A Study of Culture-Specific Items in Rickshaw: The Novel Lo-to Hsiang Tzu, from the Perspective of Aixelá’s Theory
Bibliographic record
Abstract
Culture-specific items, serving as a bridge between two cultures, have long been a focal point in translation studies. In 1996, Javier Franco Aixelá proposed a widely recognized definition and classification system for culture-specific items, along with corresponding translation strategies, significantly advancing research in this field. Luotuo Xiangzi, written by Lao She (1899–1966), is one of the most iconic works of modern Chinese literature. Since its publication, Luotuo Xiangzi has garnered international attention and has been translated into four English versions. Among these, Jean M. James’s translation stands out for its recognition and inclusion in prominent American university libraries. Despite this, research on James’s translation remains limited, particularly regarding her handling of culture-specific items. To address this gap, the present study, guided by Aixelá’s theoretical framework, conducts a comprehensive analysis of James’s culture-specific items translation strategies and the challenges she faced in translating culturally bound expressions from Chinese into English. Utilizing a qualitative methodology, the study identifies key factors influencing James’s translation decisions and explains major challenges in dealing with Chinese culture-specific items. The findings reveal James’s preference for conservative-oriented strategies over substitution-oriented strategies, reflecting her tendency to preserve cultural nuances rather than replace them with target culture equivalents. This study provides new insights into the practical application of Aixelá’s theory in Chinese-English literary translation and highlights the complexities of cross-cultural translation practices.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.016 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.005 |
| 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".