The marginalization correlatives of high homicide neighbourhoods in the City of Toronto
Bibliographic record
Abstract
This research uses homicide and marginalization data from the city of Toronto in order to understand whether, or to what extent, socio-economic marginalization has an impact on homicide counts at the neighbourhood level. This research uses a three stage methodology to answer this question. Firstly, a negative binomial regression was used to understand the relationship between socio-economic marginalization and lethal violence at the neighbourhood level. The residuals of this model were used to understand where and how this relationship varied. Next, Emerging Hot Spot Analysis was used to determine which Toronto neighbourhoods had high levels of homicide across time. Finally, the marginalization characteristics of these areas were examined to provide insight. This research found that the only marginalization variable that had a statistically significant impact on homicide counts was Material Deprivation. This echoes the criminological consensus on the subject. With regards to the Emerging Hot Spot Analysis, it was demonstrated that homicide in the city of Toronto does exhibited spatially and temporally persistent clustering. Keywords: Homicide, Negative Binomial Regression, Emerging Hot Spot Analysis, Neighbourhood Level, Marginalization.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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) | 0.002 | 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".