Leaving Dumb Phones Behind: A Commentary on the Warrantless Searches of Smartphone Data Granted in R. v. Fearon
Bibliographic record
Abstract
Given the incredible rate of smartphone technological evolution, is it about time the Supreme Court of Canada devised a special test to give law enforcement agents significantly more power to search through phone data without a warrant upon arrest of a suspect? In R. v. Fearon, the majority did just that. But this article argues the opposite is true: the increasing potential for immense privacy infringements when police search powerful and constantly evolving technological devices demands a greater limitation to police powers.\nIn recent cases, the Supreme Court has agreed with the position that limitations are needed concerning computers. Additionally, the weaknesses in law enforcement procedure described by the majority are already served sufficiently by existing principles which do not infringe Canadians’ Charter rights. Future cases should distinguish the majority decision for these reasons and recognize the thoughtful and practical dissent. Otherwise, there is a danger that this unreasonable expansion of police power to search citizens, combined with anticipated technological evolution in both smartphones and government surveillance initiatives, will have a corrosive effect on the freedom guaranteed to Canadians by section 8 of the Charter.
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 | 0.024 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.032 | 0.024 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.012 | 0.005 |
| Research integrity | 0.083 | 0.077 |
| 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".