Weighty words: exploring terminology about weight among samples of physicians, obesity specialists, and the general public
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
BACKGROUND: The words used to refer to weight and individuals with large bodies can be used to reinforce weight stigma. Given that most previous research has examined preferred terminology within homogenous groups, this research sought to examine terminology preferences across populations. METHODS: This paper reports on data gathered with the general public, family physicians, and obesity researchers/practitioners. Participants were asked about the words they commonly: (1) used to refer to people with large bodies (general public); (2) heard in their professional contexts (physicians and obesity specialists); and (3) perceived to be the most socially or professionally acceptable (all samples). RESULTS: Similarities and differences were evident between samples, especially related to weight-related clinical terms, the word fat, and behavioral stereotypes. CONCLUSION: The results provide some clarity into the differences between populations and highlight the need to incorporate use of strategies that may move beyond person-first language to humanize research and clinical practice with people with large bodies.
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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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