Plurilingualism and Native-Speaker Norms in Chinese Translation Studies — Ideological Tensions and Prospects for Reconciliation
Bibliographic record
Abstract
Over the past few decades, among language and translation scholars worldwide, this plurilingual perspective on individuals and communities on-the-move and ever-dynamic uses of several languages has increasingly gained consolidation. Native-speaker norms that sanction end-speaker authenticness as an inspirational model have dominated translation quality norms, appraisal schema, and teaching frameworks. Their binarism bred chronic ideological tension embedded within China, where translation studies and practices are written within domestic scholarly hegemonics and an increasingly internationalizing language market. This paper investigates where native speaker norms intersect and conflict with plurilingualism in Chinese translation studies under mainstream domestic indexes, Chinese Social Sciences Citation Index (CSSCI), A Guide to the Core Journals of China (GCJC) and foreign journals, Social Sciences Citation Index (SSCI), Arts and Humanities Citation Index (AHCI). According to qualitative clustering of journal papers from 2021 to 2025, the paper identifies three conflict zones: native norm-biased translation quality appraisal conflicts of translator identity and plurilingual competences and native knowledge, and cultural exchange paradox, where native norms promote and restrict exchange between cultures. To counter such tensions, this paper recommends policies, pedagogical, and professional practices that are more inclusive and language empowering. By situating such solutions in the framework of China's cultural, historical, and market-based translation, this article adds to the growing debate on how the role of translation in the multilingual world should change.
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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 | 0.044 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.009 | 0.036 |
| Scholarly communication | 0.015 | 0.011 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".