Reference in English-Chinese Legal Translation: Human Translators Versus ChatGPT
Bibliographic record
Abstract
Reference is a device of grammatical cohesion; it refers to another element with the same semantic meaning; it can be categorized into personal reference, demonstrative reference, and comparative reference (Halliday & Hasan, 1976). This paper aims to delve into the grammatical cohesion in legal translation, focusing particularly on the translatability of reference in English-Chinese translation through a detailed case study. To this end, the International Code for the Protection of Tourists is chosen as the source text and is compared with two target Chinese translations: one by human translators and the other by ChatGPT (the fourth version). The data related to differences of reference items between English and Chinese are identified and marked. Based on Toury’s (2012) framework of descriptive translation studies, the study is qualitative and is conducted on the English-Chinese translation of reference in the legal text, the human translation, and the ChatGPT’s translation. The research reveals that three translation methods (literal translation, omission, and amplification) are used in translating reference items. It indicates that there are more advantages of human translators than ChatGPT in legal translation.
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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 | 0.019 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".