Latin American Homicide (Chapter 3)
Bibliographic record
Abstract
The urgency for studying Latin America and the Caribbean(LAC) emanates from violence rates that are remarkably higher than in most world regions. Global homicide rates have recently declined (Rogers & Pridemore, 2018; Tuttle et al., 2018). While LAC followed suit, its rates are still comparatively high, and it is home to less than 10% of the global population but about one-third of homicides. The World Health Organization defines violence as endemic if a nation reaches ten homicides per 100,000 residents. Over two-thirds of LAC, nations meet this threshold (World Health Organization, 2023), and the regional mean is above 10. The Pan American Health Organization deemed LAC violence the social pandemic of the century in the Americas (Imbush, 2011). Of the 50 most dangerous cities in the world – as measured by homicide rates – 42 are in LAC (Igarapé, 2023). Although not experiencing civil war, homicide rates in Brazil, Colombia, El Salvador, Guatemala, Honduras, and Mexico are at or above nations with active conflicts (Feldmann & Luna, 2022). Beyond the harm to individuals, families, and communities, the Inter-American Development Bank reported that crime and violence in the region cost a quarter trillion dollars annually, or 3.5% of the region's Gross Domestic Product (GDP) (Jaitman et al., 2017).
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.095 | 0.020 |
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".