Positioning of Weight Bias: Moving towards Social Justice
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Weight bias is a form of stigma with detrimental effects on the health and wellness of individuals with large bodies. Researchers from various disciplines have recognized weight bias as an important topic for public health and for professional practice. To date, researchers from various areas have approached weight bias from independent perspectives and from differing theoretical orientations. In this paper, we examined the similarities and differences between three perspectives (i.e., weight-centric, non-weight-centric (health-centric), and health at every size) used to understand weight bias and approach weight bias research with regard to (a) language about people with large bodies, (b) theoretical position, (c) identified consequences of weight bias, and (d) identified influences on weight-based social inequity. We suggest that, despite differences, each perspective acknowledges the negative influences that position weight as being within individual control and the negative consequences of weight bias. We call for recognition and discussion of weight bias as a social justice issue in order to change the discourse and professional practices extended towards individuals with large bodies. We advocate for an emphasis on social justice as a uniting framework for interdisciplinary research on weight 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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| 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 it