Are men’s gender equality beliefs associated with self-reported intimate partner violence perpetration? A state-level analysis of California men
Bibliographic record
Abstract
OBJECTIVES: To assess the association between gender equality beliefs and self-reported intimate partner violence (IPV) perpetration among California men. METHODS: We analyzed men's data (N = 3609) from three waves (2021, 2022, and 2023) of cross-sectional data from a statewide sample of California adults. We assessed gender equality beliefs using a three-item measure adapted from the World Values Survey, with higher scores representing more gender unequal beliefs (e.g., 'On a whole, men make better political leaders than women'). We assessed IPV perpetration in the last year by asking a) whether men committed any form of violence in the last year (physical violence, use or threat of violence with a weapon, sexual violence) and b) among those who reported committing violence, who they committed violence against. Those reporting violence against "a spouse or romantic partner" were categorized as perpetrating past-year IPV. Analyses were weighted to yield population estimates. Crude and adjusted logistic regression models evaluated the association between gender equality beliefs and past-year IPV perpetration. RESULTS: Almost 2% of men-equivalent to more than 280,000 men in California-reported IPV perpetration in the past year [1.9% (95%CI = 1.4-2.5)], and every one-point increase in their gender equality belief scale score [indicative of less gender equitable beliefs] was associated with 2.1 times greater odds of perpetrating past-year IPV (AOR: 2.14, 95%CI 1.61-2.86). CONCLUSIONS: Findings support prior research indicating that patriarchal beliefs reinforce men's violence against women in relationships and signal a need for violence prevention efforts focused on boys and men to that can include normative belief shifts related to women's capacities.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".