Do Self-translating Poets Have Equally Distributed Equivalent Words in the Target and Original Texts? A Corpus Examination of Yu Guangzhong’s Poems
Bibliographic record
Abstract
This article examines the utilization of high-frequency words in Yu Guangzhong’s self-translated poetry through a corpus-driven analysis. The objective is to explore the presence of equivalence and inequivalence in the translations executed by Yu Guangzhong himself. The utilization of modal verbs, lexical bundles, and keywords has been analyzed in various contexts, such as novels and speeches. However, studies that compare disparities in the use of high-frequency words between the source and target languages within poetry translation are scarce. The original and self-translated poetry corpora of Yu Guangzhong have been constructed at the word level to enhance the original texts’ co-occurrence and corresponding translations. In the comprehensive self-translation of his eighty-five poems, Yu Guangzhong encounters challenges in achieving an equitable distribution of equivalent words between the target and original texts. It is observed that most high-frequency words in Yu Guangzhong’s original and target texts lack equivalence in use and meaning. Although two of the three poems analyzed individually mainly achieve equivalence, this discrepancy might be attributed to the translator’s utilization of literal and word-for-word translations.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".