Measuring sexual dimorphism in human faces
Bibliographic record
Abstract
Facial shape is one of the most widely studied sexually dimorphic traits in humans. Sexually dimorphic facial shape has been linked to processes in neurodevelopment, immunocompetence, social perception, and mate preference. However, research into these associations has produced conflicting results, owing in part to the diverse methods used to quantify sexual dimorphism of the face. Our study compares two commonly used methods for measuring morphological sexual dimorphism: regression scoring and Canonical Variates Analysis (CVA; or linear discriminant analysis). We test both methods on a large sample of adult males (n = 540) and females (n = 540) with three-dimensional (3D) descriptions of the whole face, both with and without prior decomposition of the allometric component. Our results show that CVA outperforms regression scoring, resulting in scores that are more accurate in classifying the sexes and recreating the male-female shape axis (i.e., the difference in shape means based on reported sex). We also find that height is positively associated with regression scores after controlling for sex (p < 0.01), but not with CVA scores. These results suggest the need for a possible reassessment of previous claims that taller males have more male-like facial shapes, as well as a broader re-evaluation of the literature that considers the significance of method selection in shaping research outcomes. We establish a foundation for more accurate comparisons of facial sexual dimorphism and its relationship to various domains of human health and biology.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".