The Effect of a Food Addiction Explanatory Model of Eating Behaviours on Weight-Based Stigma: An Experimental Investigation
Bibliographic record
Abstract
Weight stigmatization and discrimination are pervasive issues that have numerous adverse consequences for those with excess weight. The current study replicated and extended a study examining the effect of the food addiction model on weight-based stigma and weight controllability beliefs. Undergraduate students (N = 757) were randomly assigned to one of four conditions where they read a newspaper article accompanied by a photo of a female target who was either obese or normal-weight, and an explanation for her eating behaviours as either due to food addiction or poor lifestyle choices. Stigma towards the target, obesity in general, and self were assessed. Results were mixed, such that the target with obesity elicited greater weight stigma, a food addiction explanation increased perceptions of psychopathology towards the target, and an explanation about poor lifestyle choices elicited judgment towards the target. Neither explanation about eating behaviours elicited stigma on any other measures.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 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 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".