Bibliographic record
Abstract
Introduction: Negotiating in Research and Teaching Mark McLelland Death Note, Student Crimes, and the Power of Universities in the Global Spread of Manga Alisa Freedman Scholar Girl Meets Manga Maniac, Media Specialist, and Cultural Gatekeeper Laura Miller Must We Burn Eromanga? On Trying Obscenity in the Courtroom and the Classroom Kirsten Cather Manga, Anime and Child Pornography Law in Canada Sharalyn Orbaugh Lolicon Guy: Some Observations on Researching Unpopular Topics in Japan Patrick W. Galbraith All Seizures Great and Small: Reading Contentious Images of Minors in Japan and Australia Adam Stapleton The that Dare Not Speak its Name: Chinese Danmei Communities in the 2014 Anti-Porn Campaign Lin Yang and Yanrui Xu Negotiating Religious and Fan Identities: Boys Love and Fujoshi Guilt Jessica Bauwens-Sugimoto Is there a Space for Cool Manga in Indonesia and the Philippines? Postcolonial Discourses on Transcultural Manga Kristine Michelle Santos and Febriani Sihombing Appendix: Rise and Fall of the King of Lolicon: An Interview with Uchiyama Aki Patrick Galbraith
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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 teacher head, 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".