Contrast Study of English and Chinese Idioms in the Background of Cross- culture
Bibliographic record
Abstract
This essay combines the theory and translation experience. It analyses the differences and similarities of English and Chinese idioms with the purpose of helping people to grasp the complex linguistic phenomenon and to instruct the translation practice. Key words: English idioms,Chinese idioms, contrast,differences and similarities Resume: En recourant aux connaissances theoriques et aux experiences de traduction requises par l’auteur, ce texte effectue une analyse et recherche approfondies sur les semblances et les differences entre les locutions chinoises et anglaises dans l’objectif d’apporter de l’aide aux chercheurs linguistiques pour qu’ils puissent maitriser ce phenomene complexe de la langue anglaise et les mettre en pratique dans la traduction sino-anglaise et anglo-chinoise a titre de reference. Mots-cles: locutions anglaises, locutions chinoises, comparaison, semblances et differences 摘要：本文結合筆者的所掌握的理論知識和翻譯經驗，對英漢成語的異同現象進行了較為深入的分析和探討，旨在幫助語言工作者掌握英語這一複雜的語言現象, 以指導英漢互譯實踐。 關鍵詞：英語成語 ; 漢語成語; 對比; 異同
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".