Social Embeddedness, Multiplexity, and Criminal Collaboration Within the Sinaloa Cartel
Bibliographic record
Abstract
Objectives This study examines how social embeddedness and multiplex social ties shape criminal collaboration in the Sinaloa Cartel. It investigates how different types of relationships—such as kinship, friendship, meetings, and compadre ties—influence participation in drug crimes and broader forms of collaboration. Methods Drawing on trial transcripts from Joaquín “El Chapo” Guzmán Loera's U.S. federal case, we reconstructed social networks among 188 actors. Using social network analysis and Logistic Regression Quadratic Assignment Procedure (LRQAP) models, we assessed the conditional associations of different social ties with drug crimes and criminal collaboration, controlling for demographic and organizational characteristics. Results Friendship and meeting ties exhibited the strongest associations with drug crimes and criminal collaboration. Familial ties were initially significant but lost their effect once shared cartel affiliation was considered, suggesting a mediating role of organizational membership. Compadre and prison ties showed no significant associations. Conclusions Trust-based relationships such as friendships and meetings play a pivotal role in fostering collaboration within organized crime groups. Familial ties may exert indirect effects through shared cartel affiliation. The findings underscore the importance of distinguishing between types of social ties and demonstrate the value of trial transcripts as a source for advancing empirical research on organized crime.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| 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".