Homicídios na região das Américas: magnitude, distribuição e tendências, 1999-2009
Bibliographic record
Abstract
The scope of this study was to describe the magnitude and distribution of deaths by homicide in the Americas and to analyze the prevailing trends. Deaths by homicide (X85 to Y09 and Y35) were analyzed in 32 countries of the Americas Region from 1999 to 2009, recorded in the Mortality Information System/Pan American Health Organization. A negative binomial model was used to study the trends. There were around 121,297 homicides (89% men and 11% women) in the Americas, annually, predominantly in the 15 to 24 and 25 to 39 year age brackets. In 2009 the homicide age-adjusted mortality rate was 15.5/100,000 in the region. Countries with lower rates/100,000 were Canada (1.8), Argentina (4.4), Cuba (4.8), Chile (5.2), and the United States (5.8), whereas the highest rates/100,000 were in El Salvador (62.9), Guatemala (51.2), Colombia (42.5), Venezuela (33.2), and Puerto Rico (25.8). From 1999-2009, the homicide trend in the region was stable. They increased in nine countries: Venezuela (p<0.001), Panama (p<0.001), El Salvador (p<0.001), Puerto Rico (p<0.001); decreased in four countries, particularly in Colombia (p<0.001); and were stable in Brazil, the United States, Ecuador and Chile. The increase in Mexico occurred in recent years. Despite all efforts, various countries have high homicide rates and they are on the increase.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".