Critical Thinking of Applying Nida’s Functional Equivalence to Poetry Translation: A Study Based on English Versions of Lu Zhai by Wang Wei
Bibliographic record
Abstract
Based on the notion that poetry is translatable, this paper will analyze the pros and cons of applying Eugene A. Nida’s functional equivalence theory to the translation of Lu Zhai , written by a famous Chinese poet Wang Wei in the Tang Dynasty (618-907 CE). Because poetry translation requires translators to transmit both the meaning and beauty of the original poem to the target readers to achieve the similar response. This requirement is consistent with Nida’s functional equivalence. But there are still limitations. The paper is composed of an introduction, the main body and the conclusion. Chapter One is the introduction of the research goal and significance, and a general introduction of Nida’s functional equivalence theory, and the Chinese poet Wang Wei as well as his poem Lu Zhai . Chapter Two shows a detailed analysis of the pros and cons of applying Nida’s functional equivalence theory to the English versions of Lu Zhai . Chapter Three is the conclusion of this paper, which summarizes the relation between translation theories and practice.
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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 | 0.014 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.033 |
| Scholarly communication | 0.005 | 0.012 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".