Listen… and Speak: A Discussion of Weight Bias, its Intersections with Homophobia, Racism, and Misogyny, and Their Impacts on Health
Bibliographic record
Abstract
This article is a version of the Ryley-Jeffs Memorial Lecture, delivered on 8 June 2018. It discusses weight bias and the intersections with homophobia, racism, and misogyny, and how these impact health. While the dominant discourse attests that people can lose weight and keep it off, evidence informs us that maintenance of weight loss is unlikely. Using a flawed epistemological framework, obesity has been declared a disease, and weight bias been perpetuated. Weight bias is pervasive, both in the general public and amongst health professionals, often using inappropriate tools to assess the impact of weight on health. This contributes to overlooking the life circumstances that truly cause morbidity: social determinants of health such as income, social connectedness and isolation, adverse childhood experiences, and cultural erasure. A variety of tools dietitians can use to appropriately assess health risk are provided, along with examples of actions that can be taken to reduce weight bias. Dietitians who are leading the profession in taking action against weight bias and stigma are profiled.
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 | 0.029 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.037 | 0.046 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.018 | 0.024 |
| Insufficient payload (model declined to judge) | 0.003 | 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".