A Study on the English Translation of the Government White Paper China’s Energy Transition: A Linguistic Adaptation Perspective
Bibliographic record
Abstract
An energy white paper is seen as an important type of ecological discourse on energy. The English translation of Chinese government energy white papers is crucial to showcase China’s energy polices to the global community. Guided by Verschueren’s Linguistic Adaptation Theory, this study establishes an analytical framework focusing on both structural adaptation at lexical, syntactic and discourse levels and communicative adaptation to the mental, social and physical worlds of the target readers. It also explores the translator’s linguistic choices (translation strategies) in the white paper China’s Energy Transition and their motivations behind. The findings reveal that the translator has used free translation for culturally bound terms, reordering to align with English preference for directness, and the translation technique of addition to bridge the knowledge gaps for an international audience. It concludes that the translation process is a dynamic process in which the translator makes adaptive choices at the contextual and linguistic structure levels to achieve ideological fidelity and effective cross-cultural communication.
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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 | 0.012 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| 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".