On Wang Rongpei’s Drama Translation Strategy: A Case Study of The Peony Pavilion
Bibliographic record
Abstract
As a special literary form, drama is characterized by personalization, colloquialization, rhythm and the feature of performability. For a long time, the focus of drama translation studies has also fallen on the “performability” at home and abroad. The Peony Pavilion is one of the masterpieces in ancient China. With its beautiful and elegant lyrics, engaging anecdotes and vivid characters, The Peony Pavilion has an enduring popularity on the opera stage and a high literary value. There are many translations of The Peony Pavilion. Among all the versions, Wang Rongpei’s translation has balanced “spirit” and “meaning” and creatively reproduced the original style. Wang Rongpei first proposed the translation theory of “faithful in meaning and vivid in description” when he translatingThe Book of Songs in 1994. He thought that “faithful” refers to accurately express the meaning of the original text with target language, and “vivid” means showing the original style, emotion, rhythm, images, and characteristics. This article takes Wang Rongpei’s version of The Peony Pavilion as the study object and puts it under the criteria of “faithful in meaning and vivid in description”, analyzing the translation of the lyrics and researching how he transmits the spirit on the basis of being faithful in meaning. The aim of this article is to analyze Wang Rongpei’s dramatic translation techniques and explore the guiding and theoretical significance of this strategy for Chinese classical opera translation.
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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 | 0.009 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".