“I’ve Never Been Straight Up Robbed Like That”: Resident Perceptions and Experiences of Inner-City Police Raids
Bibliographic record
Abstract
Empirical research has consistently demonstrated that residents of disadvantaged and racialized inner-city neighborhoods across North America are subjected to disproportionate and omnipresent policing. Consequently, relationships between law enforcement officials and marginalized community members are often strained. Whilst a robust body of literature has examined how citizens perceive “every day” policing practices such as “carding,” stop and search, etc., it remains unclear how citizens perceive more invasive policing encounters—such as police raids. Drawing upon 35 interviews with residents of Toronto’s inner-city, this paper explores how community members experience, make sense of, and talk about police raids. Our data uncover widespread perceptions of nefariously motivated police misconduct, raise questions about how residents anticipate and expect police to treat them, and highlight nuances in how these experiences shape police legitimacy views. We argue that how residents perceive police to behave during raids matters, as this can damage perceptions of police legitimacy for some residents, while merely reaffirming existing views for others.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".