Body Worn Cameras and police image work: News media coverage of the Rialto Police Department’s body worn camera experiment
Bibliographic record
Abstract
In 2015, the so-called “Rialto study” was published in a peer-reviewed journal, although the findings of this experiment impacted policing as early as 2013. The yearlong study of officers who wore Body Worn Cameras (BWCs) in Rialto, California found that among those officers who wore the devices, use of force incidents decreased, as did complaints against the officers. These findings were extensively profiled in news media and lauded by numerous police agencies across North America and the United Kingdom. This article examines reporting of the findings of the Rialto study in news media. BWCs have received considerably more coverage in news media than in the research literature. Practically no scholarship has addressed this issue. BWCs are said to enhance police legitimacy, or the judgements of citizens concerning police conduct. A great deal of police legitimacy concerns maintaining control over their public image in media as the legitimate authority, or image work. Given the importance of police image work and the coverage of BWCs in news media, it remains vital then that we understand how BWCs are discussed in media. Some suggestions for future research are noted.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".