Bibliographic record
Abstract
During the Covid-19 pandemic, the fat body was caught up in a complicated logics of life and death in the North American context, where “obesity” was regarded as an “underlying condition” for greater risk of severe disease and death from Covid. As such, bodies with high BMIs were refused ICU ventilator care in certain jurisdictions, which fat activists classified as eugenics. At the same time, some vaccination campaigns prioritized people with higher BMIs for scarcely available Covid vaccinations, also on the basis of fat bodies’ higher risk status for Covid death. This paper explores the seeming tension between two articulations of fat during the Covid pandemic, whereby fat bodies were simultaneously worthy of life and of death in the same moment. Using MBembe’s conceptualization of necropolitics, which draws out and expands upon Foucault’s notion of biopolitics, I argue that the two perspectives on fatness operated in tandem, within an overall temporal shift in classification of obesity; from that of a risk factor for eventual death to that of an emergent threat. Such a temporal shift, I argue, relied on well-worn eugenic patterns in Canada, through which “normative” white bodies were prioritized for life through a complex necropolitical practice by which fat bodies were both made live and let die.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".