Interpreting Chinese Culture-Loaded Sayings: A Case Study of Press Conference of Two Sessions
Bibliographic record
Abstract
As an indispensable means to elaborate on China’s development momentum and to set up global image, interpretation of press conference for Two Sessions presents the study essence of international publicity translation and the key problems lie in the culture-loaded expression translation. In light of the Functional Linguistics and Functionalist translation theory, the top-down hierarchic problem-solving approach is adopted for the original analysis. The target-oriented perspective aims at acquiring adequate version instead of equivalence between the source and target. The past decade example extractions cover the interpretation of the four-character set phrases, colloquial expressions, quotations and proverbs. Nine frequently-used interpretation types are concluded and analyzed in terms of the intended function and addressees’ reception environment by comparing the source and target cultures. Statistic results show that the compatibility between the two cultures may account for, to some extent, the method selection for interpreting practice in question. The more compatible of the relationship between the two cultures, the more tendency of preserving the original cultural image, and the more literal forms will be employed. And the larger the cultural distance, the more adjustments and adaptions would be adopted. With due consideration of the intentional function and addressees’ background, the versions may be readily accepted.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.023 | 0.008 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".