An exploratory, cross-cultural study on perception of putative cyclical changes in facial fertility cues
Bibliographic record
Abstract
Although many researchers have argued that facial traits evolved as honest cues to women's current fertility (possibly via changes in facial femininity), evidence that women's facial attractiveness is significantly, positively related to probability of conception throughout menstrual cycle is mixed. These mixed results could reflect differences among studies in the methods used to assess facial attractiveness (i.e., forced choice versus rating-scale methods), differences in how fertility was assessed, differences in perceiver characteristics (e.g., their own attractiveness), and facial preferences possibly being moderated by the characteristics of the living environment. Consequently, the current study investigated the putative effect of cyclical changes in fertility on women's facial attractiveness and femininity (1) using forced choice and rating-scale method, (2) conducting both ovulation tests and repeated daily measures of estradiol assessing the conception probability, (3) based on a culturally diverse sample of perceivers, while (4) controlling for inter-individual variation. Although we found some limited evidence that women's faces became more attractive when conception probability increased, these effects differed depending on the methods used to assess both attractiveness and fertility. Moreover, where statistically significant effects were observed, the effect sizes were extremely small. Similarly, there was little robust evidence that perceivers' characteristics reliably predicted preferences for fertility cues. Collectively, these results suggest that mixed results in previous studies examining cyclical fluctuation in women's facial attractiveness are unlikely to reflect inter-cultural differences and are more likely to reflect differences in the methods used to assess facial attractiveness and fertility.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".