Female mate preference varies with age and environmental conditions
Bibliographic record
Abstract
Sexual selection and mate choice are dynamic processes that can be influenced by a variety of environmental and social factors, which have been well studied in a range of taxa. However, in humans, the environmental factors that influence regional variation in preference for mate attributes remain poorly understood. In addition, underlying variation based on individual age may strongly influence mate preferences. In this study, we examined written descriptions of preferred mates from the online dating profiles of 1111 women from 26 cities across Canada. We grouped the words describing preferred mates into four categories: resource holding potential, physical attractiveness, activities and interests, and emotional appeal. We then asked whether variation in environmental (sex ratio, population size and population density), economic (population income) and individual factors (age) predicted variation in the relative importance of these four categories of female mate preference. Sex ratio was the best predictor of preference for the physical attractiveness and the activities and interests of potential mates, with women in male-biased cities placing more emphasis on physical attractiveness and less emphasis on activities and interests. Age was the best predictor of preference for resource holding potential, with younger individuals placing more emphasis on this trait. No factors were strong predictors of variation in preference for emotional appeal, perhaps because this trait was highly valued in all populations. This work supports a growing body of literature demonstrating that mate choice and mate preferences are often dynamic and can be influenced by individual and environmental variation.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".