Three-Dimensional Transformation in the English Translation of China’s Foreign Ministry Spokespersons’ Conflictive Responses
Bibliographic record
Abstract
This study investigates the translation of conflictive responses in Chinese diplomatic discourse into English. Utilizing the three-dimensional transformation framework of eco-translatology, encompassing linguistic, cultural, and communicative dimensions, the research identifies strategies and challenges in achieving semantic equivalence, cultural appropriateness, and communicative effectiveness. The study employs a qualitative approach, combining descriptive translation studies and content analysis. The corpus is sourced from the official website of China’s Foreign Ministry’s press conferences in 2020 delivered by the spokesperson Zhao Lijian. Findings reveal that idiomatic expressions and neologisms with negative connotations characterize conflictive responses and present significant translation challenges. The three-dimensional transformations are not always fully realized, particularly in neologisms lacking direct equivalents in the target language. Adaptation and description are frequently adopted, and the literal translation is not often applicable when dealing with culturally-rendered expressions. This study underscores the importance of a translator-centered approach that highlights the critical role of translation in shaping diplomatic discourse.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| 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".