Body Surveillance as a Moderator of the Relationship Between Fat Stereotypes and Body Dissatisfaction in Normal Weight Women
Bibliographic record
Abstract
This study examined the moderating effect of body surveillance on the relationship between fat stereotyping and body dissatisfaction in normal weight women. Undergraduate participants (N = 301) completed online measures assessing explicit and implicit fat stereotyping, body surveillance, and body dissatisfaction. Neither explicit nor implicit fat stereotyping significantly predicted body dissatisfaction. Further, body surveillance did not moderate the relationship between either explicit or implicit fat stereotypes and body dissatisfaction. However, post-hoc analyses examining Caucasian participants (N = 224) found differing results. Specifically, body surveillance significantly moderated the relationship between explicit fat stereotyping and body dissatisfaction. Higher explicit fat stereotypes predicted greater body dissatisfaction in Caucasian women with lower body surveillance. Conversely, higher explicit fat stereotypes predicted lower body dissatisfaction in Caucasian women with higher body surveillance. These counterintuitive results suggest that endorsing fat stereotypes acts as a buffer against body dissatisfaction in Caucasian normal weight women with stronger body surveillance.
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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 | 0.001 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".