Difference between Cohesions of English and Chinese Context and Their Translation
Bibliographic record
Abstract
Cohesion is a kind of prerequisite for generating context. In context translation, the context principle explains how to realize the context equivalence between the original article and the translated article. Cohesion often determines whether people can understand and accept the translation. During translation and context cohesion, the understanding of the original article and the generation and check of the translation play an important part in the perception and mastery of the context cohesion. In this paper, the difference between the five context cohesions (that is, contrast, substitution, omission, connection and lexicon) is made to verify that English articles stress hypotaxis while Chinese articles stress parataxis. Key words: context, cohesion, difference, translation Resume: La cohesion est une condition necessaire (mais non complete) de la production du texte. Dans la traduction, le principe de la textualite peut etre applique pour expliquer comment obtenir la correspondance, sur le plan du texte, entre l’original et la traduction. Le degre de cohesion determine si la traduction peut etre comprise par les lecteurs. Tout au long du processus de la traduction, de la comprehension de l’original a la production du texte traduit, jusqu’a la revision de celui-ci, la cohesion du texte joue un role essentiel. A travers la comparaison et l’analyse des differences des cinq moyens de cohesion dans la traduction anglo-chinoise, a savoir, coherence, substitution, ellipse, raccordement et cohesion lexicale, l’article present verifie le fait que l’anglais privilegie la forme tandis que le chinois le sens. Mots-cles: texte, moyen de cohesion, difference, traduction 摘要：銜接是產生語篇的必要（盡管不足）條件。在語篇翻譯中，語篇特征原則可用來解釋如何獲得原文和譯文在語篇層面上的對等，銜接的優劣關係到譯文是否被接受者理解和接受。翻譯過程與語篇銜接的整個過程中，從對原文的理解到譯文的生成再到譯文的校核，對語篇銜接的認識和把握都起著舉足輕重的作用。本文通過對比分析英漢互譯過程中語篇在五種銜接手段，即：照應、替代、省略、連接和詞匯銜接上的差異，從而驗證了英語文化重形合而漢語文化傳統重領悟、重意合的特征。 關鍵詞：語篇；銜接手段；差異；翻譯
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".