Assessing Narratives in the Translation of Chinese Political Discourse: A Perspective from the Narrative Paradigm
Bibliographic record
Abstract
In the global era, translating Chinese political discourse is key to enhancing international understanding and diplomacy. This research employs the narrative paradigm to assess the translation of Chinese political discourse, with the goal of enhancing the effective narrative comprehension of such translations for international communication. It applies Fisher’s narrative paradigm and Baker’s assessing narratives to investigate the application and impact of the principles of narrative coherence and fidelity in the translation of Chinese political discourse for international audiences. Employing a qualitative research approach, it conducts an analysis based on translations from volumes III and IV of Xi Jinping: The Governance of China, as primary case studies. The research finds that translations of Chinese political discourse face specific challenges and opportunities in maintaining narrative coherence and fidelity. The principle of narrative coherence emphasizes the importance of creating coherent, accessible narrative structures in translation, while the principle of fidelity demands fidelity to the source’s cultural information and political connotations, reflecting distinctive Chinese characteristics. Highlighting the significance of adopting the narrative paradigm in the international communication of Chinese political discourse, this research provides theoretical and practical insights for assessing narrative comprehension of Chinese political discourse translation. Moreover, the results offer a new perspective on narrative assessment for comprehension in cross-cultural and international communication, pointing directions for future research.
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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 | 0.023 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.006 | 0.017 |
| Scholarly communication | 0.010 | 0.015 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".