Body Image and Voluntary Gaze Behaviors towards Physique-Salient Images
Bibliographic record
Abstract
= 87; mean age = 19.5 years) were discretely exposed to images of same-sex models with idealized- and average-physiques while completing an irrelevant computer task. Voluntary gaze at the images was covertly recorded via hidden cameras. Participants also completed measures of affect before and after the computer task. Measures of body-related envy, body appreciation, and self-perceptions of attractiveness, thinness, and physical strength were completed. Men and women did not differ in how often nor for how long they looked at the images overall, but body image variables were differentially associated with their voluntary gaze behaviors. For men, higher body-related envy and lower body appreciation were correlated with more looks at the average-physique model. Although women reported higher body-related envy than men, envy and body appreciation were not significant correlates of gaze behaviors for women. Both men and women experienced a general affective decrease over time, but only for men was the change in negative affect associated with their time spent looking at the ideal-physique image. Overall, these findings suggest that body-related envy and body appreciation influence how men choose to consume physique-salient media, and that media consumption may have negative consequences for post-exposure affect. Body image factors appear to be more strongly associated with behavior in men, perhaps because men are generally less often exposed to physique-salient media and, in particular, to average-physique images.
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.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.008 | 0.003 |
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".