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Record W3121699755

A "Reasonable" Expectation of Sexual Privacy intheDigital Age

2018· article· en· W3121699755 on OpenAlexvenueno aff
Moira Aikenhead

Bibliographic record

VenueDalhousie law journal · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicLegal Systems and Judicial Processes
Canadian institutionsnot available
Fundersnot available
KeywordsPrivacy lawInformation privacyInternet privacyLawPsychologyBusinessComputer sciencePolitical sciencePrivacy policy
DOInot available

Abstract

fetched live from OpenAlex

Two Criminal Code offences, voyeurism, and the publication of intimate images without consent, were enacted toprotect Canadians' right to sexual privacy in light of invasive digital technologies. Women and girls are overwhelmingly targeted as victims for both of these offences, given the higher value placed on their non-consensual, sexualised images in an unequal society.Both offences require an analysis ofwhether the complainant was in circumstances giving rise to a reasonable expectation of privacy, and the use of this standard is potentially problematic both from a feminist standpoint and in light of the rapidly evolving technological realities of the digital age. This article proposes a feminist-inspired, technology-informed approach to the reasonable expectation of privacy standard in relation to these offences, and examines the extent to which the Supreme Court of Canada's recent voyeurism decision, R.v. Jarvis, aligns with this approach.

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)0.008
metaresearch head score (Gemma)0.034
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.012
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.034
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0050.007
Scholarly communication0.0060.004
Open science0.0010.003
Research integrity0.0060.005
Insufficient payload (model declined to judge)0.0120.001

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 head0.027
GPT teacher head0.311
Teacher spread0.283 · 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 designTheoretical or conceptual
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

Citations2
Published2018
Admission routes1
Has abstractno

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