How Network Analysis Uncovers International Networks of Smuggling History: Criminals in Nagasaki, Japan circa 1667
Bibliographic record
Abstract
This paper takes a network analytic approach to investigating crime in seventeenth-century Japan. In 1667, the Nagasaki magistrate’s office conducted the largest documented smuggling crackdown in Tokugawa Japan (1603–1867), busting a ring of 87 arms traffickers who had been shipping contraband to Chosŏn Korea (1392–1910). I use the office’s “criminal investigation records” (*hankachō* 犯科帳) to build a dataset of the 94 suspects from ten Japanese towns who were interrogated about their involvement at the time. Using a three-mode network (people, place, crime), the resulting graphs and statistics reveal a new geography of the crime in question: contrary to the conclusions of the original investigators and of modern-day historians who “closely read” their records, the digital analysis relocates the epicenter of the smuggling ring to be in Tsushima, not Hakata or Nagasaki, and its ringleader as a merchant named Komoda Kanzaemon, rather than Itō Kozaemon. Though various limitations are recognized, the case study demonstrates the utility of network analysis on early modern crime data in general and for archives built with criminal-investigative intent like the _hankachō_ in particular.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".