Mating Interest Improves Women’s Accuracy in Judging Male Sexual Orientation
Bibliographic record
Abstract
People can accurately infer others' traits and group memberships across several domains. We examined heterosexual women's accuracy in judging male sexual orientation across the fertility cycle (Study 1) and found that women's accuracy was significantly greater the nearer they were to peak ovulation. In contrast, women's accuracy was not related to their fertility when they judged the sexual orientations of other women (Study 2). Increased sexual interest brought about by the increased likelihood of conception near ovulation may therefore influence women's sensitivity to male sexual orientation. To test this hypothesis, we manipulated women's interest in mating using an unobtrusive priming task (Study 3). Women primed with romantic thoughts showed significantly greater accuracy in their categorizations of male sexual orientation (but not female sexual orientation) compared with women who were not primed. The accuracy of judgments of male sexual orientation therefore appears to be influenced by both natural variations in female perceivers' fertility and experimentally manipulated cognitive frames.
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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 | 0.000 |
| 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".