Victim-Offender Overlap in Violent Crime: Targeting Crime Harm in a Canadian Suburb
Bibliographic record
Abstract
Abstract Research Question To what extent are victims of violent crime also offenders, and vice versa, with what concentrations of total crime harm across each person who has ever been reported as both a victim and an offender within the study period? Data We analyse 27,233 unique individuals who were the subject of violent crime reports to the Peel Regional Police Service in Canada, either as offenders, victims, or both, for crimes reported between January 1, 2014, and December 31, 2016. Each individual linked to a violent crime in this period was tracked for the 730 days subsequent to the first crime report naming them. Methods We coded each crime with the Canadian Crime Severity Index (CCSI) to calculate victimization and offending harm totals across all incidents for each individual. We then computed each individual’s ratio of total CCSI from victimization to total CCSI from victimization. Based on the distribution of these ratios of CCSI from all offending to all victimization, we show how police can distinguish three categories of victim-offenders (VOs): predominant victims (PVs), predominant offenders (POs), and balanced victim-offenders (BVOs), as well as the single-category absolute offenders (AOs) and absolute victims (AVs). Findings Across all 27,233 individuals tracked, 17,138 (64%) appeared first as victims, and 10,095 (36%) appeared first as suspects. Of those appearing first as victims, 997 (6%) are linked to a violent crime as an offender within 730 days. Among those appearing first as offenders, 1019 (10%) are subsequently reported as victimized within 730 days. The total of this combined group (VOs) = 1665 individuals (6% of the entire population). Using a 3.5:1 ratio of victim to offender harm, we subdivide the 1665 VOs further into 322 predominant victims, 280 predominant offenders, and 1063 balanced victim-offenders. The 20% of individuals ( n = 5455) with highest harm are linked to 71% of overall harm. On average, predominant offenders (who have also been victimized) are associated with 2.7 times as much harm as absolute offenders, and predominant victims (who have also been offenders) have three times as much harm as absolute victims. Conclusions This research shows how combining records of victimization and offending to target higher harm levels with greater potential benefits for police investments in harm reduction and prevention.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".