Translation of Onomatopoeia Words in The Republic of Wine From the Perspective of Relevance Theory
Bibliographic record
Abstract
The novel The Republic of Wine is one of the masterpieces of Mo Yan with a large number of onomatopoeia words, which are used to make the novel more vivid and interesting. The novel was translated by Howard Goldblatt who conveyed the intention of the author in the original text to Western readers. The translation version is clear and logic which makes readers easy to understand. This paper analyzes the translation of the onomatopoeia words in Mo Yan’s novel The Republic of Wine in the light of Relevance Theory in order to make contribution to the translation of the onomatopoeia words. The method of omission will not change the intention of the author in original text and can make the translation text understandable for readers. Substitution can put correspond English onomatopoeia in the place of the original words in order to help the readers obtain sufficient contextual effects. Paraphrase is used to explain the exact meaning of the original onomatopoeia words.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".