Similarities in Homicide Trends in the United States and Canada
Bibliographic record
Abstract
The decrease in the overall homicide rate in the United States during the latter 1990s has been explained in terms of changes in various factors such as the availability of guns, crack markets, and demographics. Although these are all plausible explanations, they do not explain why Canada has experienced similar declines in homicide rates during that same period. Homicides in Canada are qualitatively different from homicides in the United States, and thus changes in gun availability or crack markets are likely not behind the decrease in Canada’s homicide rate. However, changes in demographics might be one explanation behind Canada’s decreasing homicide rate. Analyses revealed that as in U.S. research findings, changes in demographics appear to account for roughly 14% of Canada’s decreasing homicide rate. Thus, although the homicides are qualitatively different from one another, demographics appear to account for similarly small proportions of the change in homicide rates in both countries. Although Canada has a much lower homicide rate than the United States, homicide trends are somewhat similar in both countries. Figure 1 shows the overall U.S. homicide rate and overall Canadian homicide rate from 1961 to 1999. In 1961, Canada’s homicide rate was 1.28 (per 100,000) whereas the U.S. homicide rate was 4.8 (per 100,000). Both countries saw increases in their homicide rates until about 1975. Throughout the 1980s, there were year-to-year fluctuations in both countries, but generally both countries saw increases in homicide rates from the mid to late 1980s until about
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".