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Record W2153335370 · doi:10.1017/s0010417512000564

Chinese Economic Dominance in Southeast Asia: A<i>Longue Duree</i>Perspective

2013· article· en· W2153335370 on OpenAlexaff
Kwee Hui Kian

Bibliographic record

VenueComparative Studies in Society and History · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicSocioeconomic Development in Asia
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsDominance (genetics)ColonialismEconomyIndustrialisationCash cropPolitical scienceGeographyAgricultureDevelopment economicsEconomicsMarket economy

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.267
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.003
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.061
GPT teacher head0.361
Teacher spread0.300 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations16
Published2013
Admission routes1
Has abstractyes

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