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Record W3198091109

Divorced from (Technological) Reality: A Response to the Supreme Court of Canada's Reasons in R. v. Fearon

2015· article· en· W3198091109 on OpenAlexaffabout
Colton Fehr, Jared Biden

Bibliographic record

VenueSSRN Electronic Journal · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicEuropean Criminal Justice and Data Protection
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsWarrantPhoneSupreme courtLawPolitical scienceSupreme Court DecisionsBusiness
DOInot available

Abstract

fetched live from OpenAlex

This article critically examines the recent Supreme Court of Canada decision in R. v. Fearon, which allows police officers to search an arrestee’s cell phone incident to arrest. We assert that the majority in Fearon did not take into consideration the current (and constantly evolving) technological realities associated with cell phones, and especially smart phones. This oversight has resulted in a decision which is difficult to implement and carries the risk for future violations of constitutional rights. We argue that permitting the search of cell phones incident to arrest is unnecessary given that the law already contains doctrinal tools, such as exigent circumstances, which allow police officers to search a cell phone without a warrant when necessary. Ultimately, unless one of these exceptions applies, police should be required to obtain a warrant before searching a cell phone. They should not have the power to search a cell phone incident to arrest.

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 imitation

Not 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.

metaresearch head score (Codex)0.019
metaresearch head score (Gemma)0.048
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.131
Threshold uncertainty score0.950

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0190.048
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.004
Science and technology studies0.0430.025
Scholarly communication0.0200.006
Open science0.0080.008
Research integrity0.0300.041
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.039
GPT teacher head0.292
Teacher spread0.254 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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
Published2015
Admission routes2
Has abstractyes

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