Crime and Public Support for the Rule of Law in Latin America and Africa
Bibliographic record
Abstract
Abstract Crime poses a formidable obstacle to democratization in many parts of the developing world. New democracies in Central America and sub-Saharan Africa face some of the highest homicide rates in the world. Politicians, citizens, and policy-makers have raised the alarm about the growing tide of criminality. Public insecurity, coupled with inefficient and often corrupt justice systems, makes democratization uncertain. Even if new democracies do not revert to dictatorship, the quality of democracy may suffer if crime continues to rise. One particularly vulnerable component of democracy is the rule of law, as public insecurity may fuel support for extra-legal justice, and a willingness to disregard the law while aggressively pursuing suspected criminals. To test these relationships, we assess the ways in which criminal victimization, as well as fear of crime, affect citizen support for the rule of law. We utilize public opinion data collected in select countries in Latin America and sub-Saharan Africa through two widely used sources – the Latin American Public Opinion Project (LAPOP) and the Afrobarometer surveys.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".