A Systematic Literature Review of Chinese-English Euphemism Translation
Bibliographic record
Abstract
To comprehend current research status of Chinese-English (henceforth C-E) euphemism translation, this research conducted a systematic literature review on 147 publications from 1997 to 2025 based on prisma, from aspects of research trend, subjects, applied theories, problems identified, and translation strategies and principles. It shows euphemism translation from Chinese to English has started in 1997, then developed to its climax in 2012 and began to decline until now. Besides, there is a transition in theories application from Skopos and functional equivalence to cultural theory. Furthermore, homogeneity is found in translation problems, strategies, and principles. All of these limitations suggest more diversified perspectives in C-E euphemism translation. Therefore, cross-disciplinary perspectives are welcomed in relevant studies, such as culture, ideology, readers’ cognitive and psychology, and readers’ reception. This research makes a summary of current research status quo, finding some limitations in this regard such as lacking of cross-disciplinary perspectives and homogeneity in translation problems and strategies, which will enrich relevant research diversity and offer more guidance to address relevant problems and fill up the research gaps.
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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 | 0.022 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.023 | 0.020 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| 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".