Higher-pitched female voices elicit jealousy: Comparing the explanatory power of perceptions of mate poaching, attributions of attractiveness, and trait jealousy
Bibliographic record
Abstract
Abstract Higher-pitched female voices elicit jealousy among women. However, it is unknown whether jealousy towards higher-pitched female voices is driven by perceptions of the rival’s mating strategy, by attractiveness to one’s romantic partner, or by variation in trait jealousy. Here, I manipulated women’s voices to be higher or lower in pitch, and tested whether jealousy towards female voices was more strongly associated with perceptions of mate poaching, perceptions of attractiveness to one’s mate, or with individual differences in trait jealousy. I replicated findings that higher voice pitch elicits more jealousy from women, which was positively associated with perceptions of mate poaching, and with attractiveness to one’s partner to a lesser extent. I found no evidence of an association between trait jealousy and perceptions of intrasexual competition. The findings suggest that perceptions of a target’s mating strategy have a somewhat stronger impact on jealousy than does perceived desirability to one’s romantic partner.
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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 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".