An Analysis of Collective Efficacy as a Predictor of Gun Violence in Toronto
Bibliographic record
Abstract
There has been a 42% increase in gun violence in Canada since 2013, largely due to increases in Toronto (Statistics Canada, 2022a). To gain a better understanding of this phenomenon, this study evaluated collective efficacy as a predictor of gun violence. Seven correlates of collective efficacy were identified including, low economic status, ethnic diversity, mobility, family disruption, employment rate, low educational attainment, and youth percentage in a population. This study included data from the City of Toronto’s Open Data Portal and the 2016 Canadian Census. The data were pulled from various datasets and then were reorganized into one file, which was then used to run a multiple regression analysis. This allowed for the assessment of the relationship between the multiple correlates of collective efficacy and gun violence. Ultimately, this research was able to provide evidence that collective efficacy is an accurate predictor of gun violence in Toronto’s neighbourhoods. Low economic status, ethnic diversity, employment rate, and youth percentage in a population were significant predictors of gun violence, and family disruption was a marginally significant predictor of gun violence. The results of this study are important as they directly advance knowledge regarding predicting gun violence using collective efficacy, and do so in a solely Canadian context. The results of this research can assist policy makers and community outreach programs to better identify and inform their gun violence reduction strategies across Toronto.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".