Signaling sizeism: An assessment of body size‐based threat and safety cues
Bibliographic record
Abstract
Abstract Pervasive stigma against fat people and evidence for its harmful health consequences highlight the need for a better understanding of people's first‐hand experiences of navigating the world with a stigmatized body size. Drawing on social identity threat theory, we conducted a mixed‐method study with a qualitative examination of threat and safety cues as experienced by people who self‐identify as overweight. In an online survey, 48 people who self‐identified as overweight responded to open‐ended prompts to describe how situational features of a setting signal weight‐based threat and safety to them. Using thematic analysis, we identified several themes that characterized threat and safety cues. Particularly notable were inverse themes, such as structural exclusion versus structural accommodation and homogeneity of others versus general diversity , that highlighted how physical features of, and the people in, an environment positively or negatively impact fat people's psychological experience. Moreover, we conducted exploratory deductive coding using a recent taxonomy of safety cues developed by Kruk and Matsick (in press). Results highlighted how weight‐based stigma both parallels and diverges from other cues of identity safety (e.g., by gender or race/ethnicity). We suggest knowledge about situational cues can inform interventions to mitigate threat and promote safety among both fat people and other stigmatized groups.
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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 | 0.008 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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 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".