Bibliographic record
Abstract
While fan communities and their complexities have already been abstractly explored in scholarly literature, Asian web novel translation groups present an interesting space for analysis as a niche imported cultural product that was made widely accessible to English speakers by multilingual fans as part of a gift economy, rather than through market expansion by the novels’ intellectual property owners. Exploring the history of online web novel translation websites like Chrysanthemum Garden, Wuxiaworld, and WebNovel, this article uses a combination of the author’s first-hand experience as an editor of these translated works, as well as academic parallels in digital culture literature, to explore the transition of fan communities that value transformational work without capital to an emerging market demographic, ripe for labour and consumer exploitation.
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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.016 | 0.006 |
| Scholarly communication | 0.014 | 0.018 |
| Open science | 0.002 | 0.027 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.088 | 0.021 |
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".