Camera-friendly Policing: How the Police Respond to Cameras and Photographers
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
How do police respond to the presence of cameras and photographers? Many speculative theories have been proposed offering mixed and sometimes contradictory answers to this question. Some theories propose that cameras will deter police misconduct, others suggest that cameras might improve police accountability, others suggest that police might respond to cameras by engaging in a risk-averse style of policing. Unfortunately, little empirical data is available to assess these theories. Drawing on data from a participant-observation research study conducted in Edmonton, Alberta, Canada, this paper helps fill this gap in research and argues that police might be learning to adapt to cameras by engaging in what I call camera-friendly policing. This style of policing involves efforts to control how the police are perceived by photographers, and how they will be perceived by viewers of any recorded footage. In this paper, I outline the basic elements of the police’s camera-friendly tactics, and discuss the implications of these tactics for contemporary understandings of police visibility.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it