Violence and Homicide Following Partner Infidelity
Bibliographic record
Abstract
Abstract Infidelity is one of the greatest adaptive challenges of our reproductive lives. A partner’s infidelity can lead to their defection from the relationship and offspring, loss of important resources, and for men, cuckoldry. It is unsurprising, then, that humans have evolved adaptations meant to prevent, curtail, and punish a partner’s infidelity. Among the most devastating of these are the perpetration of intimate partner violence, homicide, uxoricide, and filicide. This chapter reviews theory and supporting evidence that aggression has evolved, in part, as an adaptive set of behavior meant to prevent and respond to infidelity. It begins by describing the particular reproductive challenges posed by infidelity for men and women. Next, it reviews the available evidence that violence and killing is an abhorrent, yet predictable response to real or suspected infidelity, with attention paid to sex differences in these acts. The putative adaptive functions of different types of aggression toward an intimate partner, a sexual rival, and toward offspring are discussed. It then highlights the important role of perceptual biases surrounding infidelity and negative affect, including jealousy and anxiety, in mediating aggressive responses to infidelity. Finally, adaptive explanations of individual differences, cultural contexts, and environmental factors in predicting violent responses to infidelity are discussed and future directions are offered in order to highlight the pressing need for continued research on the adaptive functions of violence occurring in the shadow of infidelity.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".