Shots Fired: Experiences of Gun Violence and Victimization in Toronto Social Housing
Bibliographic record
Abstract
In my dissertation, I examine how residents of a Toronto social housing project called Lawrence Heights – a de facto Canadian ghetto – manage the day-to-day realities of gun violence and victimization in their neighbourhood. Grounded in nearly 5-years of ethnographic fieldwork (including 75 formal interviews, hundreds of informal interviews, and thousands of pages of ethnographic field notes), my project engages with literature on street knowledge, street codes, and victimization to explore how random and recurring gun violence affects the actions and perceptions of local residents. More specifically, it examines how young black men in Lawrence Heights – the exclusive targets of gun violence in this community – negotiate the social and spatial realities of danger and risk in their neighborhood, relying on what I call ‘neighbourhood wisdom’ (chapter 3), ‘the code of survival’ (chapter 4), and the ‘on point - slipping framework’ (chapter 5). Ultimately, my findings illustrate that despite living in a de facto ghetto characterized by concentrated poverty, lethal violence, and disorder, residents of this Toronto social housing project have found ways to allow social and community life to continue – adapting, in other words, to an otherwise paralyzing socio-spatial milieu. This dissertation sheds light on the lived experiences of one of Canada’s most marginalized populations, calling for more nuanced and ‘on the ground’ understandings of poverty, crime, and victimization in the Canadian context.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.027 | 0.014 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".