Bibliographic record
Abstract
Hoi-eun Kim. Doctors of Empire: Medical and Cultural Encounters between Imperial Germany and Meiji Japan. Toronto: University of Toronto Press, 2014. 272 pp. $55 (cloth/ebook). \n \nAs recently as the early 1980s, the literature in English concerning the broader transformation of East Asia as a space for emerging developments in science, technology, and medicine (STM) was dominated almost exclusively by works on imperial China. This is not surprising, given its considerable historical legacy as the dominant cultural force in the region. It was perfectly acceptable within the field, moreover, to treat neighboring countries within this Sinocentric framework, or at least to regard their cultural and historical indebtedness to China as one of their central features of interest. If I exaggerate the hegemonic force of China studies in the recent past to make a rhetorical point, I do so to mark the arrival of a great deal of newer scholarship concerning the transformation of the East Asian region since the nineteenth century, and arguably since at least the seventeenth century, particularly within the field of medicine—whether Western, “traditional” (a problematic term, admittedly), or even, in more complex cases, those practices embedded within a dense nexus of religious worship and healing. The work under review here, Hoi-eun Kim’s Doctors of Empire, provides a new and welcome addition to the growing literature on Meiji Japan, following in the tradition of a substantial body of previous work on scientific and technological accomplishments, including studies by James Bartholomew (1993), Tessa Morris-Suzuki (1994), and Morris Low (2005), among many others....
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.024 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".