A Comparative Study on the English Translation of the Personalized Language of the Character Huniu (虎妞) in Luotuo Xiangzi
Bibliographic record
Abstract
This study aims to compare and discuss the Chinese-English translations of Huniu’s (虎妞) personalized language in four different English translations of the Chinese novel Luotuo Xiangzi, supported by Nida’s theory of “functional equivalence” and with a demonstration of the features of Huniu’s personalized language that lead to difficulties in translation as a framework for the analysis. The analysis reveals that, when translating Huniu’s personalized language, the translators adopted various translation methods, including euphemism, literal translation, deletion, and free translation. The findings indicate that the use of euphemisms as a translation strategy does not support maintaining the character’s language features and style when translating the swear words used by the character. Proactive changes in the tone of the speech of the character in translation impacts the reproduction of that character’s personality and image. In addition, Huniu’s language style of the Beijing dialect is difficult to maintain in translation. These findings serve as a reference for the Chinese-English translation of a character’s personalized language in novels to facilitate the dissemination of Chinese literature around the world.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.002 |
| 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".