A Study on the Translation of Culture-Specific Items in Character Depiction in the English Version of Pu Songling’s Liaozhai Zhiyi
Bibliographic record
Abstract
The characterization through literary translation is crucial to the international dissemination and reception of literary works. Nevertheless, the research on the translation of character depiction in Liaozhai Zhiyi greatly falls behind other translation themes of Liaozhai Zhiyi. This study is intended to explore the translation of culture-specific items (CSIs) in character depiction in Liaozhai Zhiyi from Chinese to English, within the framework of character’s three dimensions: physiological, sociological and psychological. This study reveals the shifts in the characterization by investigating translator’s strategies, and the significant integration of CSIs and character depiction. The findings demonstrates that the translator’s choice of strategies influences the rendering of character depiction. It is found that the character depiction in the original text of Liaozhai Zhiyi is more mysterious, whereas in the translated text, it seems to possess a stronger sense of intimacy. This study suggests that translators thoroughly consider the culture-specific elements in character depiction, and take into account the combination of multi-strategies. It is hoped that this study contributes to the research field of character depiction translation in Liaozhai Zhiyi, and sheds light on the translation of ancient Chinese classics.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".