Analysis on Howard Goldblatt’s Translation of Rice From the Perspective of Translator’ Subjectivity
Bibliographic record
Abstract
With the emergence of culture turn in the 1980s, translator’s invisible status has been changed. Translation process is no longer considered as a simple conversion process from the original language to the target language, but a process full of creativity. Rice is the Chinese novelist Su Tong’s second novel which deals with China in the 1930s. He vividly portrays Depression-era China and the characters that populate this novel. Howard Goldblatt devotes himself to the translation of modern and contemporary Chinese novels into English and Rice is one of his numerous works. His impeccable translation does much justice to the flow of the tale. From the study on Goldblatt’s case, inspirations can be drawn on the exercise of translator’s subjectivity in the process of introducing and translating Chinese literature to the world. Research methods such as exemplification and induction were adopted in this article. Different interpretations of translator’s subjectivity were reviewed at the beginning of the article according to Professor Lu Jun’s division of three paradigms in Chinese translation study. The influential factors on translator’s subjectivity were analyzed on the theoretical basis of manipulation school and functionalist school. The manifestation of Howard Goldblatt’s subjectivity in the translation of Rice was analyzed, followed by reflections on the exercise of translator’s subjectivity.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.010 | 0.025 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".