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
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.011 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".