Documenting Contact and Thinking with Skin: A Dermatological Approach to the Study of Police Street Checks
Bibliographic record
Abstract
Abstract In contrast to quantitative studies that rely on numerical data to highlight racial disparities in police street checks, this article offers a qualitative methodology for examining how histories of anti-Blackness configure civilians’ experiences of present-day policing. Taking theHalifax Street Checks Reportas our primary object of analysis, we apply an innovative dermatological approach, demonstrating how skin itself becomes meaningful when police officers and civilians make contact in the process of a street check. We explore how street checks become an occasion for epidermalization, whereby a law enforcement practice projects onto the skins of civilians locally specific histories and emotions. To think with skin, we focus on the narratives shared by African Nova Scotians, a group that has been street checked at higher rates than their white counterparts. By doing so, we argue that current debates about police street checks in Halifax must attend to the emotional stakes of police-initiated encounters in order to fully appreciate the lived experience of street checks for Black civilians.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.014 | 0.027 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".