'Superbugs' and the 'Dirty Hospital': The Social Co-Production of Public Health Risks
Bibliographic record
Abstract
This dissertation examines the construction of antimicrobial resistance (AMR) as a public health risk. Its focus is on how AMR is co-produced among a network of medical professionals, scientists, and science journalists. The research advances three main arguments: first, narratives and definitions of health risk are not absolute or fixed, but constituted in the discourses and practices of global, national and local actors; second, the production of knowledge about the risk of AMR is not based on a linear process but one in which various definitions, interests, and practices are involved, and influence one another; and third, conceptualizing health risk as discursive co-production provides a more robust and nuanced understanding of how risks are defined and understood by stakeholders, particularly in relation to attributions of responsibility, blame, victimhood, and resource allocation. I argue that this represents a novel way of imagining and conceptualizing risk communication. The research involved the development of a novel methodology, which I call ethnography of risk, that brings together hospital ethnography, in-depth interviews, and qualitative analysis of media coverage and policy documents. The results of this study show that health risks are co-produced through processes of negotiation between different and co-existing types of knowledge, including situational and embodied experience, emotional memory, and expert assessments. Second, it argues that risks are multifaceted and constituted at the intersection of different perspectives, such that AMR is understood and addressed as a personal risk, a professional risk, a global risk, and a political risk.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".