Self-Perceived Mate Value, Facial Attractiveness, and Mate Preferences: Do Desirable Men Want It All?
Bibliographic record
Abstract
Ten years ago, Buss and Shackelford demonstrated that high mate value (i.e., physically attractive) women held more discerning mate preferences relative to lower mate value women. Since then, researchers have begun to consider the equally important role of men's sexual selectivity in human mate choice. Yet, little research has focused on whether high mate value men are similarly choosy in their mate preferences. In a sample of 139 undergraduate men, relationships between self-perceived mate value as well as female-rated facial attractiveness were examined in relation to men's expressed mate preferences. Results showed that self-perceived mate value was unrelated to men's facial attractiveness as rated by women. Men who believed they were of high mate value were more likely than lower mate value men to prefer to marry at a younger age; to have a spouse who was younger than them; and to have a partner who was sociable, ambitious, high in social status, with good financial prospects, a desire for children, health, good looks, and mutual attraction. Objective male facial attractiveness was generally unrelated to heightened mate preferences, with the exception of heightened preference for similar religious background and good physical health. Findings suggest that men who perceive themselves as high in overall mate value are selective in their mate choice in a manner similar to high mate value women.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 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".