Women’s Competition for Mates: Experimental Findings Leading to Ethological Studies
Bibliographic record
Abstract
There has been an explosion of survey-based and experimental work pertaining to women’s intrasexual competition for mates. Rather than extensively review this growing and vast body of literature, the goal of this paper is instead to call for ethological studies on this topic. I propose that, in general, non-ethological studies should cause us to question the reliability of findings, how frequently, and in what contexts competitive strategies are used. After a condensed overview of the evolutionary theory of female intrasexual competition, the paper is organized around three central problems that are faced by researchers who want to use an ethological approach. First, I will briefly review how female intrasexual competition involves multiple strategies that are often indirect or covert. Second, I will discuss how female intrasexual competition is dynamic, and changes depending on particular variables, such as hormonal status and audience. Third, I will argue that the context for examining competition matters, such that the reach of competitive views and attitudes is far wider than previously considered. I support this third point by presenting the results of a preliminary study where women appeared to engage in competition after merely being primed to think about potential threats to their romantic relationships.
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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 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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; 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".