vaccination is discussed.
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.016 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".