Measles, Mickey, and the Media: Anti-Vaxxers and Health Risk Narratives during the 2015 Disneyland Outbreak
Bibliographic record
Abstract
Background Outbreaks of disease are common fodder for political debate and public discourse. In the past decade alone, health officials have faced a steady stream of serious public health threats, from H1N1 to Ebola and Zika, as well as large outbreaks of measles and other highly contagious illnesses. These incidents command intense media attention and focus public conversation around questions of risk and responsibility. Analysis This article examines major frames in Canadian news coverage of the Disneyland measles outbreak in 2015 to show how public health events are translated into social problems that magnify moral and political concerns. It discusses how parents who reject or express worries about vaccination were portrayed, and traces which solutions were presented to address the problem of vaccine preventable illness. Conclusion and implications Media coverage focused heavily on “anti-vaxxers” as central characters in the outbreak story. The coverage conformed largely to an established biomedical narrative, in which medical and health experts set the definitional parameters around the outbreak causes and consequences, and the preventive measures that should be taken to prevent future occurrences.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.023 | 0.017 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".