Appreciation of the Translated Version of Father Sews on a Button by Lv Shuxiang
Bibliographic record
Abstract
In Lv Shuxiang’s opinion, translation is not easier than writing; it is necessary to make sure of sound, lasting appeal and tone which are the basic conceptions for some knowledge of Chinese characters. This paper compares Lv’s Chinese translation Father Sews on a Button with the source text written by Clarence Day in the language features respectively and the main translation skills. The author holds that Lv’s Father Sews on a Button is a beautiful translation with the lasting appeal of the language effect and straight-forward and short-tempered image of father retained; he is indeed a master of language in China. Key words: translation of short stories, language features, translation skills, conversion Resume: Selon M. Lu Shuxiang, la traduction n’est point plus facile que la creation. Il faut connaitre bien la consonne initiale, la voyelle et l’accent qui constiuent les notions fondamentales du chinois. Suivant son point de vue sur la traduction, l’article present procede a une comparaison minutieuse entre sa version chinoise de Father Sews on a Button et l’original de Clarence Day sous les angles de la caracteristique langagiere et de la technique de traduction principale. L’auteur trouve que M. Lu est bel et bien le maitre de langage de notre pays et que sa traduction est une belle oeuvre ou l’image d’un pere droit et franc dans l’original est parfaitement representee et le style humoureux et vivant du langage est maintenu. Mots-cles: traduction de la nouvelle, caracteristique langagiere, technique de traduction, transformation 摘要：呂叔湘先生認為，翻譯並不比創作容易；“聲”、“韻”、“調”是瞭解漢語字音的基本概念，必須弄清楚。本文根據他的翻譯觀點，將他的中譯文《父親釘鈕子》與 Clarence Day的 Father Sews on a Button從各自的語言特色及使用的主要翻譯技巧作了一個詳細比較。筆者認為，呂先生不愧是我國的語言大師，他翻譯的《父親釘鈕子》確實是一篇再現了原文中率真直爽的性情父親形象，也保留了原文幽默活潑的語言風格的美文。 關鍵詞：短篇小說翻譯；語言特色；翻譯技巧；轉換
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.002 |
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".