A trail leading home. Analysing the evolution of Mpox risk narratives and targets of blame in UK media
Bibliographic record
Abstract
This research is interested in the ways the media ‘holds together’ science, history, and culture in the coverage of a new and frightening disease outbreak. Building on previous studies, which have shown that meanings given to an epidemic take shape within pre-existing geographies of hope and blame, and drawing upon Douglas’ understanding of risk and blame, this article explores the relation between perceived epidemic risk, outbreak narratives and accusation. It analyses how different outbreak risk narratives evolved in UK media articles (n = 227) during the first three months of the 2022 Mpox outbreak. Findings highlight a shifting accusatory dynamic in narrative framings over time. They illustrate that as the epidemic risk was framed as increasing, the most prominent narratives shift from the accusation of distant racialised others to an increasingly ‘proximal blame’ tendency, which initially targets marginalised local groups, followed by societal structures and institutions. We argue that this general trend of meaning-making and blame during epidemics is historically recurring and, considering this chronic reaction to outbreak risk, we conclude by suggesting potential avenues of thought for communication strategies.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".