Investigations Into Co-wife Aggression and Competition Across Cultures [Conference Presentation Abstract]
Bibliographic record
Abstract
Here I examine the dynamic of co-wife aggression through the lens of ethology, utilizing empirical evidence drawn from the eHRAF World Cultures database.My goal is to elucidate the underlying causes and strategies of competition among co-wives, who represent multiple women engaged in intense mating competition for the limited resources from one man.I propose that co-wife aggression is an adaptive response stemming from resource competition, specifically over male investment in one's offspring, and directly impinges on wives' reproductive success and inclusive fitness.Given the prevalence of polygyny in societies represented within the eHRAF database, the data set provides a fertile ground for the exploration of these dynamics.I employ a mixed-methods approach, combining quantitative analysis of eHRAF data with qualitative examination of ethnographic records.Searches were limited to records containing the terms polygamy and wives, with cohabiting, aggression, violent, or fight.Preliminary findings indicate a complex interplay of strategies used by co-wives that range from direct physical aggression to more subtle forms of manipulation (e.g., gossip, social exclusion, altering husband's perception), and alliance formation (i.e., to increase status and reputation), reflecting a nuanced understanding of social dynamics within polygynous households.In rare instances children are used to foster (and presumably win) competition.Further, they may also compete via differential reproduction, attempting to bear more children, thereby securing a larger portion of the husband's resources for their offspring.Mediation tends to involve the husband, the acknowledgement of hierarchy, and inclusion of sororal rather than unrelated co-wives.Directions for future research into women's intrasexual mating competition will be presented.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".