Bibliographic record
Abstract
Drones have been a hot topic in recent years particularly when used in war and in domestic police operations. Drones have also attracted attention because of highprofile plans to use them for package delivery, among other things. While the glamourous and future uses of drones catch media attention, drones are already being used in the private sector for more mundane purposes including surveying, infrastructure inspection and real estate sales promotion. While the privacy threats of military and police drones are widely discussed, privacy concerns of private drones have attracted much less consideration.\nThis paper looks at the privacy risks of private drones in Canada. It begins with an overview of the uses of private drones and their regulation in Canada. Regulation of drones in Canada is quite permissive and does not address the privacy risks. The paper then presents several privacy theories and a deeper discussion of two problems caused by technology such as drones: data aggregation and erosion of privacy in public. The paper then considers some theoretical and practical legal protections that might be used to protect against drone privacy invasion. The more theoretical include the torts of trespass and nuisance. The more practical include the tort of intrusion upon seclusion and the Personal Information and Electronic Documents Act. The paper concludes that the dominant theories of privacy embedded in Canadian law are not fully prepared for the challenge of drones, though the tort of intrusion upon seclusion holds some promise for the future.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.015 | 0.010 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 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".