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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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; both teacher heads agree on what is shown here.
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".