Bibliographic record
Abstract
Nonverbal behavior plays an important role in literary work but receives little attention in literary translation. Different use of linguistic devices by translators in representing nonverbal behavior of the source text would portray different images of characters. This paper, taking Chinese writer Shen Congwen’s short story Pai-tzu as an example, compares the translation of nonverbal behavior in its two English versions (by Ching Ti and Hsu Kai-yu). It firstly reviews definition and category of nonverbal behavior by scholars in diverse fields, as well as related theories in literature and translation. It then compares the two versions in dealing with the paralanguage and kinesics of the two characters, and explores how the differences between them lead to different features of the characters. This paper comes to the following conclusion: Ching’s version, by the choice of material or behavioral process and illocutionary verbs indicating voice quality, shapes a louder and more dynamic image of the woman, in contrast with a static image in Hsu’s version; the image Pai-tzu is vividly portrayed by Hsu due to the use of marked vocabulary and addition of chronemics and proxemics elements, in contrast with core vocabulary and word omission in Ching’s version; in dealing with body parts as agent metonyms, Ching’s version is closer to the style of the original due to the choice of agent metonyms and material process, while Hsu opts for mental process with human agent.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".