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Record W4396715353 · doi:10.29173/mlj1299

Talking to Strangers: A Critical Analysis of the Supreme Court of Canada’s Decision in R v Mills

2022· article· en· W4396715353 on OpenAlexaboutno aff
Chelsey Buggie

Bibliographic record

VenueManitoba Law Journal · 2022
Typearticle
Languageen
FieldComputer Science
TopicHate Speech and Cyberbullying Detection
Canadian institutionsnot available
Fundersnot available
KeywordsWarrantSupreme courtOfficerLawEnforcementJurisprudenceLaw enforcementEconomic JusticePower (physics)Position (finance)Political scienceBusiness

Abstract

fetched live from OpenAlex

In R v Mills, an undercover officer acting without a warrant posed as a 14-year-old girl online and communicated with Mr. Mills through Facebook messages. The officer eventually arranged a meeting with, and arrested Mr. Mills who sought to have the message evidence excluded. The Supreme Court unanimously ruled to allow the evidence. However, only Justice Martin agreed that Mr. Mills’ s. 8 rights were engaged and infringed. This paper takes the position that the Mills decision is inconsistent with prior s. 8 jurisprudence regarding content neutrality and expectation of privacy in conversations. The type of sting operation used in Mills should have been classified as participant surveillance requiring a warrant. In Mills, the Supreme Court unduly adjusted the balance of power to favour law enforcement. The result of the Mills decision is that law enforcement may continue to use this investigative technique unregulated, and unencumbered. Such an adjustment in favour of law-enforcement is not justified. Other investigative techniques are available to law enforcement and obtaining a warrant would not unduly hinder child luring investigations. Failure to oversee these operations could have a potential chilling effect on legitimate online relationships and reinforce stereotypes about hypersexualized youth online.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.640
Threshold uncertainty score0.712

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.011
GPT teacher head0.233
Teacher spread0.222 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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
Published2022
Admission routes1
Has abstractyes

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