A Cross-cultural Comparison of Mate Preferences among University Students; The United States Vs. The People’s Republic of China (PRC)
Bibliographic record
Abstract
In this study, college students from both North America (n = 648) and People’s Republic of China (n = 735) completed a questionnaire that asked about their preferences for characteristics in a mate. Drawing from both social and evolutionary theoretical perspectives, we predicted that traditional gender differences in mate preferences, such as men preferring physical attractiveness to a greater degree than women, would be found in both samples. We also explored other differences between the two cultures, including in the relative importance of the various traits. In both cultures, men emphasized physical attractiveness to a greater degree than women, whereas women emphasized characteristics associated with status (earning potential, social status, wealth) to a greater degree than men. Additional gender differences were found in the Chinese sample which may be associated with China’s more traditional emphasis on gender roles. Further analyses suggested that there was similarity between the two cultures in traits most and least desired but also differences in the relative importance of many of the traits. The greatest cross-cultural differences were found in preferences for “wants children” (desired more in the American sample than in the Chinese sample), “creative and artistic” and “good housekeeper” (both more important in the Chinese sample than in the American sample).
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".