On the Translational Poetics for Museum Commentary: A Case Study of the Archaeological Excavations at the Royal Cemetery of Haihunhou Kingdom in the Han Dynasty
Bibliographic record
Abstract
Andre Lefevere believes that translators must adapt to the requirements of the times in the translation, so the language of the translation is inevitably manipulated by the dominant poetics. As a branch of applied text translation, museum commentary should not only convey the information of the cultural relics, but also introduce and disseminate Chinese history and culture to foreign tourists, whose language requires accuracy and vitality. Therefore, the linguistic level of museum commentary translation is bound to be manipulated by translation poetics. In the light of Levefere’s poetics of translation, the paper attempts to analyze the translation of Haihunhou museum commentary from three linguistic levels and finds out that the translated version’s poetics have actually changed, including lexicon, syntax and rhetoric, to restore the characteristic language form of the original text. And it concludes domestication, literal translation and the translation methods frequently applied, with the desire for providing an innovative theoretical direction for the study of translation of museum commentary.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.027 | 0.020 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".