The role of registerial expertise in translators’ logical choices: A case study of the Chinese medicine classic Huang Di Nei Jing
Bibliographic record
Abstract
Expertise as an indicator of a translator’s competence has experienced a growing amount of research interest recently, but little attention has been paid to the role of registerial expertise, especially in medical translation. This study aims to carry out a systemic functional investigation of the role that a translator’s registerial expertise plays, namely medical expertise, in the translations of Huang Di Nei Jing, the most ancient and important medical classic in traditional Chinese medicine (TCM). The focus is on the logical choices made by both clinician and non-clinician translators. The findings report a few interesting patterns in the translators’ logical choices in relation to their medical expertise. Firstly, clinician translators tend to have a higher degree of intervention through their strategic logical choices, and their translations tend to be more grammatically intricate. They are also found to have a stronger sense of the logical relationships in modelling medical events according to their importance. Further, although mother tongue is found to be impactful on the translators’ logical choices to some degree, it is the registerial expertise that has been found to play the major role. The evidence reported in this study suggests that the translator’s registerial expertise should be included as an important component of translator training.
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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 | 0.013 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| 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".