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Record W4402243353 · doi:10.1177/08438714241272583

‘Belonging to the Company’: Transporting and documenting unfree labour in the Indian Ocean, 1719–1790

2024· article· en· W4402243353 on OpenAlexafffund
Margaret Schotte

Bibliographic record

VenueInternational Journal of Maritime History · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicGlobal Maritime and Colonial Histories
Canadian institutionsYork University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsTerminologyLegislationContext (archaeology)Indian oceanOrder (exchange)HistoryPolitical scienceBusinessLawArchaeologyFinanceOceanographyLinguistics

Abstract

fetched live from OpenAlex

Over the course of the eighteenth century, French East India Company ships carried numerous sailors, soldiers, passengers and unfree labourers to and from various ports of trade in the Indian Ocean. Although European merchant companies developed extensive documenting systems, certain elements received little attention in the records. When it came to tracking unfree labourers, Company employees used terminology with ambiguous meanings and categories that were codified in the Atlantic context and therefore not initially applicable in the Indian Ocean. In order for historians to interpret these records more accurately, this article reviews specific terminology and pertinent French legislation about racialized labourers. This contextual information helps to uncover previously overlooked groups of unfree labourers working for - and, at times, trying to escape from - the French East India Company in the Indian Ocean and beyond.

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)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.543
Threshold uncertainty score0.920

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.005
Science and technology studies0.0100.008
Scholarly communication0.0040.002
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.011
GPT teacher head0.273
Teacher spread0.262 · 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 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

Citations1
Published2024
Admission routes2
Has abstractyes

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