Cross-Cultural Variation in Mate Preferences for Averageness, Symmetry, Body Size, and Masculinity
Bibliographic record
Abstract
Sexual selection has greatly influenced the evolved biology, psychology, and culture of humans and favors individuals who choose healthy and fertile mates. Physical traits that cue quality are generally preferred and perceived as attractive. However, because such traits often involve cost-benefit trade-offs, mate preferences are expected to vary among cultures as a function of local ecology and social environment and among individuals as a function of one’s personal experiences and life history. As such, it is essential to understand how ontogenetic and environmental factors influence mate preferences that may be locally adaptive and context specific. Here the authors review a growing body of comparative research, demonstrating predictable patterns in men’s and women’s preferences for facial averageness, facial symmetry, stature, body mass, and facial and vocal masculinity or femininity both between and within cultures. The authors consider potential factors influencing variation in preferences that include resource availability, disease prevalence, paternal investment, visual experience, and cultural norms.
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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 | 0.002 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".