Bibliographic record
Abstract
The “obesity epidemic” has become one of the most divisive public health issues of our time. It divides us politically by creating factions: those who believe immediate intervention is necessary to scale back obesity rates to pre-millennial levels struggle to communicate productively with those who dismiss governmental measures to combat obesity as attempts to resuscitate the “nanny state.” Those who believe being overweight or obese is the effect of an irresponsible individual’s lifestyle choices give little credence to advocates of a more structural critique emphasizing free market capitalism’s establishment of parameters that compel people—and, in particular, economically disadvantaged people—to make unhealthy choices. In this essay I align with the group of critics who identify as obesity sceptics: scholars who read any ideological formulation of the obesity problem with irreverence and do not concede the veracity of the “epidemic” without some reservation. My focus here is on the rise of a certain state paternalism in response to obesity and what this shift in risk-perception suggests about the role of belief in creating the conditions for a public health emergency. The crucial factor here is potentiality: while dangers are concrete situations that imply a requisite reaction, risks represent emerging and often contentious threats. Rather than reducing the politics of obesity-fighting projects to cost/benefit analysis, then, I seek to find a language that re-opens the problem and complicates the diverse assumptions about individuals, structures and temporalities informing the epidemiology of obesity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.082 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.004 | 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".