Cross-cultural communication of medical knowledge in 19th century China: A corpus-based perspective through grammatical metaphor
Bibliographic record
Abstract
This study explores shifts in discourse strategies in the cross-cultural communication of medical knowledge from Britain to China in the 19 th century. The study adopts a novel perspective via grammatical metaphor that encodes the cognitive construal of medical knowledge. A parallel corpus was constructed containing both the original text in English and the translated text in Chinese. Extensive linguistic annotation of the corpus was carried out to identify metaphor-congruency shifts encoded grammatically as noun-to-verb and noun-to-adjective shifts between the source language and the target language. Based on empirical observations, the study uncovers important differences in communication strategies across the two texts through metaphor-congruency changes as a meaning-making choice for knowledge construal. To us, such shifts in the semiotic representation as communication strategies might represent cultural adaptations necessary for the smooth transmission of disciplinary knowledge across British and Chinese cultures. The study suggests that the cognitive underpinning of linguistic encodings is specific to explicitation and local experience, which influence how ideas and concepts are perceived and represented in medical discourse in particular and cross-cultural communication in general.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".