Supportive Interventions of Chinese Police in Domestic Violence: Do Officer Knowledge and Training Matter?
Bibliographic record
Abstract
Policing domestic violence (DV) poses significant challenges in China due to cultural, legal, and organizational complexities. Policing DV in China favors mediation over assertive interventions, complicating law enforcement's role. While previous research has focused on coercive interventions by Chinese police, there is limited information on non-coercive, supportive approaches. This study investigates the relationship between police officers' knowledge and training regarding the Anti-DV law and their willingness to provide supportive services to DV victims in China. It also considers various individual and organizational factors. The data used in this study are derived from the Policing DV in China project, with a sample of 1,353 respondents who had experience dealing with DV cases within the past 3 years. The study focuses on three dependent variables representing supportive approaches to DV cases: Referral, Counseling, and Protection orders. Independent variables include officers' knowledge of the Anti-DV law and agency training. Control variables include the use of body-worn cameras (BWC) and attitudes toward Violence Tolerance, Male Dominance, and Gender Equality. Additionally, demographic variables, working environment, length of service, and police rank are considered. The analytical approach involves a three-step strategy, incorporating descriptive, bivariate analyses, and regression analyses. The results are interpreted using odds ratios and average marginal effects, and statistical software such as SPSS by IBM and R by Open-Source Model is utilized for data analysis. Key findings indicate that more than half of the officers referred intimate partner violence survivors to shelters and assisted victims in filing protection orders. Counseling practices varied across provinces and between male and female officers. Agency training and the use of BWC were positively associated with non-coercive and supportive approaches, while knowledge of the DV Act, male dominance score, and gender equality score did not predict the use of such approaches. Demographic characteristics, including police rank, length of service, and province of employment, influenced the utilization of non-coercive and supportive approaches. This study examines the challenges faced by Chinese police officers when responding to DV cases and their willingness to provide supportive interventions. The study highlights the complexities surrounding the initiation of protection orders due to officers' legal knowledge and discretion. The study emphasizes the importance of police support in addressing DV in China and the role of agency training in promoting non-coercive responses. It highlights regional variations in police support and underscores the need for addressing disparities in service provision across different provinces.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".