Correlated Male Preferences for Femininity in Female Faces and Voices
Bibliographic record
Abstract
Sexually dimorphic physical traits are important for mate choice and mate preference in many species, including humans. Several previous studies have observed that women's preferences for physical cues of male masculinity in different domains (e.g., visual and vocal) are correlated. These correlations demonstrate systematic, rather than arbitrary, variation in women's preferences for masculine men and are consistent with the proposal that sexually dimorphic cues in different domains reflect a common underlying aspect of male quality. Here we present evidence for a similar correlation between men's preferences for different cues of femininity in women; although men generally preferred feminized to masculinized versions of both women's faces and voices, the strength of men's preferences for feminized versions of female faces was positively and significantly correlated with the strength of their preferences for feminized versions of women's voices. In a second study, this correlation occurred when men judged women's attractiveness as long-term, but not short-term, mates, which is consistent with previous research. Collectively, these findings (1) present novel evidence for systematic variation in men's preferences for feminine women, (2) present converging evidence for concordant preferences for sexually dimorphic traits in different domains, and (3) complement findings of correlations between women's facial and vocal femininity.
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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 | 0.002 |
| 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".