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Record W2023550175 · doi:10.1080/01440390500319612

The urban background of Enslaved Muslims in the Americas

2005· article· en· W2023550175 on OpenAlexafffund
Paul E. Lovejoy

Bibliographic record

VenueSlavery and Abolition · 2005
Typearticle
Languageen
FieldSocial Sciences
TopicColonialism, slavery, and trade
Canadian institutionsYork University
FundersCanada Research Chairs
KeywordsHausaPopulationContext (archaeology)GeographyYorubaEthnologyMulticulturalismEthnic groupHistorySociologyAnthropologyDemographyArchaeology

Abstract

fetched live from OpenAlex

Enslaved Muslims constituted a relatively small proportion of the enslaved population in the Americas, and that population was largely male. This article explores an unappreciated dimension of the background of these enslaved Muslims, the fact that most came from towns and had traveled widely, between towns; that is enslaved Muslims tended to come from urban settings, no matter where they ended up in the Americas. This urban background has implications in terms of the experiences and expectations of the enslaved. The urban context was associated with commerce, craft specialization, literacy, and political and social consciousness of slavery and its meaning within west Africa. The study examines available biographical information on enslaved Muslims from the Western Sudan, usually referred to as Mandingo or some variant in the Americas, and those from the Central Sudan, including Hausa, Yoruba, Nupe and people from Borno. The urban setting of Muslim areas of West Africa is then compared with other towns and cities in the Atlantic world during the eighteenth and nineteenth centuries in terms of size of towns and multicultural backgrounds of urban populations, further demonstrating that the urban background of many enslaved Africans and the extent to which the enslaved population was moved between towns has not been appreciated.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.691
Threshold uncertainty score0.362

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.025
GPT teacher head0.315
Teacher spread0.290 · 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 designNot applicable
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
Published2005
Admission routes2
Has abstractyes

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