The Accuracy and Defamiliarization Translation Strategy of Pearl S. Buck on All Men Are Brothers
Bibliographic record
Abstract
The living standard of the people in the Song Dynasty was high and the culture of drinking, tavern and drinking customs were popular at that time. Chinese classic literature Shui Hu Zhuan has a large number of wine cultures writing in the background of Song Dynasty; it proved the prosperous culture of Song Dynasty in details，and this masterpiece was repeatedly translated into English world. Pearl S. Buck’s All Men Are Brothers received bad fame at the very beginning and since 2003 the version had been fairly evaluated. The author took Pearl S. Buck’s All Men Are Brothers and Jewish American scholar Sidney Shapiro’s Out Laws of the Marsh for example, studied the translation of tavern description, wine drinking customs and the name translation of wine from the perspective of culture, semantic and aesthetic，the author made a conclusion that the translator Pearl S. Buck is more familiar with the Chinese culture and during her translation, she reserved more accurate cultural terms and delivered a more exotic version of Chinese classic.
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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 | 0.012 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.007 |
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".