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Record W3213767749 · doi:10.4324/9781315637884-13

Manga, anime, and child pornography law in Canada

2016· book-chapter· en· W3213767749 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldPsychology
TopicSexuality, Behavior, and Technology
Canadian institutionsnot available
Fundersnot available
KeywordsChild pornographyAnimePornographyCriminologyLawPolitical sciencePsychologySociologyThe InternetPhilosophyWorld Wide WebComputer scienceLinguistics

Abstract

fetched live from OpenAlex

This chapter represents an attempt to think through the implications of the current Canadian law addressing child pornography – how the law defines it, and under what circumstances it is prosecuted – for Canadian consumers of Japanese popular culture products such as manga and anime. However, it is also a very personal attempt by a 58-year-old woman who experienced sexual abuse as a child to think through the reasons why she opposes (that is, I oppose) the censorship of sexual materials, including those that depict people under the age of 18, so long as no actual child was harmed in the making of them. The process of preparing this topic for a paper presented at the Manga Futures conference at the University of Wollongong in 2014 was – to my surprise – excruciating. It forced me to scrutinize my most fundamental values for consistency and ethical rigor. It forced me to recognize and articulate processes of exploration and recovery that I have instinctively (that is, without conscious thought or planning) pursued throughout the course of my life. In this chapter I retain the personal quality of that 2014 presentation: it is about me and why I hold the opinions I do. In future work I hope to incorporate more academic and empirical evidence to support those opinions, but this essay is about my experiences and the ways they are not reflected in or supported by Canadian law.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.957
Threshold uncertainty score1.000

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0060.000

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 head0.020
GPT teacher head0.266
Teacher spread0.246 · 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.

Study designTheoretical or conceptual
Domainnot available
GenreOther

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
Published2016
Admission routes1
Has abstractyes

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