The Contribution of Applied Social Sciences to Obesity Stigma-Related Public Health Approaches
Bibliographic record
Abstract
Obesity is viewed as a major public health concern, and obesity stigma is pervasive. Such marginalization renders obese persons a "special population." Weight bias arises in part due to popular sources' attribution of obesity causation to individual lifestyle factors. This may not accurately reflect the experiences of obese individuals or their perspectives on health and quality of life. A powerful role may exist for applied social scientists, such as anthropologists or sociologists, in exploring the lived and embodied experiences of this largely discredited population. This novel research may aid in public health intervention planning. Through these studies, applied social scientists could help develop a nonstigmatizing, salutogenic approach to public health that accurately reflects the health priorities of all individuals. Such an approach would call upon applied social science's strengths in investigating the mundane, problematizing the "taken for granted" and developing emic (insiders') understandings of marginalized populations.
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 | 0.053 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.004 |
| Science and technology studies | 0.010 | 0.080 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.005 | 0.009 |
| 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".