Bibliographic record
Abstract
Acknowled gmentsThis project began with a discovery I made a dozen years ago.As I was wrapping up my first book on colonial Korea, it dawned on me that many entrepreneurs and exponents of the Japanese empire hailed from a single province, Ōmi (present-day Shiga prefecture), and their network stretched far beyond the Korean peninsula.I too learned that while I grew up in Japan familiar with the famed peddlers of Ōmi, many U.S. colleagues had barely heard of them.These revelations led to my aspiration to write a new history of empire through a provincial lens, extending my research horizons spatially to the Chinese continent and across the Pacific to North America, and temporally back to the Tokugawa archipelago.Along my ensuing journey through uncharted and unfamiliar archives, I have incurred numerous debts to scholars and colleagues who began crossing conventional boundaries of scholarship far ahead of their field.Kären Wigen has been my muse from the inception.She was also part of a dream team of historians who read the first draft of my book manuscript: Priya Satia, David Howell, and Jordan Sand (who also reviewed a revised manuscript later).I thank them for their valuable feedback and inspiration, and Jenny Martinez of the Stanford Humanities Center for organizing a manuscript workshop, despite being held days after the Capitol insurgency in January 2021. I made my first foray into the field of transpacific history in 2011, when Eiichirō Azuma and Sydney Xu Lu
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.341 | 0.202 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".