Visible translators in Chinese heritage museums: toward a Sinocentric interpretive translation
Bibliographic record
Abstract
This study addresses the understudied aspect of the museum translator’s visibility, as translators consciously inject their voices, styles, and cultural references into their translated works. By investigating the translation and interpretation of history in bi/multilingual heritage museums, this paper contrasts ‘visible’ and ‘invisible’ translators according to Venuti’s (2018) framework, highlighting translation as a creative and interpretive process. This study draws on empirical data from semi-structured interviews with stakeholders/translators in national heritage museums in Xi’an, China, including the Museum of Terracotta Warriors and Horses, and analyses of cultural policies and training materials; accordingly, it demonstrates the emergence of translators’ visibility in Chinese heritage museums and outlines the factors contributing to this visibility. The findings reveal that museums adopt a Sinocentric translation approach characterized by interpretive engagement, encouraging audiences to engage with cultural differences. This approach fosters encounters with an exotic, expressive otherness, facilitating intercultural dialogues. This study’s significance lies in its recognition of translation as an event that challenges static literary norms and enables diverse voices to contribute to intercultural communication. Translators’ visibility is essential in shaping the communicative landscape.
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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 | 0.016 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.013 | 0.028 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.010 |
| 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".