Mate Competition <i>between</i> the Sexes
Bibliographic record
Abstract
Abstract Darwinian sexual selection has been the dominant framework for understanding mate competition, which usually involves intrasexual competition for opposite-sex partners. This expected dynamic can become more complicated in any species that shows an appreciable rate of bisexual behavior. Specifically, intersexual mate competition is possible any time opposite-sex individuals engage in romantic/sexual competition over the same target. We review historical and ethnographic evidence that intersexual mate competition occurs among humans. Going further, we describe data from two different non-Western cultures—Samoa and the Istmo Zapotec (Oaxaca, Mexico). These data show that competitions to acquire and maintain sexual relationships with men occur between women and feminine same-sex attracted males. This intersexual mate competition most commonly involved feminine males attempting to mate-poach partnered men from their relationships with women. Using participant stories to complement mixed-methods data, we illustrate how these competitions typically involved feminine males attempting to entice the target man, whereas women engaged in mate-guarding and emotionally punitive behaviors. Although intersexual mate competition is unlikely to be found in most species, or across all human cultures, our data show that intersexual mate competition can ensue when males and females prefer the same sexual partners, who themselves behave in a bisexual manner.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".