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Record W2948396588 · doi:10.3968/10970

A Study on Benjamin Hobson’s Contribution to the Translation of Western Medicine in Modern China

2019· article· en· W2948396588 on OpenAlexvenueno aff
Ruoran Wang, Changbao Li

Bibliographic record

VenueCross-cultural communication · 2019
Typearticle
Languageen
FieldArts and Humanities
TopicTranslation Studies and Practices
Canadian institutionsnot available
Fundersnot available
KeywordsChinaMilestoneWestern medicineFoundation (evidence)TerminologyClassicsVocabularyModern medicineTraditional Chinese medicineHistoryTraditional medicineMedicinePhilosophyAlternative medicineLinguisticsLawPolitical sciencePathology

Abstract

fetched live from OpenAlex

Benjamin Hobson is a British medical missionary who came to China in the Qing Dynasty. Living in China for nearly twenty years, Hobson had quite a few translation works published, and he was not only the first one who systematically translated various kinds of medicine theories into Chinese but also the pioneer of creating medical terms in Chinese. This paper first attempts to make a thorough inquiry into Hobson’s medical translation practice and his views on translation, and then points out that Hobson’s major contributions to the translation of western medicine in modern China are that his medical translation promoted the popularization of western medicine in China, that the publication of Treatise on Physiology set a milestone of translating medicine works for modern China to learn from the West, and that his compilation of A Medical Vocabulary in English and Chinese laid the foundation for the Chinese translation of modern medicine terminology.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.016
metaresearch head score (Gemma)0.021
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.053
Threshold uncertainty score0.106

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.021
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.006
Science and technology studies0.0210.019
Scholarly communication0.0060.006
Open science0.0020.006
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.077
GPT teacher head0.368
Teacher spread0.291 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2019
Admission routes1
Has abstractyes

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