A Social and Spatial Analysis of Firearm Related Incidents in Toronto
Bibliographic record
Abstract
The overall objective of this paper is to determine the location of spatio-temporal clustering of Firearm Related Incidents (FRI) in the City of Toronto between 2014-2018, to determine specific demographic, economic and social variables that would most likely be impacted by FRI violence and explore potential locations to host support groups for populations disproportionally impacted by FRI. Utilizing PCA/factor analysis and a grouping analysis (K-Means) was done as an attempt to minimize the presence of ecological fallacy and reduce the impact of spatial stigma typically associated to subgroups of the population historically impacted by FRI. A regression model was done to test the validity of the grouping analysis and finally a location-allocation model was produced to identify potential support centre locations. Findings revealed that FRI in Toronto rose between 2014-2017 but decreased in 2018. There is spatial auto-correlation occurring throughout all years and areas historically impacted see a rise in FRI as years progress. Findings also revealed that variables for low educational attainment, low income, high visible minority status, lone parents and those between the ages of 0-19 were more likely to encounter FRI. A total of 21 potential locations were identified in a way to alleviate the side effects of FRI occurring in identified areas.
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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 | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 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) | 0.003 | 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".