Victim-Offender Overlap in Violent Crime: Targeting Crime Harm in a Canadian Suburb
Notice bibliographique
Résumé
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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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».