Transplanted boys' love conventions and anti-"shota" polemics in a German manga: Fahr Sindram's "Losing Neverland"
Bibliographic record
Abstract
Although manga arrived somewhat later in Germany than elsewhere in the West, the local publishers rapidly capitalized on its appeal to female readers and began fostering local manga artists in Germany. These are mainly young women producing shōjo manga, and increasingly integrating popular boys' love elements into their work. An unusual example of such work is Fahr Sindram's Losing Neverland, the story of an adolescent in Victorian London whose widowed father prostitutes him to middle-class men. Suggestive, though not visually explicit, such a story would likely run afoul of German and European Union laws against child pornography, were it not for the fact that Sindram continually reminds the reader that Neverland is in fact intended to raise awareness of child abuse and protest the dissemination of Japanese child pornography in Germany. Sindram thus openly advertises her work as a polemic, intended to mobilize the censorship of works seemingly much like her own; as a result, Losing Neverland has not only been socially accepted but even praised, earning an honorable citation from Germany's federal Council for Sustainable Development. Sindram's work thus accepts and capitalizes upon the globalizing aesthetic influence of manga, while at the same time adopting a defensive, quasi-protectionist stance against the spread of certain overtly foreign sexual attitudes associated with manga—and is visibly rewarded for doing so.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.022 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".