Police body‐worn camera policies as democratic deficits? Comparing public support for policy alternatives
Bibliographic record
Abstract
Abstract Research Summary Policies that govern the use of body‐worn cameras (BWCs) by police vary widely between American cities. However, it is currently unclear whether citizen preferences for these policies vary in a similar manner. More specifically, do BWC policies reflect citizen preferences or are existing policies disfavored by a majority of the public? To investigate these questions, we randomly sampled 1000 respondents for each of the three representative metropolitan areas, Los Angeles, CA; Seattle, WA; and Charlotte, NC, in addition to a further 1000 Americans across the country to inquire about policy preferences. We found that most respondents prefer the BWC policies recommended by the American Civil Liberties Union (ACLU) to those currently implemented in their regional police departments. In other words, elements of the BWC policies in Los Angeles, Seattle, and Charlotte do not reflect residents’ preferences. Policy Implications The policy stating that footage access should be given to parents of minors, a deceased subject's family members, or anyone filmed in an encounter, a model promoted by ACLU, is a clear favorite in the United States at large, but also in the three cities we studied. The policy stating that footage access should not be given to superior officers to find disciplinary infractions, also backed by the ACLU, is less popular among Americans at large and residents of Seattle. Beyond the high support for BWCs within the American population, decision makers need to make sure that the policies that govern the use of this tool respect democratic principles. Therefore, the voice of citizens needs to be heard to avoid a democratic deficit.
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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 | 0.024 | 0.132 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".