Falling in line: news media and public health response during the 2009 H1N1 outbreak in Canada
Bibliographic record
Abstract
I show how the high profile media story of a pandemic outbreak was a product of active societal agents and forces that fed off each other to shape, generate and exploit crises. Using media articles and interviews with public health leaders, public relations practitioners, and journalists who covered the 2009 H1N1 story in Canada, I combine media and communications studies, cultural studies and science and technology studies to explore how relevant social actors–in this case members of the media and public health officials–constructed the H1N1 pandemic as a public health crisis. I argue that the media used the circumstance of widespread health danger to invigorate their role and importance in the public sphere, to produce what they saw as sound discourse for the public, whom they believed were eager for balanced and objective information. In doing so, the media promoted the idea of their audiences being bound together in bodily risk, and I argue that this tendency encouraged the idea of “biological citizenship” to create value in their news stories. I also contend that public health officials set up critical limitations by failing to recognize the news media as a complex mediator. The media response included strong elements of this failure. Public health and state leaders thought that journalists and their work could be controlled, yet had few tools to constrain the media or transmit a version of their messages. A breakdown in the management of the crisis ensued. Finally, I describe how public health and the media furthered a discourse that an ill and endangered body could be made resilient by a restored nation. This argument came to life with the H1N1 pandemic, when stability hinged on a nationalist frame and the population was invited to find comfort by sharing ways to reduce the global and invisible threat. I conclude by suggesting alternative approaches to the use of the news media in public health work, arguing that the extreme volume and intensity of media coverage during this outbreak should act as the catalyst for new thinking and approaches
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.029 | 0.009 |
| Scholarly communication | 0.015 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".