Perspectives on Firearm Violence, and Their Impact on Youth Identity in the Greater Toronto Area, from the Outlook of Youth Outreach Workers
Bibliographic record
Abstract
How do perceptions of firearm violence influence the formation of at-risk youth identities in the Greater Toronto Area (GTA) from the Youth Outreach Workers (YOW) perspective? This thesis research examines how perceptions of firearm violence have influenced the formation of youth (15-20 years of age) identities in the Greater Toronto Area (GTA). Recent literature has called attention to the firearm situation in the United States, reflecting the country’s public rhetoric on firearm issues. Relative to Canadian literature, there has been little time spent on recognizing younger generational perspectives. Academics mention the vulnerability of these populations to violent offending (O’Grady 2014, 51), and even community engagement initiatives to firearm violence (Ezeonu 2008). Since youth’s views are under-acknowledged in literature, my research intends to fill this gap. My research answered three questions: Firstly, how do youth perceive the significance of firearm violence in the GTA? Secondly, how will a youth perspective help with policy discussions of firearm violence in the GTA? Finally, how are the intersectional dynamics of gender, race, and class included in youth perspectives on firearm violence?
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".