On the Intertextuality of English-to-Chinese Literary Translation: A Case Study on the Chinese Translation of Overworld in Flames
Bibliographic record
Abstract
This study was composed based on the author’s published translation practice, Overworld in Flames, which, written by the best-selling author with New York Times, Winter Morgan, portrays the course of how Gameknight999 and his companions avert the blazing inferno devouring the whole Overworld against unidentified attackers. Since intertextual references were found ubiquitous in the book, in consonance with the classification by N. Fairclough (1992), the author categorizes its intertextual references into manifest intertextuality and interdiscursivity, and discusses its Chinese translation. This paper demonstrates that intertextuality provides a systematic approach for identifying and handling complex intertextual relationships in literary works, guiding translators to make dynamic choices based on both theory and their own intentions, in the hope to provide some implications for translators to understand intertextual representations in literary translation and to produce better works in the future.
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 | 0.011 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.024 | 0.016 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 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".