Translators’ Introduction : Eromanga in the Global Now
Bibliographic record
Abstract
Eromanga , Japan and Translation In the summer of 2014, a swarm of international journalists descended on Akihabara, a neighborhood in Tokyo known for its concentration of stores selling manga (comics), anime (cartoons) and related media and material. For over a decade, they and others had filed breathless reports about the global spread and influence of Japanese popular culture, especially manga and anime, which fueled hype about “Cool Japan.” In the process, Akihabara, where manga/anime stores are more densely clustered and visible than anywhere else in the world, had become a symbolic site for Cool Japan and tourist destination (Galbraith 2019). The journalists, however, were not in Akihabara to talk about Cool Japan. On the contrary, they came to report on manga and anime as something that Japan ought to be ashamed of. In Akihabara, which metonymically stood for Japan, they found examples of comics and cartoons featuring youthful-looking characters engaged in explicit sex, or what appeared to them to be “child pornography” (Adelstein and Kubo 2014; Ripley et al 2014; Fawcett 2014). The keyword was “ lolicon ,” or work associated with the “Lolita complex.” There was nothing new in responding to manga and anime this way, which reflects a relatively stable international discourse about “the Japanese Lolita complex” (Saitō 2011: 6) and Japan as a “dangerous (potentially pedophilic) ‘other’” (Hinton 2014: 65), but this coverage was notable for the intensity of its collective moral outrage. The journalists were reporting on the Japanese government's decision to revise child pornography laws, namely to ban possession (production and distribution were already illegal). This seemed to align Japan with international standards, but lawmakers notably did not include fictional forms in their definition of child pornography. If increased concern about the safety of children led to stances against pornography and abuse from the late 1970s on (Rubin 2011: 168, 218), then this had taken the form of an ongoing and open-ended campaign by the 2010s. In hopes of stamping out the scourge of child pornography and abuse, Canada, Australia and the United Kingdom have all moved to make illegal both real and fictional forms (McLelland 2016: 11).
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".