A Translation Analysis of Nursery Rhymes in the English Version of The Straw House Based on Relevance Theory: a Cognitive Approach
Bibliographic record
Abstract
The Straw House (草房子) written by Cao Wenxuan (曹文轩) had encountered overseas coldness before the writer won the Hans Christian Andersen Award in 2016. Such a phenomenon has been attributed to three main reasons: copyright agents, translation challenges, promotion and review of social media. Taking the translation of five nursery rhymes as the research object which play a vital role in the English version of The Straw House , this study analyzed and explored the translation approaches of those nursery rhymes adopted by Sylvia Yu, Julian Chen and Christopher Malone. Through case analysis, a concrete analysis was made of the translation methods of the nursery rhymes, contrasted with the Chinese Mother Goose Rhymes by Isaac Taylor Headland, a book of similar genre. Based on Relevance Theory, a discussion of advantages and disadvantages of the translations was made before solutions were put forward to the translation problems which led to the failure to communicate the author’s intention to the target readers. It was found out that despite the attention to the reproduction of the original content by using literal strategy, the translation ignored the individual characteristics, contextual functions and the role in emotional expression that nursery rhymes play. In this process, the separation of the original information intention and communication intention, the loss of contextual presupposition and implication all resulted in the absence of relevancy which fails the communication. Based on the identified translation problems, some solutions were proposed. It is hoped that these solutions will bring inspirations to the translation of nursery rhymes in children’s literature.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".