Neighborhood, school, and individual effects on substance use, violence, and the drugs/violence nexus: A study of Toronto secondary students
Bibliographic record
Abstract
This research examines the relative impact of neighborhood, school, and individual effects on a variety of substance use measures, violent victimization, violent perpetration, and the drugs/violence nexus. A unique dataset of Toronto area secondary students, the Drugs Alcohol Violence International (DAVI) survey, containing many measures of substance use, violence, school atmosphere, and family environment is used. The dataset also contains the postal codes of respondents allowing for the merger of the survey data and Canadian Census data for 2001. The theoretical framework for this research combines elements of Shaw and McKay's (1942) social disorganization theory and Sampson and colleague's (1997) work on collective efficacy at the macro level and social control theory at the micro level. At the individual level, I extend the logic of Sampson et al.'s (1997) community level collective efficacy to the domains of the school and family. School and family efficacies encompass similar notions of shared expectations and values found in the work on collective efficacy. Cluster analysis is used to create groups of similar neighborhoods based on their combination of neighborhood disadvantage (as measured by proportion of immigrants and low income residents) and collective efficacy. The results indicate four distinct groups of neighborhoods in the sample. Cluster 1 contains a high concentration of immigrants, is 92% non-white, and has low collective efficacy. Cluster 2 has high levels of disadvantage, is more ethnically heterogeneous, and exhibits average collective efficacy. Cluster 3 is neither advantaged nor disadvantaged, but has the lowest collective efficacy. Cluster 4 is advantaged, has the lowest proportion of non-whites, and the highest collective efficacy. Multinomial logits are used to analyze the frequency of alcohol and marijuana use. Generalized linear models are used for the number of hard drugs used, the rate of violent victimization, and the rate of violent perpetration. Results indicate that the logic of collective efficacy can be successfully extended to the school and family. The effect of family and school efficacy and demographic measures varies by neighborhood context, as does the significance and strength of the drugs/violence nexus. Recommendations for policy and future research are included.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".