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Record W2972998107 · doi:10.26443/jiows.v3i1.59

From the Cape to Canton: The Dutch Indian Ocean World, 1600-1800 — A Littoral Census

2019· article· en· W2972998107 on OpenAlexvenueno aff
Markus Vink

Bibliographic record

VenueThe Journal of Indian Ocean World Studies · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicAsian Studies and History
Canadian institutionsnot available
Fundersnot available
KeywordsCapeGeographyIndigenousCensusColonialismLittoral zonePopulationAncient historyBENGALHistoryEconomic historyEconomyOceanographyArchaeologyDemographyGeologySociology

Abstract

fetched live from OpenAlex

As an exercise in trans-oceanic history, this article focuses on the Dutch IndianOcean World in the seventeenth and eighteenth centuries from the Dutch EastIndia Company or VOC’s permanent colony at Cape Town, South Africa, inthe Far West to its seasonal trading factory at Canton (Guangzhou), in the FarEast. It argues that the ‘seismic change’ after 1760 noted by Michael Pearsonand associated with the British move inland from their Bengal ‘bridgehead’should be extended to the contemporary polycentric Dutch expansion intothe interior of, most notably, South Africa, Ceylon, Java, and Eastern Indonesia.Demographic measuring points include the number of Dutch citizens andsubjects, comprising European settlers, mixed peoples, and indigenous populations; and: the size and composition of the population of central nodal places in the Dutch Indian Ocean thalassocratic network in the late seventeenth and late eighteenth centuries. By the end of the period, both ‘John Company’(EIC) and ‘Jan Kompenie’ (VOC) effectively were, to some extent, reversingthe colonial gaze inland turning from maritime merchants into landlords andtax collectors. These seismic changes with multiple epicenters were the harbinger of tidal waves about to sweep both the littoral and interior of the modern Indian Ocean World.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.425
Threshold uncertainty score0.846

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.012
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0100.002

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 head0.028
GPT teacher head0.303
Teacher spread0.275 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations3
Published2019
Admission routes1
Has abstractyes

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