Hoi-eun Kim.<i>Doctors of Empire: Medical and Cultural Encounters between Imperial Germany and Meiji Japan</i>.
Bibliographic record
Abstract
When I was a child in Japan, I used to wonder why our family doctor wrote medical records in German even though he could not speak the language. Doctors of Empire: Medical and Cultural Encounters between Imperial Germany and Meiji Japan seeks to trace the origins of such enduring German influence in Japanese medicine. This book follows the German doctors hired by the Meiji government in the late nineteenth century and their Japanese students, the first generation of doctors fully trained in Western medicine who then became the leaders of modern Japanese medical science. The appeal of this scholarship lies in Hoi-eun Kim’s mastery of the German, Japanese, and English languages, which enables the author to explore the archives in Germany and Japan and incorporate sources from all three languages. The author presents two underlying questions in the introduction and provides answers in the epilogue. The first question is whether Japan’s biological weapons development during World War II had anything to do with the earlier German tutelage of Japanese medical modernization. Kim’s answer is yes, indeed, but not because German medicine had an inherently genocidal nature or because the two countries were exceptionally “feudal,” as others have argued, but because Germans equated medicine with laboratory research rather than with a clinical method to cure diseases and understood the resulting close relationship between medical scientists and the state. The second inquiry concerns whether Germany was able to use its influence in Japan (“soft power” as Kim calls it) to serve its imperial interests in Asia. Here, Kim’s answer is no, because of the German sense of racial superiority to the Japanese.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".