Perceived Femininity and Masculinity Contribute Independently to Facial Impressions
Bibliographic record
Abstract
In person perception research, femininity and masculinity are regularly conceived as two ends of one bipolar dimension. This unidimensional understanding permeates work on facial impressions, gender diagnosticity, and perceptions of LGBTQ individuals—but, it is perhaps most prominent in evolutionary work suggesting that sexually dimorphic facial features (which vary along a female–male continuum) correspond directly with subjective ratings of femininity and masculinity, which in turn predict ratings of traits such as attractiveness. In this paper, we analyze two large face databases (the Chicago and Bogazici Face Databases) to demonstrate that femininity and masculinity are distinct dimensions in person perception. We also evaluate key theoretical assumptions surrounding femininity and masculinity in evolutionary theories of face perception. We find that sexually dimorphic features weakly correlate with each other and typically explain just 10-20% of variance in subjective ratings of femininity and masculinity. Femininity and masculinity each explain unique variance in trait ratings of attractiveness, dominance, trustworthiness, and threat. Femininity and masculinity also interact to explain unique variance in these traits, revealing facial androgyny as a novel phenomenon. We propose a new theoretical model explaining the link between biology, facial features, perceived femininity and masculinity, and trait ratings. Our findings broadly suggest that concepts that are “opposites” semantically cannot necessarily be assumed to be psychological opposites.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".