‘There’s A Certain Group of Cops that have their Own Vendetta’: Resident Perceptions of Notorious Police Officers and ‘Cop Clockin’ in the Inner-City
Bibliographic record
Abstract
Abstract Many inner-city neighbourhoods across North America are disproportionately subject to heightened and aggressive policing strategies. Consequently, many inner-city residents have developed strategies to limit their encounters with police. Research examining police–community relationships has traditionally examined how residents perceive and respond to ‘the police’. However, this homogenizes and over-simplifies nuanced processes. Based upon 48 interviews with Toronto inner-city residents, we demonstrate how narratives about allegedly ‘notorious’ officers reveal that residents differentiate between individual officers and modify their behaviours accordingly. This process of officer differentiation—‘cop clockin’—results in strategic responses to specific officers as residents attempt to hinder the potential harms of interactions with ‘notorious cops’. Furthermore, officer complacency raises questions about police legitimacy and strengthening police–community relationships.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".