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-related issues (including excess weight, disordered eating and body concerns) are often considered as comprising distinct domains of 'obesity' and 'eating disorders'. In this commentary we argue that the concept of weight bias is an important variable when considering wellbeing across the spectrum of weight-related issues. We make the following six points in support of this argument: i) weight bias is common and has adverse health consequences, ii) shaming individuals for their body weight does not motivate positive behaviour change, iii) internalized weight bias is particularly problematic, iv) public health interventions, if not carefully thought out, can perpetuate weight bias, v) weight bias is a manifestation of social inequity, and vi) action on weight bias requires an upstream, population-level approach. To achieve sustainable reductions in weight bias at a population level, substantive modifications and collaborative efforts in multiple settings must be initiated. We provide several examples of population-level interventions to reduce 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it