Bibliographic record
Abstract
People offer accounts in response to actions that are subjected to valuative inquiry. Recordings of police actions captured on body‐worn camera (BWC) video can become subject to valuative inquiry when footage is publicly released. This footage may possibly undermine police legitimacy. Managing legitimacy is a basic rationale for why police provide accounts in response to their conduct. Proponents of BWCs assert that the devices enhance legitimacy and, while police have long used accounts to justify their conduct, less is known about how body cameras affect police accountability in practice. Drawing from Scott and Lyman's accounts theory framework, this qualitative, exploratory study examines official police accounts as both outcomes of police accountability in practice and as provided in the context of news media coverage of publicly released body‐worn camera footage. This allows us to ask the following questions: What types of accounts have police officials provided in news media reports in the context of publicly released BWC footage? And, more generally, what insight might an analysis of police accounts provide about accountability in relation to the implementation of BWCs? A key finding reveals that the timing of police accounts varied quite considerably, and that accounts were usually not static. The findings provide some general insight into what kinds of actions captured on body camera recordings constitute acceptable use of force by officers, even in situations when police actions resulted in death. This study also provides a small empirical window into when and how police officials provide accounts in response to publicly released body camera footage.
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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 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".