Do Men and Women Exhibit Different Preferences for Mates? A Replication of Eastwick and Finkel (2008)
Bibliographic record
Abstract
Evolutionary theory predicts that men will prefer physically attractive romantic partners, and women will prefer wealthy, high-status partners. This theory is well-supported when examining ideal hypothetical partner preferences, but less support has been found when people interact face-to-face. The present study served as a direct replication of results reported in Eastwick and Finkel (2008). We recruited 307 participants and utilized a speed-dating methodology to allow in-person interactions, then administered follow-up surveys to measure romantic interest over 30 days. Data were analyzed using multilevel modeling and were aggregated using meta-analysis. Consistent with previous findings, our results showed that participants were more romantically interested in potential partners if they were viewed as attractive and good potential earners, and these associations were not moderated by gender. Results suggest that gender differences predicted by evolutionary theory may not hold when people interact with potential romantic partners face-to-face. However, we discuss these results in light of some general methodological limitations and evidence from other lines of research.
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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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".