Resisting biopedagogies of obesity in a problem population: understandings of healthy eating and healthy weight in a Newfoundland and Labrador community
Bibliographic record
Abstract
High rates of obesity in Newfoundland and Labrador, Canada’s eastern-most province, have helped to position Newfoundlanders as a ‘problem population’ within discourses of the Canadian obesity ‘epidemic.’ As such, biopedagogies of obesity have been deployed by public health offices in the province, through which Newfoundlanders are imagined as unhealthy eaters who are unknowledgeable about healthy eating, a narrative which aligns with older classist stereotypes about Newfoundland as ubiquitously poor, its population uneducated, backward, and naïve. A qualitative study with 28 participants (and a total of 54 interviews) from St. John’s, the urban center of Newfoundland and Labrador, however, not only revealed a group of people quite knowledgeable about and invested in biopedagogies of healthy eating and healthy weights as propagated by public health discourse, but who also resisted them through alternative understanding of healthy foodways. Results of this study therefore contribute to critical obesity scholarship, as they interrupt assumptions that position populations with high obesity rates as unknowing and uncaring about healthy eating and body weight, demonstrate the ways in which a population might resist biopedagogies of obesity, and highlight the need for research disrupting universalist stereotypes about ‘problem populations’ and their health behaviors.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.023 | 0.037 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| 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".