Review of Fan’s Camera Power: Proof, Policing, Privacy, and Audiovisual Big Data
Bibliographic record
Abstract
Police body-worn cameras (BWCs) and civilian cameras continue to proliferate across the US, the UK, and Canada.As more BWCs and civilian cameras are adopted, the total collected audiovisual data increases.This collected data increase poses new challenges and opportunities for governments, police services, and the public.Camera Power: Proof, Policing, Privacy, and Audiovisual Big Data provides an excellent description of these new challenges and opportunities alongside various ethical, legal, and policy implications from the vast stores of audiovisual data and the continued proliferation of both police and civilian cameras.Fan argues that more cameras and their collected data, both from police and civilians, is not necessarily bad.These collected data can be bad, and there are serious ethical, legal, policy, and privacy concerns around the use of these collected data.However, if these concerns are navigated correctly, these data can provide better police training, public trust, and predictive analytic ability.Camera Power examines these concerns and potentials, spending its pages discussing both sides of each primary and sub-argument, offering academic literature, personal accounts, and legal decisions as support for the various arguments.The book ends with a short conclusion on how BWCs are "no technological silver bullet" (251); BWCs cannot solve the hard policy and law enforcement problems.However, BWCs do provide an avenue for both sides of the political divide to communicate, a necessity for navigating the ethical, legal, and policy concerns about BWCs.
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.
How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".