Firearm Homicide in Australia, Canada, and New Zealand: What Can We Learn From Long-Term International Comparisons?
Bibliographic record
Abstract
Although firearm homicide remains a topic of interest within criminological and policy discourse, existing research does not generally undertake longitudinal comparisons between countries. However, cross-country comparisons provide insight into whether "local" trends (e.g., declines in firearm homicide in one particular country) differ from broader, international trends. This in turn can improve knowledge about the role of factors such as policing practices and socioeconomic variables in the incidence of lethal violence using firearms. The current study compares long-term firearm homicide trends in three countries with similar social histories but different legislative regimes: Australia, Canada, and New Zealand. Using negative binomial regression, the study found that the most pronounced decline in firearm homicide over the past two decades occurred in New Zealand. Connections between social disadvantage, policing policy, and violence are discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".