Examining Micro-Level Homicide Patterns in Toronto, 1967 Through 2003
Bibliographic record
Abstract
This article assesses the spatial distribution and developmental pattern of micro-level homicide clusters in Toronto between 1967 and 2003. The spatial unit of analysis is the street segment and is defined as the two block faces on both sides of a street between two intersections. Three time periods (1967 to 1979, 1980 to 1989, and 1990 to 2003) covering 1,671 homicides were pooled to ensure sufficient numbers for analysis. Given the qualitative strength of the dataset, a series of descriptive statistics and geospatial statistics are used. Toronto’s developmental homicide pattern is characterized by a dense concentration of single-homicide street segments within the downtown core between 1967 and 1989, with the mass dispersal of multi-homicide street segments across the city between 1990 and 2003. Single-homicide street segments accounted for 84% and 81% of homicides between 1967 and 1979 and 1980 and 1989, respectively. However, multi-homicide street segments tripled between 1990 and 2003, rising from 16% of homicides in the first time period to 45% in the third. This reflects a change in the character of homicides with drug and gang-related homicides increasing in the third time period.
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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 | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| 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".