Bibliographic record
Abstract
The paper describes the functions of description translation theories in translating the Chinese ancient literatures.Descriptive equivalence theories simplifies the difficult and complexed translation of the Chinese ancient literatures. Besides,the paper points out some difficulties while translating the literatures and analyses them. Key words: Chinese classics; Descriptive translation; Equivalence of translation; Translation of metalanguage Resume: Cet article decrit les fonctions de la theorie de traduction descriptive dans la traduction de la litterature classique chinoise. La theorie d’equivalence descriptive simplifie la tâche difficile et complexe de traduction de la litterature classique chinoise. En outre, l’article souligne certaines difficultes lors de la traduction de la litterature et les analyse. Mots-cles: Classiques chinois; Ttraduction descriptive; Traduction d’equivalence; Traduction de metalangage 摘 要：本文介紹了當今西方翻譯研究的一個重要學派---描寫學派描寫性翻譯理論以及Mona Baker (2008.1) 在《換言之：翻譯教材》中指出的翻譯對等論（該翻譯對等論完善了尤金·奈達(Eugene A.Nida)的“功能對等理論”理論的片面性和局限性），在漢語古典文學的英譯過程中現實意義。在漢語古典文學的翻譯過程中的對等翻譯、語義學的元語言的翻譯，語篇結構的對等等問題都可使用描寫性翻譯定位。同時指出描寫性翻譯並不是想完全推翻傳統的規範性的翻譯標準，而是對其不完善的地方並加以補充。另外歸納出若干古典文學作品英譯過程中的重難點，並稍加解釋和分析。 關鍵詞：古典漢語文學作品； 描寫性翻譯； 翻譯的對等； 元語言的翻譯
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".