Cultural Translation and “Cooking”: on the Translator’s Role in Cultural Translation
Bibliographic record
Abstract
By comparing the similarities between cultural translation and cooking, especially cooking Sichuan cuisine, the present paper tempts to propose that, in cultural translation, the role of the translator, like that of the cook, is to express faithfully the cultural information in the source-text to the readership of the target-text. Key Words: Cultural Translation, Cooking, Role of the Translator, Faithfulness Resume L’auteur de cet article avance, par l’analyse et le contraste de la similarite entre la traduction culturelle et la ‘‘cuisine’’, surtout la ‘‘cuisine des plats au gout de Sichuan’’, l’deee que le traducteur dont le role ressemble a celui du cuisinier doit, dans la traduction culturelle, transmettre fidelement les informations culturelles du texte original aux lecteurs de langue cible. Mots-cles: la traduction culturelle, la cuisine, le role du traducteur, la fidelite 摘 要 本文試圖通過分析對比文化翻譯與“烹飪”，尤其是“烹飪川菜”的相似性，提出文化翻譯中譯者的角色如同廚師，應忠實地將原文中的文化信息“原汁原味”地傳達給譯語讀者。 關鍵詞：文化翻譯；烹飪；譯者的角色；忠實性
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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 | 0.015 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.031 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".