Microcosms of violence among street gang members: Social contagion, propensity to violence, and gang embeddedness
Bibliographic record
Abstract
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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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".