One gang dies, another gains? The network dynamics of criminal group persistence*
Bibliographic record
Abstract
Abstract What leads a minority of criminal groups to persist over time? Although most criminal groups are characterized by short life spans, a subset manages to survive extended periods. Contemporary research on criminal groups has been primarily descriptive and static, leaving important questions on the correlates of group persistence unanswered. By drawing from competing perspectives on the relationship between cohesion and group persistence, we apply a longitudinal approach to examine the network dynamics influencing the life span of criminal groups. We use 9 years of official data on the criminal and social networks of gang associates in Montreal, Quebec, Canada, to delineate criminal group boundaries and examine variation in group duration. Our statistical approach simultaneously considers within‐ and between‐group attributes to isolate how groups’ cohesion, as well as their embeddedness in the wider gang structure, impacts survival. Our results show that group survival is a function of their cohesion and embeddedness. Yet, the relationship is not direct but moderated by group size. Whereas large groups that adopt closed structures are more likely to persist, small groups’ survival depends on less cohesive and more versatile structures. In the discussion, we consider the impact of these findings for the continued understanding of group trajectories.
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".