Women’s Preferences for Strong Men Under Perceived Harsh Versus Safe Ecological Conditions
Bibliographic record
Abstract
Ecological conditions provide information about available resources for one’s environment. In humans, this has been shown to influence reproductive behavior, as individuals may engage in trade-offs between partner quality and investment. For instance, many women may trade-off preferences for men with physical features indicative of social dominance and health over physical features indicative of commitment and investment. The current study explored women’s preferences for formidable men under safe vs. harsh ecological conditions. Across three studies, U.S. university women ( N = 1,098) were randomly assigned to a perceived harsh or safe ecological condition. They were asked to rate the attractiveness of men’s body types (i.e., muscular vs. less muscular). Findings revealed that in general, women rated stronger men as more attractive than weaker men irrespective of the ecological condition. Evidence for preference as a function of ecology appeared only when a two-alternative forced-choice task was used (Study 3), but not in rating tasks (Studies 1 and 2). Study 3 showed that women had a relatively stronger preference for stronger men for short-term relationships in a resource scarce ecological condition. This research provides some evidence that perceived ecological conditions can drive women’s preferences for men with enhanced secondary sex characteristics as a function of mating context. These findings are consistent with previous research indicating the importance of physical characteristics in men’s attractiveness, and it adds to the existing literature on ecological factors and mating preferences.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".