The politics of the surgical mask: Challenging the biomedical episteme during a pandemic
Bibliographic record
Abstract
COVID-19 has seen politicians use a selective 'science' to justify restrictions on mobility and association, to mandate the wearing of face masks, and to close public infrastructure. There seems to be no role for health humanities scholars as yet, but perhaps there should be. This paper considers the fate of a health humanities article on surgical mask use that was published in a biomedical journal in 2016. This article, which did not operate from within the biomedical episteme but which was in conversation with the episteme, was misappropriated on both sides of the political spectrum to justify personal beliefs around mask use in the pandemic. This mistaken misappropriation is not only evidence of the utility of the common ground shared between biomedicine and the health humanities, it is also evidence of the possibilities inherent in a future interdisciplinary involving biomedicine and the health humanities.
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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 | 0.071 | 0.075 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.106 |
| Scholarly communication | 0.019 | 0.021 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.019 | 0.021 |
| Insufficient payload (model declined to judge) | 0.002 | 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".