Beauty and the Body of the Beholder: Raters’ BMI Has Only Limited Association with Ratings of Attractiveness of the Opposite Sex
Bibliographic record
Abstract
OBJECTIVE: Assortative mating for adiposity increases the genetic burden on offspring, but its causes remain unclear. One hypothesis is that people who have high adiposity find other people with obesity more physically attractive than lean people. METHODS: The attractiveness of sets of images of males and females who varied in adiposity were rated by opposite sex subjects (559 males and 340 females) across 12 countries. RESULTS: There was tremendous individual variability in attractiveness ratings. For female attractiveness, most males favored the leanest subjects, but others favored intermediate fatness, some were indifferent to body composition, and others rated the subjects with obesity as most attractive. For male images rated by females, the patterns were more complex. Most females favored subjects with low levels of adiposity (but not the lowest level), whereas others were indifferent to body fatness or rated the images depicting individuals with obesity as the most attractive. These patterns were unrelated to rater BMI. Among Caucasian males who rated the images of the thinnest females as being more attractive, the magnitude of the effect depended on rater BMI, indicating limited "mutual attraction." CONCLUSIONS: Individual variations in ratings of physical attractiveness were broadly unrelated to rater BMI and suggest that mutual attraction is an unlikely explanation for assortative mating for obesity.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".