A scoping review of policing and coercive control in lesbian, gay, bisexual, transgender, and queer plus intimate relationships
Bibliographic record
Abstract
Abstract Coercive control is a form of intimate partner violence (IPV) that encompasses non‐physical behaviors used to constrain and entrap a partner. Coercive control is especially relevant to lesbian, gay, bisexual, transgender, and queer plus (LGBTQ+) relationships when abusers target the gender and sexual identity of their partners. Victim‐survivors, community members, and service providers often struggle to identify and intervene with this form of abuse. The role of police responding to coercive control is poorly understood, despite laws and calls to criminalize coercive control. Police responses to physical abuse in LGBTQ + relationships have caused harm, and it is important to extend this knowledge to police responses to coercive control. We conducted a systematic scoping review of international academic and gray literature sources published from 2014 through 2022 on the topic of policing coercive control within LGBTQ+ intimate relationships. We identified four interrelated policing themes across 35 sources: (1) reluctance to seek help from the police, (2) low rates of reporting abuse to police, (3) police actions following reports of IPV and coercive control, and (4) police harassment and violence increasing the experience of coercive control. Our review confirms that more research is needed on LGBTQ+ survivors of coercive control and their help‐seeking, as laws and calls to criminalize coercive control may not benefit LGBTQ+ communities.
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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 | 0.012 | 0.068 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.030 | 0.031 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".