Digitalized Translation of Chinese Online Literature: Practice and Research
Bibliographic record
Abstract
This study thoroughly explores the digitalized translation and overseas dissemination of Chinese online literature from the perspectives of industry practice and academic research. It points out that Chinese online literature has grown from a sub-cultural phenomenon in China to an essential component of contemporary Chinese literature, with digitalized translation being the primary mode for its overseas dissemination. The study defines the concepts of online literature and digitalization, discusses the diversified digitalized translation platforms of Chinese online literature,and analyzes their respective advantages and challenges. Thereafter, it scrutinizes the research on the translation of Chinese online literature, highlighting the richness of research perspectives and the diversity of research content, noting that current researches on the overseas dissemination of Chinese online literature is relatively scarce but holds significant academic and practical value. Finally, it looks forward to the future directions of research on the translation of Chinese online literature, including further exploration on translation content, translation subjects, translation modes, and translation criticism, and emphasizes the importance of theoretical integration and innovation.
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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 | 0.024 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.012 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".