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.
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 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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".