Snakes, Ladders, and Brokers: The Role of Social Networks in Rising Through the Ranks of an Outlaw Motorcycle Gang
Bibliographic record
Abstract
Contrary to unstructured criminal groups, outlaw motorcycle gangs (OMCGs) utilize a three-tiered promotional process. Through these stages, members have shown to vary in their ability to advance. The current study uses social network analysis (SNA) to investigate the influence of network capital on the timing of promotion. We pay particular attention as to whether bikers show a capacity to connect and work with others, and/or whether the involvement in violence or drug trafficking plays a role in the timing of promotion. Using longitudinal data, we analyze the promotional trajectories of 62 members. Survival analyses indicated that those with an increase in social capital, whether that be in the number of contacts or strategic network positions, experienced faster promotions. Specifically, how individuals positioned themselves the year before promotion was a determining factor for advancement. Findings hold important implications for the timing of target intervention efforts on individuals involved in OMCGs.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".