Microcosms of violence among street gang members: Social contagion, propensity to violence, and gang embeddedness
Notice bibliographique
Résumé
Gang members face a paradox: while they may join a gang for protection from violence, they are actually more likely to be victimized than non-gang members. Although it is known that gang affiliation increases the risk of violence perpetration and victimization, little is understood about the factors within gangs that influence these risks. This study examines the relationship between violent perpetration and victimization within the context of gang networks. Using 20 years of police data, we mapped the incidents of violent victimization and perpetration among 1587 Haitian street gang members and their affiliates in Montreal, Canada. Our results show that violence occurs in clusters within these groups and that victimization and perpetration are more likely to happen in the same locations within the network. Regression models revealed that victimization was strongly related to: (1) having committed violence, (2) having more violent perpetrators in one's entourage, and (3) having more victims in one's entourage. These three effects were interdependent, creating a mutual aggravation effect: members who had perpetrated high levels of violence, high levels of victimization in their network, and who had violent peers were 15 times more victimized than members who were not directly or indirectly involved in violence. The structure of peer relationships was also important. Denser networks provided some protection against victimization, but this was dependent on members' own level of violence. Violent perpetrators did not benefit from the protection offered by a close-knit network. Our findings show that violence within gangs is not equally distributed and is concentrated in certain areas of the network. Perpetration and victimization are linked, and the local density of the network can reduce the impact of violence in the network. Thus, the idea that gangs can provide protection may not be as paradoxical as it seems. In tightly knit groups, and for members not directly involved in violence, gang affiliation did not increase violence risk. This understanding may improve targeted interventions to prevent both the experience and commission of violence. • Violence within gangs tends to occurs in clusters. • Victimization and perpetration of violence occur in the same areas of the network. • Violence, perpetrated and sustained, among peers creates a mutual aggravation effect. • In some circumstances, denser gang networks offer protection against victimization.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,000 | 0,002 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 source (Gemma direct ou Codex distillé), 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 ».