Bibliographic record
Abstract
Translating English attributive clauses into Chinese is one of the most knotty tasks for translators. This paper compares the syntactic differences between English and Chinese, and explores the possible methods of translating English attributive clauses into Chinese. It is suggested that the translator should bear in mind the syntactic differences, correctly grasp the original meaning and conform to the Chinese expressing rules in translation. Key words: English attributive clause, syntactic difference, translation Resume: la traduction de la proposition attributive anglaise en chinois est une des tâches les plus epineuses pour les traducteurs. Cet article compare les differences syntaxiques entre l’anglais et le chinois, et explore les methodes possibles de la traduction de la proposition attributive anglaise en chinois. Il est suggere que les traducteurs doivent porter en tete les differences syntaxiques, correctement saisir le sens original et conforme aux regles de l’expression chinoise dans la traduction. Mots-Cles: proposition attributive anglaise, difference syntaxique, traduction
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".