BALAS Y BARRIOS: AN ANALYSIS OF U.S. DOMESTIC AND REGIONAL ANTI-GANG POLICIES FROM A HUMAN SECURITY PERSPECTIVE
Bibliographic record
Abstract
Threats to human security from transnational organized crime (TOC) and gangs have increased since the 1990s in the Americas. The United States implemented the Strategy to Combat Transnational Organized Crime, the U.S. Strategy to Combat the Threat of Criminal Gangs from Central America and Mexico, and the Mérida Initiative in response. This thesis employs a multi-goal policy to evaluate how effectively U.S. policy responses achieved desired outcomes. For comparison, this thesis analyzes the Canadian gang violence strategy, examining what has worked and what has not worked. Findings demonstrate that law enforcement tactics prioritized within the U.S. strategy result in outputs, but they fail to impact gang violence outcomes. Prevention programs, on the other hand, both in Canadian and U.S. strategies, are effective in reducing gang crime and violence but are under-resourced and undervalued in U.S. endeavors. This thesis proposes that a comprehensive approach is better aligned with current expert gang research and more effective in producing desired outcomes. Recommendations include funding the Juvenile Justice Reform Act and rebalancing Mérida funding to support United States Agency for International Development prevention programs; integrating federal, state, and local partnerships through a community coalition council through the Department of Justice; evaluating the Treasury’s TOC designation status; and supporting complementary prevention and rehabilitation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".