Ethics trumps resources in women’s and men’s evaluations of potential mates and competitors
Bibliographic record
Abstract
Just as kindness is prioritized in mate selection, warmth and fairness are often favored in cooperative social interactions, sometimes over competence and wealth, suggesting that these traits may influence social status. We conducted three studies to examine how heterosexual men (N = 193) and women (N = 178) from the U.S. evaluate men's faces for mating- and status-relevant traits, both alone and in combination with vignettes describing their economic resources and ethical reputation. The vignettes presented all men as generally smart, nurturant, and healthy, but pitted economic resources against ethical reputation surrounding care and fairness. Study 1, pitting men's ethics against their resources, found that an ethical reputation enhanced ratings of long-term mating attractiveness, prestige, intelligence, and kindness, but short-term mating attractiveness and physical dominance ratings were unaffected. Study 2, pitting men's parent's ethics against parental resources, yielded results consistent with Study 1. Study 3, pitting men's ethical history related to their adolescence with their current resources, found similar results to studies 1 and 2 with one key difference: women lowered prestige ratings for men with an unethical past, and men lowered physical dominance ratings for these men. The discussion reassesses notions of status and resources, exploring their relative significance in mating evaluations.
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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 | 0.006 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".