Manga, anime, and child pornography law in Canada
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.016 | 0.010 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".