Bibliographic record
Abstract
Computers have been an indispensable part of our lives for at least two decades. Given the extent of our dependency on computers and the vast amounts of information that they contain, it was inevitable that they would become the focal point of criminal investigations. The unique privacy concerns raised by computers create special challenges for search and seizure law under section 8 of the Charter. In recent years, the Supreme Court of Canada has decided several important cases dealing with the search and seizure of computers under section 8. Most recently, in R. v. Vu, the Court held that the police cannot search the contents of a computer upon executing a search warrant on the place in which the computer is found unless the warrant specifically authorizes the search of that computer. The Court also made some useful comments concerning the regulation of the manner of computer searches, including: (i) the manner of computer searches will generally be reviewed after the fact if and when a Charter challenge is brought; and (ii) in certain cases, it may be appropriate to impose search protocols (i.e., ex ante conditions spelled out in the warrant to limit the scope of the search). This paper seeks to build on the Court’s statements and imagine the post-Vu world of computer search and seizure law. The paper first summarizes Vu and the propositions for which it stands. It next takes up Vu’s invitation to carefully examine the manner of computer searches and draws on lower court decisions in an attempt to tease out some general principles to guide ex post review. It the n analyzes the issue of search protocols and when it might be appropriate — and, indeed, constitutionally required — for authorizing justices to impose such protocols before computer searches are conducted.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".