Coercive control in a national U.S. self-report survey: Prediction of repeated intimate partner violence.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Coercive, controlling behavior toward intimate partners correlates with physical intimate partner violence (IPV). We examined whether it also predicts subsequent IPV or other aggression. We conducted a secondary analysis of self-reports by 1,039 women and 509 men who participated in the first two waves of the Interpersonal Conflict and Resolution Study (Mumford et al., 2019). We defined coercive control as any reported perpetration at Wave 1 of threat to physically harm, threat to use information to control, or put down or disrespect their partner. The participants also reported perpetration of verbal abuse and physical or sexual aggression against intimate partners. We tested correlations of these behaviors with similar acts toward nonintimates (friends or unfamiliar persons) in Wave 1 and the prediction of physical violence in Wave 2, approximately 5 months later. Coercive control (14% of men, 26% of women) was correlated with physical or sexual IPV (8% of men, 15% of women) in both women and men and with physical violence and coercive control to nonintimates. In logistic regressions entering Wave 1 physical IPV on the first step, Wave 1 coercive control was a significant independent predictor of Wave 2 physical IPV overall, and for men but not women. Coercive control did not independently predict nonintimate physical violence. Coercive control toward an intimate partner is a unique predictor of physical IPV among men. Future research should use improved measures of coercive control and further examine coercive control as an indicator of general antisociality. (PsycInfo Database Record (c) 2025 APA, all rights reserved).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it