On Inter-Subjectivity in Translation: The Chinese Poetry Translator Xu Yuanchong as a Case
Bibliographic record
Abstract
Inter-subjectivity in translation studies provides a new perspective for studies of translators’ subjectivity. The author as the creating subject of the source text, the translator as the translating subject, and the reader as the reception subject, is all involved in the whole process of translation activity. The three subjects should interact with each other and give their inter-subjectivity a full play. A case study of the Chinese scholar Xu Yuanchong’s translation of classic Chinese poetry shows that the translator should play the role of a good learner who has to obtain a thorough understanding of the author and the source culture, and also play the role of a qualified teacher who imparts both form and content of the source text to target readers in an easier and more effective way. In a word, inter-subjectivity in translation studies requires cross-cultural understanding and cooperation among the author, the translator and the target reader beyond time and space.
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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 | 0.012 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.023 | 0.024 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".