Revisiting Jia Pingwa’s Ruined City: A Corpus-Based Study on the English Translation of Folk Languages
Bibliographic record
Abstract
As cultural contacts increase, literary works have emerged as a vital medium for disseminating Chinese culture. The folk languages present in these literary pieces pose significant challenges in conveying the original meanings due to the intricacies of translating such languages. Therefore, this paper examines the translation of folk language in Jia Pingwa’s Ruined City, utilizing a self-constructed bilingual parallel corpus and UAMCT software to annotate and analyze the translation methods and strategies used for the folk language elements. It was found that the text contains 1,033 instances of folk language, with domestication emerging as the predominant translation strategy. Domestication was primarily achieved through free translation, while various translation methods were employed to strike a balance between readability and the preservation of cultural distinctiveness. This paper illustrates the application, preference context, and effect of free translation, literal translation, omission, idiom translation, and mixed translation methods in specific sentences, aiming to build a strategic framework for the folk language translation of Ruined City and provide references for cross-language communication of similar folk literature works.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".