Beyond Gang Truces and Mano Dura Policies: Towards Substitutive Security Governance in Latin America
Bibliographic record
Abstract
With responses to urban violence receiving increasing academic attention, the literature on anti-gang efforts in Latin America has focused mainly on coercive mano dura policies and cooperative gang truces. Yet, there remains a paucity of studies going beyond such carrots-and-sticks approaches towards gangs. To fill this gap, this study investigates the possibilities and limitations of substitutive security governance across Latin America and the Caribbean. More specifically, this article looks at Disarmament, Demobilisation and Reintegration (DDR) programmes in Medellín, Armed Violence Reduction and Prevention (AVRP) efforts in Haiti and Security Sector Reform (SSR) in Guatemala and Rio de Janeiro. It will be argued that communities are driven to support gangs against the oppressive state when they are indiscriminately targeted through muscular operations. Likewise, engaging gangs in dialogue grants them legitimacy and further weakens the position of the state. Therefore, the only sustainable solution lies in substitutive security governance, which aims to replace the functions gangs fulfil for their members, sponsors, and the community in which they are nested with a modern and accountable state that is bound by the rule of law. Still, substitutive strategies vis-à-vis gangs have their own limitations, which can only be overcome by way of an integrated and coordinated framework.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".