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Record W3185996556 · doi:10.1017/s0021853721000396

The Demography of Slavery in the Coffee Districts of Angola, c. 1800–70

2021· article· en· W3185996556 on OpenAlexfundno aff
Jelmer Vos, Paulo Teodoro de Matos

Bibliographic record

VenueThe Journal of African History · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicColonialism, slavery, and trade
Canadian institutionsnot available
FundersUniversity of GlasgowLeverhulme TrustFundação para a Ciência e a TecnologiaFederation for the Humanities and Social Sciences
KeywordsAtlantic slave tradePortugueseEmpireContext (archaeology)HistoryCash cropCommodityAtlantic WorldPopulationEthnologyGeographyEconomic historyAncient historyPolitical scienceEconomyDevelopment economicsDemographySociologyArchaeologyEconomicsAgriculture

Abstract

fetched live from OpenAlex

Abstract This article uses demographic data from nineteenth-century Angola to evaluate, within a West Central African setting, the widely accepted theory that sub-Saharan Africa's integration within the Atlantic world through slave and commodity trading caused significant transformations in slavery in the subcontinent. It specifically questions, first, whether slaveholding became more dominant in Angola during the last phase of the transatlantic slave trade; second, whether Angolan slave populations were predominantly female; and third, whether slavery in Angola expanded further during the cash crop revolution that accompanied the nineteenth-century suppression of the Atlantic slave trade. Besides making a significant contribution to understanding the demographic context of slavery in the era of abolition, the article aims to display ways in which historians can use the population surveys the Portuguese Empire carried out in Africa from the late eighteenth century.

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 score0.084
Threshold uncertainty score0.167

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
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 head0.023
GPT teacher head0.253
Teacher spread0.231 · 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

Citations4
Published2021
Admission routes1
Has abstractyes

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