Bibliographic record
Abstract
Recent reconsideration of the history of 20th century obesity research suggests that the etiology1 of obesity has been fundamentally misunderstood or misrepresented (Gard & Wright, 2005; Guthman, 2011; Phinney & Volek, 2011; Taubes, 2002, 2007, 2011, 2016; Teicholz, 2014). The reasons for this are manifold and one is that 20th century obesity research is fraught with bias. There is a temporal overlap between the establishment of the modern theory of obesity and the entrenchment of neoliberalism in Western countries. I posit that the evidence of the influence of neoliberalism is discernible when considering both how the etiology of obesity has been [mis]understood and how the obese are characterized. Further, I argue that neoliberal policy and governance have contributed to increased levels of obesity. Through discourse analysis (Foucault, 1972) and institutional ethnography (Smith, 2005), I consider the ways in which neoliberalism and the social organization of scientific \nknowledge have influenced obesity science. I also identify how the resultant conceptualization of obesity that appears in Canadian public health reports reflects neoliberal ideological bias.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.034 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".