The Spatial Distribution and Social Context of Homicide in Toronto’s Neighborhoods
Bibliographic record
Abstract
Objectives: To examine the social ecology of homicide in Toronto, Canada. Method: Using both ordinary least squares regression and negative binomial models, we analyze the structural correlates of 965 homicides occurring in 140 neighborhoods in Toronto between 1988 and 2003. Results: Similar to research in U.S. cities, Toronto neighborhoods with higher levels of economic disadvantage, higher proportions of young and Black residents, and greater residential instability have higher homicide rates. In contrast to U.S. studies, Toronto neighborhoods with higher proportions of residents who are recent immigrants also have higher homicide rates. In multivariate models, only two of these characteristics—economic disadvantage and the proportion of residents aged 15 to 24—are significantly associated with homicide in Toronto’s neighborhoods. Despite low levels of both lethal violence and spatial inequality in Toronto, the correlates of homicide in its neighborhoods are similar in some respects to those in U.S. cities. Conclusion: Our findings lend support to the notion of invariance in some ecological covariates of homicide but also highlight the need to be cautious about generalizing from U.S.-based research on the relationship between immigration and homicide.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".