Societal determinants of violent death: The extent to which social, economic, and structural characteristics explain differences in violence across Australia, Canada, and the United States
Bibliographic record
Abstract
In this ecological study, we attempt to quantify the extent to which differences in homicide and suicide death rates between three countries, and among states/provinces within those countries, may be explained by differences in their social, economic, and structural characteristics. We examine the relationship between state/province level measures of societal risk factors and state/province level rates of violent death (homicide and suicide) across Australia, Canada, and the United States. Census and mortality data from each of these three countries were used. Rates of societal level characteristics were assessed and included residential instability, self-employment, income inequality, gender economic inequity, economic stress, alcohol outlet density, and employment opportunities). Residential instability, self-employment, and income inequality were associated with rates of both homicide and suicide and gender economic inequity was associated with rates of suicide only. This study opens lines of inquiry around what contributes to the overall burden of violence-related injuries in societies and provides preliminary findings on potential societal characteristics that are associated with differences in injury and violence rates across populations.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| 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".