Body-worn cameras, police violence and the politics of evidence: A case of ontological gerrymandering
Bibliographic record
Abstract
Public demands for greater police accountability, particularly in relation to violence targeting Black and Brown communities, have placed pressure on law enforcement organisations to be more transparent about officers’ actions. The implementation of police body-worn cameras (BWCs) has become a popular response. This article examines the embrace of BWCs amidst the wider shift toward evidence-based policing by scrutinising the body of research that evaluates the effects of these technologies. Through an intertextual analysis informed by insights from Critical Race Theory and Science and Technology Studies, we illustrate how the privileging of certain forms of empiricism, particularly randomised controlled trials, evinces what Woolgar and Pawluch describe as ontological gerrymandering. In doing so, the emergent evidence base supporting BWCs as a policing tool constitutively redefines police violence into a narrow conceptualisation rooted in encounters between citizens and police. This analysis examines how these framings, by design, minimise racialised power relations and inequalities. We conclude by reflecting on the implications of these evidence-based claims, arguing that they can direct attention away from – and thus can buttress – the structural conditions and institutions that perpetuate police violence.
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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 | 0.176 | 0.185 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.018 | 0.200 |
| Scholarly communication | 0.023 | 0.034 |
| Open science | 0.004 | 0.021 |
| Research integrity | 0.019 | 0.024 |
| 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".