Pathogen disgust predicts women’s preferences for masculinity in men’s voices, faces, and bodies
Bibliographic record
Abstract
Recent studies suggest that pathogen-related factors may contribute to systematic variation in women’s preferences for masculinity in men’s faces. However, there is very little evidence for similar correlations between pathogen-related factors and women’s preferences for masculinity in other domains (e.g., men’s voices or bodies). Consequently, we conducted a series of studies to examine whether pathogen disgust (assessed using Tybur et al’s Three Domains of Disgust Scale) predicts individual differences in women’s preferences for masculine characteristics in men’s voices, bodies, and faces. We also tested if pathogen disgust predicts individual differences in measures of women’s actual mate choices in the same way. We observed positive correlations between women’s pathogen disgust and their preferences for masculinity in men’s voices (Study 1) and faces and bodies (Study 2). We also observed positive correlations between women’s pathogen disgust and their masculinity ratings of both their current and ideal romantic partners (Study 3). Each of these correlations was independent of the possible effects of women’s sexual and moral disgust. Together, these findings suggest that individual differences in pathogen disgust predict individual differences in women’s masculinity preferences across multiple domains and may also predict individual differences in their actual mate choices.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".