Understanding public opinion of UAVs in Canada: A 2014 analysis of survey data and its policy implications
Bibliographic record
Abstract
This study has two aims: first, assessing the knowledge of Canadians with regard to their awareness of the use of UAV technology for data collection; and second, testing the hypothesis that public opinion regarding the use of UAVs for data collection in Canada varies by application, by institution, by collection method, and by respondent demographics. The survey contains questions regarding awareness of UAV use in Canada, as well as (i) the degree of support found for use by specific groups, (ii) for law enforcement applications, (iii) for private or industry applications, (iv) for border or coastal surveillance, and (v) for visibility and data sharing practices. Polling data also enables the comparison of UAV support against traditionally piloted aircraft and automated UAVs. This study found a majority in support of the use of UAVs for safety or emergency-response purposes. However, this support falls away in cases where UAV are used to perform routinized acts of surveillance, or identification. These findings will be useful to legislators and regulators in developing policy on UAVs that takes into account public sentiment and opinion, and for private sector actors and governments in addressing public concerns about UAVs as the industry moves forward.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.013 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".