Chinese Translation of English Human Body Idioms Based on the Functional Equivalence Theory
Bibliographic record
Abstract
There have existed a large number of idioms related to human organs in both English and Chinese, which are set phrases or sentences abstracted from language. They form an essential part of the whole vocabulary of language and have been used for a long time, which reflect the national colors of the native speakers and are said to be considered as the sinew of the language possessing various cultural characteristics. It is because those idioms bear certain features of ethnic cultures that the human body idioms translation becomes a real problem for translators. So it’s necessary for translators to do their utmost to pursuit the most proper way of translation. Only under proper translation strategies can the human body idioms be translated with the national features and convey the meaning of the original .This thesis is about Chinese translation of English human body idioms based on Nida’s equivalence theory. After the general understanding and introduction to the source and characteristics of English idioms and the general comparison between Chinese idioms and English idioms associated with human body, much more practical methods of the idioms translation, such as rhetorical devices, are discussed in this thesis.
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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 | 0.002 |
| 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 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".