The Criminalisation of Fantasy Material: Law and Sexually Explicit Representations of Fictional Children
Bibliographic record
Abstract
This book addresses the criminalisation of sexually explicit material depicting or describing fictitious characters who appear to be children. It is the first book of its kind to specifically examine the expansion of the law to include fictional representations of children, focusing on the law in Australia, Canada, the United Kingdom, and the United States. Based on a detailed socio-legal study, this book extensively analyses literature and pertinent theories of criminalisation, such as the Harm Principle, Offense Principle, and Legal Moralism. The book will be an invaluable resource for academics and students in various disciplines, including law, criminology, sociology, and psychology. It will also be of interest to fans of fantasy fiction. The author explores the potential criminalisation of comics and subgenres of manga that frequently depict childlike characters in a sexual context. Of course, the need to protect children from harm outweighs freedom of expression and the right to privacy; however, this argument is complicated by the material being purely fictional. Does prohibiting the fictional representation of minors interfere with individual freedoms?
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".