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Record W4409249601 · doi:10.1080/07418825.2025.2486703

Snakes, Ladders, and Brokers: The Role of Social Networks in Rising Through the Ranks of an Outlaw Motorcycle Gang

2025· article· en· W4409249601 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJustice Quarterly · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicCrime, Illicit Activities, and Governance
Canadian institutionsSimon Fraser University
FundersSocial Sciences and Humanities Research Council
KeywordsAdvertisingBusinessCriminologyPublic relationsMarketingPsychologyPolitical science

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.173
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.009
GPT teacher head0.291
Teacher spread0.281 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it