Women’s Gossip as an Intrasexual Competition Strategy
Bibliographic record
Abstract
In the evolutionary sciences, gossip is argued to constitute an adaptation that enabled human beings to disseminate information about and to keep track of others within a vast and expansive social network. Although gossip can effectively encourage in-group cooperation, it can also be used as a low-cost and covert aggressive tactic to compete with others for valued resources. In line with evolutionary logic, the totality of evidence to date demonstrates that women prefer to aggress indirectly against their rivals via tactics such as gossip and social exclusion, in comparison to men who use proportionally more direct forms of aggression (e.g., physical aggression). As such, it has been argued that heterosexual women may use gossip as their primary weapon of choice to derogate same-sex rivals in order to damage their reputation and render them less desirable as mates to the opposite sex. This involves attacking the physical attractiveness and sexual reputation of other women, which correspond to men’s evolved mating preferences. Androcentric theorizing in the evolutionary sciences has stifled a well-rounded understanding of how women use gossip to compete, with whom, and in what situations.
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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 | 0.001 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".