Stigmatizing effects of weight status on lay perceptions of eating disorder-related distress
Bibliographic record
Abstract
The present study examined how weight status would affect lay perceptions of a White female student presenting signs of eating disorder-related distress. We recruited a mixed-gender, weight-diverse U.S. community sample through Mechanical Turk (N = 130; 49.2% female) to complete an online survey. Participants were randomly assigned to one of two conditions in which they read a personal statement section of a college application revealing eating disorder-related distress from a student who was either ‘overweight’ or ‘underweight.’ Participants evaluated the student on need for support, behavioural prescriptions for eating and exercise, and personal qualities. Although participants recognized a serious mental health concern in both conditions, they were more likely to prescribe eating disorder behaviors to the higher weight student. Findings suggest that weight stigma may bias lay perceptions of and even reinforce an eating disorder when exhibited by higher weight individuals.
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How this classification was reachedexpand
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".