WHAT DO RATIONAL CHOICE THEORY AND SOCIAL NETWORK ANALYSIS HAVE TO SAY ABOUT THE DRUG WAR IN MEXICO?
Bibliographic record
Abstract
The violence attributed to drug trafficking organizations (DTOs) has evolved in the last decade, with DTOs increasing the reach and scope of their operations in the Latin American region and trafficking narcotics into the United States and Canada. The complexity of combating DTOs using military force has shown limited results, especially in Mexico, where levels of violence have increased to record levels. This study examines the development of a coercive approach based on two perspectives: First, on rational choice theory to describe how violence presents a maximizing strategy for drug cartels under actual conditions of competitive dynamics derived from the kingpin strategy employed in the previous two decades, and second, by employing social network analysis to study the competing dynamics of the cartel alliance structure of the two main cartels in Mexico, Cartel de Jalisco Nueva Generacion (CJNG) and Sinaloa Cartel. Under this framework, policy recommendations vary over social, political, and economic actions that, based on the underlying economic motivation of the cartels, seek to increase perceived costs, and create incentives for assimilating part of the cartels into an open marijuana market to reduce competitive pressures that fuels inter cartel conflict.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".