Bibliographic record
Abstract
Sailor Moon, a Japanese series grounded in manga and anime, began airing translations in the West throughout the 1990s. The series provided what could be interpreted as resistance to dichotomous conceptualizations of sexuality, sex and gender. The focus of this article is the set of challenges presented by the genderqueer characters in Sailor Moon and how Westernization and English translations have worked to erase and re-write queer identities. Arguably, Sailor Moon acts as a site to play out the contextualities and complexities of sexuality, sex and gender identities. To name Sailor Moon characters in Western specific terms would be at the expense of reducing the complexity of their identities to a categorical system whose boundaries detract and limit meaning. Queer characters in Sailor Moon are not translatable into dichotomous Western thought - categories fail us and, through their enforcement, the depth of meaning and the complexities of queer identities/desires are lost in translation. Working within Western binary systems, categories and language, many of these identities appear contradictory and incoherent. Sailor Moon characters offer a re-envisioning of identities that is not limited by Western binaric thought and cannot be easily pegged within the heterosexual matrix.
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 | 0.005 | 0.015 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".