Being ‘thick’ indicates you are eating, you are healthy and you have an attractive body shape: Perspectives on fatness and food choice amongst Black and White men and women in Canada
Bibliographic record
Abstract
Despite recent critiques of contemporary obesity discourses that link ‘modern Western lifestyles’ to an ‘obesity epidemic’, the population’s weight remains a central concern of current dietary guidelines. Food choices that are considered beneficial to maintaining a certain weight are understood to play a key role in one’s health. This concern reflects medico-moral assumptions about the properties of food and what people should eat. However, the impact of obesity discourses on different individuals and social groups is rarely considered, although there is some evidence that people do generate, reflect and resist the norms and standards set for them, including those that relate to food/weight. In this paper, we will examine the perspectives on fatness and food choice amongst Black and White women and men living in Vancouver and Halifax, Canada. With this examination, we will challenge conventional assumptions about the singular ‘modern Western lifestyle’ that leads to obesity concerns by teasing out some of the social, cultural and political contexts within which people conceptualise issues regarding weight and make their food choices. By examining the experiences of both women and men we will also provide important insights into the gendered ways in which people engage with obesity discourses and the injunction to ‘eat healthily’ as a form of weight management.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.015 | 0.009 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".