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Record W4398236501 · doi:10.26443/law.v68i4.1363

“Must the Police Refuse to Look?”

2023· article· en· W4398236501 on OpenAlexaffvenueabout
Robert Diab

Bibliographic record

VenueMcGill Law Journal · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicPolicing Practices and Perceptions
Canadian institutionsThompson Rivers University
Fundersnot available
KeywordsPolitical scienceCriminologyComputer securityBusinessLawLaw and economicsComputer scienceSociology

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.879
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0090.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.002

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.

Opus teacher head0.103
GPT teacher head0.408
Teacher spread0.305 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes3
Has abstractyes

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