Employment of Reduplicated Words in E-C Translation of Children’s Literature: A Case Study of Ren Rongrong’s Translation of The Wind in the Willows
Bibliographic record
Abstract
This thesis is to explore the application of reduplicated words in Chinese translation of children’s literature. It selects Ren Rongrong’s translation of The Wind in the Willows to conduct research. First, data collection and analysis of reduplicated words in the text are carried out. Then, from the three effects of reduplicated words, rhythmical effect, imaging effect, and emotional effect, the corresponding sentences in Ren Rongrong’s translation of The Wind in the Willows are selected and analyzed. Finally, it is concluded that reduplicated words are widely used in the English-Chinese translation of children’s literature. In the process of English-Chinese translation, verbs, adjectives, adverbs, nouns, quantifiers, numerals and onomatopoeia can be translated into reduplicated words when necessary. On the one hand, it can add language charm and make the translation more vivid, and on the other hand, it is easy for children to accept.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".