Protecting the Right to Privacy in Digital Devices: Reasonable Search on Arrest and at the Border
Bibliographic record
Abstract
Canada’s courts in recent years have consistently recognized a high degree of privacy in the content of digital devices. Yet the law authorizing device searches on arrest and at the border has failed to reflect this higher interest. In both contexts, courts have assumed that the state has a compelling interest in immediate access to device data to advance pressing law enforcement objectives — but the claim is not supported by evidence. This paper builds upon earlier critical views of device search law and policy by demonstrating that searches are being carried out on arrest and at the border without clear limits, resulting in significant intrusions into personal privacy, and without effective avenues of recourse. Part I critically examines the Supreme Court’s justification in Fearon for authorizing device searches on arrest, including its dismissal of the US Supreme Court’s approach in Riley v California (requiring a warrant). It then presents evidence to support the dissent’s argument that the majority’s test provides ineffective guidance to police to avoid unreasonable searches, and that the exclusion of evidence is not an adequate remedy. Part II examines the Canada Border Services Agency’s rationale and practice for groundless device searches under the Customs Act. It considers proposals for reform, including a Parliamentary report in late 2017 recommending a requirement of reasonable suspicion. Finally, it argues that the guarantee against unreasonable search in section 8 of the Charter requires a warrant for device searches at the border, because the state’s interest in searching devices there is less pressing than the state’s interest in searching a person.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.025 |
| Scholarly communication | 0.017 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.011 | 0.013 |
| Insufficient payload (model declined to judge) | 0.004 | 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".