Chinese Economic Dominance in Southeast Asia: A<i>Longue Duree</i>Perspective
Bibliographic record
Abstract
Abstract As the industrialization process in Western European countries took off in the late nineteenth and early twentieth centuries, they largely turned to Asia and Africa for raw materials and other resources, as well as for markets of their manufactures. Various entrepreneurial diasporas, including the Indians, Lebanese and Chinese, were at the forefront to exploit these burgeoning economic possibilities, particularly in gathering local mineral and agricultural commodities and marketing European goods in the Afro-Asian regions. The Chinese activities in Southeast Asia stood out: they not only presided over the commercial realm but also organized mining production and cash crop agriculture in ways largely autonomous of the colonial regimes and Western entrepreneurs. How can we explain the dominance of the Chinese migrants and sojourners in the Southeast Asian economy from the 1850s to the 1930s? This paper repudiates the existing literature, which largely credits their economic presence to conscious immigration policies of the colonial authorities, and instead highlights the effects of a confluence of developments in the early modern period (ca. 1450–1800), including the sidelining of South Asians, West Asians, and regional trading communities in favor of the Chinese. A particular focus is the roles played by symbolic capital and mechanisms of advanced credit and spiral marketing, and how these gave the Chinese a comparative advantage over other trading groups.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".