Shots Fired: Experiences of Gun Violence and Victimization in Toronto Social Housing
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it