Cues for facial attractiveness and preference of sexual dimorphism: A comparison across five cultures
Bibliographic record
Abstract
It has been hypothesised that the strength of association between sex typicality and attractiveness follows an adaptive pattern across cultures. Such pattern allows for adjustment of individual preferences for facial cues associated with direct (parenting) and indirect (biological quality) benefits from mating with a potential mate according to environmental conditions. To test this hypothesis, we examined associations among intra-culturally perceived sex typicality, attractiveness, measured skin lightness, measured averageness, and sexual dimorphism of shape from facial images, while controlling for age, body mass, and facial width, in five distinct cultures with different environmental and socioeconomic conditions (Cameroon, N of facial stimuli = 200, 100 women; Colombia, N = 138, 66 women; Czechia, N = 100, 50 women; Iran, N = 87, 43 women; and Turkey, N = 185, 93 women). Our results suggest that measured sexual shape dimorphism and averageness are not significantly associated with neither perceived sex typicality nor attractiveness across the cultures. In all samples of female faces, however, perceived sex typicality was positively related to facial attractiveness. Women found perceived sex typicality in men as more attractive only in the Czech environment, with its relatively abundant resources, and in Colombia, which is a highly socioeconomically heterogeneous and competitive culture. Lighter skin raised the ratings of both attractiveness and sex typicality only in Cameroonian women. Darker men were perceived significantly more sex-typical but not more attractive in Cameroonian, Colombian, and Iranian samples. Altogether, our results highlight the need to control for which measure of sexual dimorphism is used (perceived or measured) and make a detailed description of the local environment. It is the perceived, rather than measured, sexual dimorphism that is associated with perceived attractiveness, and wealth distribution, rather than public, health that seems to affect masculinity preferences.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".