Survey of Explicitation Strategies in Chinese-English Translation of Political Texts of Contemporary Chinese Government
Bibliographic record
Abstract
Nowadays as China plays a more and more important role in the world, the exact conveying of “big country image” to the world is urgently put into agenda. In the process of political communication, political texts translation is of vital importance. It not only matters the interpretation of a country’s management concepts, foreign policies and political opinions, but also concerns with a country’s overall capacity and international status. Excellent political translation can contribute to forming a good country image internationally and can minimize the misunderstandings among countries. By proper political translation, countries can solve disputes through friendly negotiation and increase cultural exchanges. And China will embrace the “Chinese dream” and realize the goal of developing into a harmonious society. Nevertheless, many translation problems still exist in the political translation because of the differences in language use and culture, religion and geography. Many political translation texts only focus on the word-for-word equivalence, which brings many misunderstandings in the international communication. Explicitation strategies are urgently needed in political translation in order to clearly convey the meaning to readers and minimize the disputes occurred in translation. In this paper, the researcher analyzes the Report on the Work of the Government Delivered at the Second Session of the Twelfth National Peoples Congress on March 5, 2014 and some of Chinese leaders’ important speech texts carefully. Through detailed examination of their English version, the researcher aims to find out the frequency, use and acceptance degree of explicitation strategies. On the basis of systematic knowledge of explicitation strategies, an agreement will be expected to be reached between explicitation strategies and political translation. Our final goal is to translate Chinese political texts in authentic English and change the previous obscure translation. In this paper, the researcher selects some texts as cases to study, and finally grasps the overall rules of explicitation strategies application. These rules of explicitation strategies will be put into future translation practices.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".