Correlated preferences for facial masculinity and ideal or actual partner's masculinity
Bibliographic record
Abstract
Studies of women's preferences for male faces have variously reported preferences for masculine faces, preferences for feminine faces and no effect of masculinity-femininity on male facial attractiveness. It has been suggested that these apparently inconsistent findings are, at least partly, due to differences in the methods used to manipulate the masculinity of face images or individual differences in attraction to facial cues associated with youth. Here, however, we show that women's preferences for masculinity manipulated in male faces using techniques similar to the three most widely used methods are positively inter-related. We also show that women's preferences for masculine male faces are positively related to ratings of the masculinity of their actual partner and their ideal partner. Correlations with partner masculinity were independent of real and ideal partner age, which were not associated with facial masculinity preference. Collectively, these findings suggest that variability among studies in their findings for women's masculinity preferences reflects individual differences in attraction to masculinity rather than differences in the methods used to manufacture stimuli, and are important for the interpretation of previous and future studies of facial masculinity.
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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 | 0.004 |
| 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".