When the Love Hormone Leads to Violence
Bibliographic record
Abstract
Does oxytocin influence intimate partner violence (IPV)? Clues from prior research suggest that oxytocin increases prosocial behavior, but this effect is reversed among people with aggressive tendencies or in situations involving defensive aggression. Animal research also indicates that oxytocin plays a central role in defensive maternal aggression (i.e., protecting pups from intruders). Among highly aggressive people, a boost of oxytocin may cause them to use aggression toward close others as a means of maintaining their relationship. Adopting an interactionist approach, we predicted that oxytocin would increase IPV inclinations, but this effect would be limited to people high in trait physical aggression. In a double-blind, placebo-controlled, between-subject experiment, participants varying in trait physical aggression received either 24 international unit of oxytocin or a placebo. Following two provocation tasks, participants rated the probability that they would engage in various aggressive behaviors (e.g., slapping, throwing an object that could hurt) toward a romantic partner. Oxytocin increased IPV inclinations, but this effect was limited to participants prone to physical aggression. These data offer the first evidence that IPV inclinations have a biological basis in a combination of oxytocin and trait physical aggressiveness.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".