Police UAV use: institutional realities and public perceptions
Bibliographic record
Abstract
Purpose The purpose of this paper is to explore institutional realities and public perceptions of police use of unmanned aerial vehicles (UAVs) in Canada in relation to each other, drawing attention to areas of public misunderstanding and concern. Design/methodology/approach Public perceptions data are drawn from a national survey ( n =3,045) of UAV use. Institutional realities data are drawn from content analyses of all Special Flight Operation Certificates issued by Transport Canada from 2007 to 2012 and flight logs of a regional service kept from 2011 to 2013. Officer interviews ( n =2) also provide qualitative insights on institutional realities from this same regional service. Findings The data reveal disparities between institutional realities and public perceptions. Although federal, provincial and regional services currently use UAVs, awareness of police use of UAVs relative to traditionally piloted aircraft was low. Further, support for police use of UAVs was significantly lower than traditionally piloted craft; but, support also varied considerably across UAV applications, with the greatest opposition tied to tasks for which police do not report using UAVs and the greatest support tied to tasks for which police report using UAVs. Originality/value This research provides previously unknown descriptive data on the institutional realities of police use of UAVs in Canada, positioning that knowledge in relation to public perceptions of police use of the technology. The findings raise concerns over how UAVs may negatively shape police/civilian relations based on procedural justice literature which demonstrates that a lack of public support for the technology may affect the police more broadly.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".