On the Translation of Manchu’s Entertainment Way: A Case Study of Hawkes’ Translation of Hongloumeng
Bibliographic record
Abstract
Translation is not only a transference between two languages but also a communication between two cultures. In literary translation, a translator is expected to take the language features, the artistic style and cultural information of the source text into consideration. In this sense, translation serves as a bridge to introduce Chinese literature to western readers. However, the translation of Chinese classical literature is still at an outset stage. As one of the four great masterpiece of Classical Chinese literature, Hongloumeng has been translated by transaltors both at home and abroad. The most widely recognized English translation of Hongloumeng are Yang Xianyi&Gladys Yang’s version and David Hawkes’ version. As for the influence and acceptance of the two versions, Hawkes’ version is more popular among the western readers for its natural, smooth and idomatic language. However, after a careful comparision with the source text, we find that Hawkes’ version failed to impass part of the China-specific cultural information to the target reader. In this paper, the author will take Hawkes’ translation of Manchu’s entertainment way depicted in Hongloumeng as an example to demonstrate the loss of cultural information in Hawkes’ version. In terms of being responsible for the target reader and faithful to the source text writer, we strongly holds that cultural information should be reproduced in the most faithful way as much as possible.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.007 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".