The Long-term Pattern of Maritime Trade in Java from the Late Eighteenth Century to the Mid-Nineteenth Century
Bibliographic record
Abstract
This article investigates the trade pattern of Java from the late eighteenth centur to the mid-nineteenth century from a long-term perspective. There is no comprehensive data on Javanese trade during the period in question, with information on local and regional trade being particularly scarce. To fill in the missing pieces and identify a broad trend, this paper attempts to examine data on both the late eighteenth century and the second quarter of the nineteenth century and put them together with the scattered data available on the first half of the nineteenth century.This paper suggests, first, that while it is known that Java's economic relations with the outside world were heavily oriented toward trade with the Netherlands, this trend began in the late eighteenth century rather than with the introduction of the Cultivation System in 1830. Second, Java's coastal trade also began to develop in the late eighteenth century. This trade was conducted by European traders and Asian indigenous traders, including overseas Chinese traders settled in Java.Third, trade with the Outer Islands declined in the late eighteenth century but resumed its expansion in the second quarter of the nineteenth century. Fourth, intra-Asian trade with the region outside insular Southeast Asia declined in the long run, along with the decline and bankruptcy of the VOC, which had successfully engaged in this branch of intra-Asian trade since the seventeenth century.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".