Intercultural exchanges in the translation of poetry East and West: the private and the public, the scholar and the artist
Bibliographic record
Abstract
This article on East and West focuses on translation and intercultural exchange in relation between public and private, scholar and artist. After an introduction that discusses poetry and translation, the essay illustrates in three parts on the poetry and translation of Jüri Talvet, especially his poem on Christmas in China (including H. L. Hix’s observation); the overlapping translations of French women poets into English by Norman L. Shapiro and George Edward Hart; the ideas of translation from the translators of my poetry, Siho Ho (Chengru He) and Hua Zhao. Translation and intercultural exchanges involve loss and gain and a creativity that creates new poems on their own merit and not simply as they are connected to the originals. These networks of translation and intercultural exchange are not binary but multiple.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.012 | 0.037 |
| Scholarly communication | 0.016 | 0.014 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.006 |
| 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".