Attrition in intimate partner violence cases through the criminal justice system: a scoping review of patterns and predictors
Bibliographic record
Abstract
Intimate partner violence (IPV) poses significant social and legal challenges, with severe consequences for victims and society. Despite the criminal justice system’s crucial role, attrition – where reported cases diminish before prosecution – remains a major concern. This scoping review examines patterns and predictors influencing attrition in IPV cases, analysing 24 empirical studies. Findings reveal that 63% to 97.2% of reported cases do not result in conviction, with predictors categorised into victimological (e.g. vulnerability, lack of cooperation), criminological (e.g. offender characteristics, recidivism), and legal/institutional (e.g. evidence collection, procedural barriers). The review emphasises the need for improved victim support, evidence handling, and professional awareness. However, the inclusion of studies from diverse legal systems, primarily the U.S. limits generalisability. Future research should explore diverse legal contexts, compare jurisdictions, and consider the timeline of attrition to identify key intervention points.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".