Health Status by Gender, Hair Color, and Eye Color: Red-Haired Women are the Most Divergent with the Lowest Viability and the Highest Fertility
Bibliographic record
Abstract
Abstract Background Red hair is associated with pain sensitivity, and more so in women than in men. Hair redness may thus interact with a female-specific factor. We tested this hypothesis on a large sample of Czech and Slovak respondents. They were asked about the natural redness and darkness of their hair, their natural eye color, their physical and mental health (24 categories), and other personal attributes (height, weight, number of children, lifelong number of sexual partners, frequency of smoking). Results We found that red-haired women did worse than other women in ten health categories and better in only three. In particular, they were more prone to colorectal, cervical, uterine, and ovarian cancer. Cancer risk increased steadily with increasing hair redness except for the reddest shade. Red-haired men showed a balanced pattern of health effects, doing better than other men in three categories and worse in three. Number of children was the only category where both male and female redheads did better than other respondents. We also confirmed earlier findings that red hair is naturally more frequent in women than in men. Conclusion Red-haired women had higher fecundity and sexual attractiveness, but this selective advantage seems offset by worse health outcomes and therefore lower viability. The resulting equilibrium between these two counterbalancing forces might explain why red hair has remained less common than other hair and eye colors. Of the ‘new’ hair and eye colors, red hair diverges the most from the ancestral state of black hair and brown eyes. It is the most sexually dimorphic variant, not only in population frequency but also in health outcomes. This sexual dimorphism seems to have resulted from a selection pressure that acted primarily on early European women and which led to a general and apparently rapid diversification of hair and eye colors.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".