Bibliographic record
Abstract
Abstract “Obesity epidemic” discourse relies on scientific and epidemiological research to justify subjecting certain bodies to healthist messaging and disciplinary technologies. The science and framing of obesity has evolved, potentially partially due to critical obesity scholars. However, critical obesity scholars are now reliant on outdated critiques of ever progressing evolving/adapting discourse. Recent discoveries have been made concerning the determinants of obesity and the complexity of weight loss. Scientific discourse often now references environmental factors as contributors to obesity, low levels of sustained weight loss, and limitations of common measures such as the body mass index. Despite this refinement, the ultimate conclusion of studies, as presented within the scientific articles or in related media reportage, remains unchanged. Individuals are still expected to attempt to “correct” their non‐normative bodies through lifestyle changes, regardless of the evidence underscoring the likely futility of these endeavors. This paper updates obesity science and public health responses and evaluates the extent to which new findings, with the potential to greatly subvert standard weight loss advice, have altered supposedly evidence‐based public health communications and recommendations. As emerging scientific insights have further muddied already‐complex obesity‐related pathways, solutions have largely remained the same.
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 | 0.014 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.003 | 0.024 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.005 | 0.008 |
| 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".