Three-Process Analysis Method on the Translation Process of Metaphorical Clothes Images in the Graceful and Restrained Poetry
Bibliographic record
Abstract
Considering the frequent appearance of clothes and decoration in the graceful and restrained poetry, this paper studied on the image of “greenish sleeves” in Tune: Mountain Hawthorn:She never likes to cross the river far composed by Yan Jidao(1038-1110A.D.), a representative Ci poet of the graceful and restrained poetic school in the Northern Song Dynasty of China (960-1127A.D.), a monumental period in Chinese literature. This paper borrowed as theoretical support Chinese scholar Lin Xinru’s three-process analysis method, which is a daring application of Fauconnier’s Conceptual Blending Theory in the interlingual discourse of translation. According to the guiding method, this paper analyzed the translation process of the selected poem mainly from the processes of comprehending and translating since the process of revising is a recycle of the former two in actual translation. The translation selected for analysis is provided by Xu Yuanchong, an authoritative Chinese translator of classic Chinese literature. The analysis result revealed that the blending types judged from the correspondence structure can be a pragmatic reference for translators in the translating process.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".