Translation and Plurality of Interpretations: A Brief Discussion on Practical Significance in Translation of the
Bibliographic record
Abstract
Based on one of the theories of the modern philosophical hermeneutics---“ fusion of horizon”, the author of this paper is to put forward new perspectives to reconsider the criticism toward the two English versions of Chinese classical work---A Story of Red Mansions. The paper also elaborates the theoretical significance of “ fusion of horizon” in translation practice: It is an unavoidable fact that any target version has translator’s style, so we must have an objective attitude toward the cultural filtering and misunderstanding; translation and plurality of interpretations is the need of history; in order to produce ideal target version, translators should make efforts to enrich and perfect their horizon. Key words: hermeneutics, fusion of horizon, preconceived idea, translation and plurality of interpretations Resume: Ce texte met en question les critiques sur les deux versions traduites du Reve du pavillon rouge, et expose la signification de la theorie sur la traduction . Les textes traduits sont marques plus ou moins de style personnel du traducteur , il faut donc traiter des problemes de fausses traductions de facon objective . En meme temps , le pluralisme de traduction et d’explication est devenu necessaire en raison de l’ouverture du texte . Les traducteurs doivent s’y adapter pour ameliorer la qualite de traduction Mots-cles: explicationisme , fusion horizontale , idee preconcu , le pluralisme d'explication 摘要：本文結合現代闡釋學“視域融合”的理論，對≤紅樓夢≥兩個不同譯本的有關批評提出異議，進一步闡述了闡釋學“視域融合”理論對翻譯的借鑒意義：即“先行結構”的影響使譯文不可避免地帶有譯者風格，因而應客觀看待“文化過濾”和誤譯現象。與此同時，文本的開放性使翻譯與闡釋的多元成為必要，作為譯者也應不斷完善自己的先行結構，以提高譯文的品質。 關鍵詞：闡釋學；視域融合；先行結構；闡釋的多元
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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 | 0.018 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.046 |
| Scholarly communication | 0.013 | 0.018 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 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".