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Record W7070796539

Revisiting the “Private Use Exception” to Canada’s Child Pornography Laws: Sexual Expression/Sexting, Control, Privacy, and Pleasure in the Digital Age

2019· article· en· W7070796539 on OpenAlexaboutno aff

Bibliographic record

VenueeYLS (Yale Law School) · 2019
Typearticle
Languageen
FieldEngineering
TopicMineral Processing and Grinding
Canadian institutionsnot available
Fundersnot available
KeywordsChild pornographyCLARITYPleasureSupreme courtPornographyChild protectionThe Internet
DOInot available

Abstract

fetched live from OpenAlex

In R. v Sharpe (2001), the Supreme Court of Canada read in a “private use exception” to the offence of possessing child pornography. The Court reasoned that youths' self-created expressive materials and private recordings of their lawful sexual activity would pose little or no risk to children and may in fact be of significance to adolescent self-fulfillment, self-actualization, sexual exploration, and identity. Fundamental changes in the technological, social, sexual, and legal landscape since Sharpe have resulted in a lack of clarity regarding the exception’s scope. Federal and provincial police and federally funded child protection agencies now regularly inform young people that they do not have the legal right to consensually create and share their digital sexual images with an intimate partner. Scholarly opinion on the exception’s application to teenage sexting is under-considered and varied, and subsequent judicial interpretations of the exception have extended the boundaries of private use while also circumscribing the protection by requiring youth to retain the ability to “maintain control” of their images. Via a mapping of our new technological and legal landscape, as well as a consideration of shifting privacy, communication, and sexual norms, this article seeks to examine and clarify the application of the private use exception to teenagers’ contemporary digital sexual expression practices.

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)0.000
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 score0.542
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.009
GPT teacher head0.203
Teacher spread0.194 · 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
Published2019
Admission routes1
Has abstractyes

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