Coercive control in the context of partner abuse: behavioural markers, assessment challenges, and interview approaches
Bibliographic record
Abstract
Coercively controlling behaviours are highly prevalent in the context of intimate partner violence. However, coercive control often goes undetected because, unlike physical violence, it has not always been recognized as a criminal offence, is often perceived as less severe, and does not produce visible signs of physical violence. This paper outlines the importance of understanding what coercive control is, what coercive control looks like, why it is difficult to identify, and how investigative interviewing approaches can be employed to capture behaviours associated with coercive control when working with individuals who have engaged in partner abuse. Investigative interviewing approaches and motivational interviewing can help uncover coercively controlling behaviours that would otherwise be undetected by police and other justice-involved practitioners. Use of these approaches are illustrated to emphasize the importance of planning and preparation prior to the interview process, establishing rapport, and creating collaborative, non-adversarial relationships between the interviewer and the interviewee. These factors are likely to increase the quantity and quality of information gathered during the interview process, capture the nuances of coercive control, and reduce the likelihood that the interviewee will engage in controlling behaviours that could negatively impact the interview process.
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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 | 0.059 | 0.107 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".