Bibliographic record
Abstract
Courts in Canada are dealing more frequently with an old problem in a new guise: civilians bringing police digital evidence that engages a suspect’s privacy interest (text messages, email). Do police carry out a seizure when they receive it or a search when they proceed to review it, even briefly? Should police ‘refuse to look’ before obtaining a warrant or other authorization? If so, why? What measure of protection would calling this a search or seizure under section 8 of the Charter afford Canadians? The Supreme Court of Canada has yet to decide these issues directly, and trial, appeal courts, and commentators have offered widely diverging responses to the questions they raise. In doing so, courts and commentators alike have lost sight of the Supreme Court’s principled approach to what constitutes a search or seizure and when it will be reasonable. Applying this approach in R v Marakah, McLachlin CJ in obiter held that receiving a text exchange from a third party would require police to obtain a warrant before reading it, but she provided no rationale. This article articulates the Court’s principled approach and shows why diverging approaches among recent courts and commentators are not compelling. More crucially, given how central digital communication has become to all of us, the article sets out a rationale for insisting on a warrant before police review texts or photos, and what is at stake in failing to provide this vital safeguard.
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 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 | 0.007 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.030 | 0.047 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.009 | 0.011 |
| 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".