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African Women and Commerce in the Atlantic World

2023· reference-entry· en· W4389925250 on OpenAlexaff
Vanessa S. Oliveira

Bibliographic record

VenueOxford Research Encyclopedia of Latin American History · 2023
Typereference-entry
Languageen
FieldSocial Sciences
TopicAfrican history and culture studies
Canadian institutionsRoyal Military College of Canada
Fundersnot available
KeywordsAtlantic WorldAtlantic slave tradeScholarshipNarrativeHistoryGeographyEconomic historyAncient historyPolitical science

Abstract

fetched live from OpenAlex

Abstract Women have been active players in the history of the Atlantic world from early times to the present. They have contributed to the economic and social fabric of Atlantic societies as food producers, mothers, healers, queens, and merchants, just to mention a few roles. Despite their importance, women were not part of the mainstream narrative neither in the early days of African history nor in the history of other geographic areas in the Atlantic world. The first wave of Africanist scholars in the 1960s were more interested in recovering the stories of great kingdoms and states and their leaders, who were, for the most part, men. It was not until the late 1970s that Africanist scholars incorporated women as a category of analysis with the publication of collections and monographs dedicated to their contribution to African societies. Women merchants commonly known as donas, signares, nharas, and senoras were among those who benefited from the new approach. Since then, scholars have explored the trajectories of women merchants and petty traders in places such as the Gold Coast, Luanda, Benguela, Saint-Louis, Gorée, Freetown, Lagos, Bissau, and Cacheu. This growing scholarship has shown that women participated actively in the trade in foodstuffs, manufactured goods, and captives, as well in tropical commodities. African women’s ability to trade crossed the Atlantic Ocean, where they and their descendants continued to control the retail trade in port cities in Brazil, the southern United States, and in the West Indies.

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)0.004
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.441
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0010.008
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
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 head0.080
GPT teacher head0.346
Teacher spread0.266 · 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.

Study designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2023
Admission routes1
Has abstractyes

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