The Application of Translation Variation Techniques in Martial Arts Fiction: Taking The Book & The Sword as an Example
Bibliographic record
Abstract
Distinguishing itself from normal novels by being endowed with Chinese unique chivalrous spirits, Chinese martial arts fiction is the epitome of Chinese culture since it shares a wide range from martial arts styles, heroes’ nicknames, national conflicts, love and hate between schools and heroes, and poems to human’s body structure. Together with the destitute of martial culture in western world, the complicated plots of martial arts fictions make it extremely difficult for translators to do the work if he adopted complete translation strategy. According to the author’s observation, quite a number of translation variation techniques including large scale of addition (added explanations), deletion, etc. have been applied in English-translated Chinese martial arts novels. Therefore, the author intends to take this opportunity to testify the reasonability and practicality of the application of translation variation strategy and techniques in translating martial arts fictions by analyzing Chinese famous kung fu novel The Book & The Sword translated by English writer Enshaw.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".