The ‘Ideal’ Body According to AI: Body Image Implications for Athletes and Non-Athletes
Bibliographic record
Abstract
Exposure to media images depicting ‘ideal’ bodies is a long-standing precursor to negative body image, and with advancements in artificial intelligence (AI) guiding creative media processes, it is necessary to explore the ways in which AI learns and generates ideal bodies. This study is a content analysis of images generated using three common AI platforms (Dall-E, MidJourney, Stable Diffusion) whereby comparisons across athlete and non-athlete images were examined. 48 images (50% athlete, 50% female) were generated using consistent prompts across AI platforms, which were then systematically analyzed for body image features. Comparisons across athlete and non-athlete images for females and males were conducted. Deductive coding suggested that the majority of images depicted low or very low body fat (athletes: 100%, non-athletes: 95.6%, p = .43 for group difference) and high muscularity (athletes: 91.7%, non-athletes: 37.5%, p < .001 for differences between the groups). Images of males were significantly more muscular (p < 0.05) and wearing less revealing clothing than females (p < 0.05). Among athletes, female images were coded as more objectified than males (p < 0.05). The results suggest that current body ideals represent unrealistic standards, specifically in their promotion of low body fat and greater than average muscularity in both groups, despite obvious differences in activity levels and sport type. The results also suggest that female athletes are more objectified than their male counterparts. This research offers insight on the body ideals perpetuated in the media which emphasize unrealistic standards that may damage mental well-being.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".