Men's strategic preferences for femininity in female faces
Bibliographic record
Abstract
Several evolutionarily relevant sources of individual differences in face preference have been documented for women. Here, we examine three such sources of individual variation in men's preference for female facial femininity: term of relationship, partnership status and self-perceived attractiveness. We show that men prefer more feminine female faces when rating for a short-term relationship and when they have a partner (Study 1). These variables were found to interact in a follow-up study (Study 2). Men who thought themselves attractive also preferred more feminized female faces for short-term relationships than men who thought themselves less attractive (Study 1 and Study 2). In women, similar findings for masculine preferences in male faces have been interpreted as adaptive. In men, such preferences potentially reflect that attractive males are able to compete for high-quality female partners in short-term contexts. When a man has secured a mate, the potential cost of being discovered may increase his choosiness regarding short-term partners relative to unpartnered men, who can better increase their short-term mating success by relaxing their standards. Such potentially strategic preferences imply that men also face trade-offs when choosing relatively masculine or feminine faced partners. In line with a trade-off, women with feminine faces were seen as more likely to be unfaithful and more likely to pursue short-term relationships (Study 3), suggesting that risk of cuckoldry is one factor that may limit men's preferences for femininity in women and could additionally lead to preferences for femininity in short-term mates.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".