Why Are Violent Non-State Actors Able to Persist in the Context of the Modern State?
Bibliographic record
Abstract

 
 
 El Salvador, Guatemala, and Honduras constitute the most violent region on the globe outside a declared warzone: The Northern Triangle. Cities in these countries have dominated the list of most dangerous cities in the world for years. For instance, Honduras’ San Pedro Sula had been at the top of the list for four consecutive years - only overtaken by Caracas, Venezuela in the latest report (Seguridad Justicia y Paz, 2016). El Salvador has, at the time of writing, an average of twenty-four homicides per day (Marroquin, 2016), and Guatemala is the fifth country with the highest homicide rate in Latin America (Gagne, 2016). Most of the violence in these countries is generally attributed to the Maras, urban gangs that formed in marginalized neighborhoods in Los Angeles, California by Central American migrants and refugees, and then strengthened in the Northern Triangle following mass deportations from the United States, including the expatriation of criminals (Cruz, 2010).
 
 
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.019 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".