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Record W4399636721 · doi:10.1080/23268743.2024.2355902

Age-verification technologies and the censorship of online pornography in Canada: a critique of Bill S-210: An Act to Restrict Young Persons’ Online Access to Sexually Explicit Material

2024· article· en· W4399636721 on OpenAlexaffabout
Kyler Chittick

Bibliographic record

VenuePorn Studies · 2024
Typearticle
Languageen
FieldPsychology
TopicSexuality, Behavior, and Technology
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPornographyCensorshipHuman sexualityContext (archaeology)QueerLawSexual revolutionSociologyHomosexualityPolitical sciencePsychologyGender studiesHistory

Abstract

fetched live from OpenAlex

Written in the style of an opinion editorial, this short article serves as a critical examination of Bill S-210, which proposes to restrict minors’ online access to sexually explicit material in Canada through age-verification technologies. Framed within the historical context of obscenity law and the feminist sex wars, the article underscores the impact of censorship and anti-pornography feminism on queer businesses and sexual expressions. Emphasizing the need for comprehensive sexual education rather than increased censorship, it critiques the impracticality of age-verification technologies as well as contemporary cultural anxiety around youth sexuality and the perceived need to ‘protect’ women and minors from pornography. Urging a re-evaluation of the Bill in light of its broader sociopolitical implications, the article cautions against rash policy decisions that may further stigmatize alternative forms of sexual expression, specifically queer, feminist, and fetish pornographies.

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)0.009
metaresearch head score (Gemma)0.024
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 score0.089
Threshold uncertainty score0.645

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.024
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0280.043
Scholarly communication0.0140.004
Open science0.0040.004
Research integrity0.0160.021
Insufficient payload (model declined to judge)0.0030.000

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 head0.105
GPT teacher head0.405
Teacher spread0.300 · 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

Citations4
Published2024
Admission routes2
Has abstractyes

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