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Record W3215959857 · doi:10.1525/nrbp.2021.2.3-4.171

The Legacy of Cooperatives among the African Diaspora

2021· article· en· W3215959857 on OpenAlexaff
Caroline Shenaz Hossein

Bibliographic record

VenueNational Review of Black Politics · 2021
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMicrofinance and Financial Inclusion
Canadian institutionsThe Scarborough HospitalUniversity of Toronto
Fundersnot available
KeywordsDiasporaSolidaritySolidarity economyPopulationEconomic growthPolitical scienceSociologyAllianceGender studiesEconomicsLaw

Abstract

fetched live from OpenAlex

According to the International Co-operative Alliance, more than 12 percent of the global population belong to the world’s three million cooperatives. The African diaspora has contributed to this legacy. Many Caribbean people view cooperatives as trustworthy because these institutions put people’s local needs first. Both Haiti and Grenada have deeply embedded cooperative values and identities that are rooted in the ancient African systems of Sol and Susu. The African diaspora has a strong history of organizing solidarity financial economies to counter exclusion in business and society. This article draws on interviews with 138 direct users of cooperative institutions, bankers, and experts. Based on the findings, it argues that the African diaspora has had a key role in cooperative development and that Susu and Sol are the preferred financial institutions because they give people a way to help each other in times of adversity. This research uses the theory of the Black Social Economy to analyze Caribbean cooperators and their use of informal and formal cooperatives to stymie exclusion in business and society. Documenting the Haitian and Grenadian people’s cooperative legacy reveals that the choice of the people of the Black diaspora to politicize their economic solidarity, and this has been a major contribution to the global cooperative movement.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.864
Threshold uncertainty score0.234

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
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 head0.032
GPT teacher head0.275
Teacher spread0.243 · 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 designTheoretical or conceptual
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

Citations0
Published2021
Admission routes1
Has abstractyes

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