"Known to the Police": A Black Male Reflection on Police Violence in Toronto
Bibliographic record
Abstract
In response to the increased prevalence of gun violence in Toronto, local politicians and media have focused on how to more efficiently police the city's most violent neighborhoods. Because of the racialized nature of this violence, much attention has been given to the role of structural and institutional effects of systemic racism and marginalization on the manifestation of violence in the City of Toronto. Obscured from these discussions however are the ways in which narratives of criminality are internalized by Black and Brown bodies and their communities. In light of this, this research will highlight the lack of attention given to the discursive remapping, and the reimaging of the Black male body in urban spaces. Ultimately, what I propose is a radical decentering of this institutional paradigm in favour of one that takes the subjectivity of the Black male as its point of entry.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.067 | 0.029 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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".