Protective and Risk Factors Associated With Youth Attitudes Toward Violence in Canada
Bibliographic record
Abstract
Adolescents and young adults are the main perpetrators and victims of violence in almost all parts of the world. Theories of human behavior predict that the intention to behave violently is formed in part by the individual’s attitude toward violent behavior. The purpose of this study was thus to investigate factors which both promote and protect against violent youth attitudes in Toronto, Canada’s largest urban center. Multinomial logit models were fit separately for males and females in Grades 7 to 9 using cross-sectional data from the 2006 International Youth Survey. Odds ratios were estimated for the associations between levels of attitude toward violence and select factors in each of the biological, familial, peer-related, school and community domains. A graded effect of school attachment on violent attitude was observed for both sexes; male and female students who do not like school at all are 9.89 (3.15-31.0) and 6.49 (2.19-19.2) times as likely as those who like school a lot to have the “most” versus “least” violent attitude, respectively. For every one-unit increase in (negative) perception of neighborhood score, male and female students are 1.15 (1.07-1.23) and 1.20 (1.12-1.28) times as likely to have the “most” versus “least” violent attitude. The number of victimization events was associated with attitude toward violence in males but not females, while the reverse was true for academic performance and exposure to prejudice. Our findings highlight the important relationships between connections to social environments and youth attitudes toward violence, and identify modifiable factors which may be amenable to intervention. Sex-specific differences in the predictors of violent youth attitudes warrant additional investigation and have implications for policy design.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".