Lost (and Found) in Translation: Evaluating English translations of Mulan Shi
Bibliographic record
Abstract
The classical Chinese poem Mulan Shi has been translated many times into different languages and adapted to different media. Drawing upon Sonesson’s definition of translation as a double act of communication and combining it with concepts from narratology and translation theory, we develop a framework comprising three levels of translation resemblance: (a) between the structure and content of the source and target texts, (b) stylistic resemblance, and (c) pragmatic resemblance, concerning the impacts of the texts upon respective audiences. We compare five English translations of the poem, selecting the one by Arthur Waley from 1923 for detailed scrutiny. We triangulate between a first-person, second-person and third-person methods, leveraging our intuitive grasp of languages and cultures, and measures of different aspects in the source and target texts. To be able to assess pragmatic resemblance we conducted a survey with 20 participants from each cultural/linguistic group (Chinese and Western English speakers), asking for interpretations and evaluations of key aspects of the poem. The findings were that the target text exhibits high resemblance with respect to narrative structure but moderate resemblance on content due to some key omissions and substitutions. The stylistic resemblance was also moderate, while the pragmatic was considerable, with similar assessments in the two groups, though with different proportions.
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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 | 0.017 | 0.075 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".