Bibliographic record
Abstract
All of the cases under study—from Latin American and the Caribbean, as well as North America—have horrific legacies of enslavement, colonization, and racism, and the cases will be used to discuss how Black people have contributed to the social amelioration of their communities through social-purpose businesses, which strive to reach both social and economic objectives. The Black social economy is taking place all over the Americas and is proving to be a viable alternative to extreme forms of capitalism. Brazil, with one of the largest Black diaspora populations in the world, has the legacy of Quilombos (cooperatives run by Afro-Brazilians) to retain their African cultural heritage and to have sustainable economic livelihoods. Caribbean women in Jamaica, Haiti, Guyana, Grenada, and Trinidad and Tobago organize economic cooperatives to support businesses and local projects. In Latin America, the experiences of Afro-Argentines and Afro-Colombians are lesser known cases but nonetheless have a rich history of cultivating community economies to preserve their own culture in the face of business and social exclusion. The story would not be complete without the study of the Black diaspora in the USA and Canada who encounter many forms of violence in the society. African-Americans have always had mutual-aid societies as a way to cope in a hostile environment. In Canada, newcomers from Africa and the Caribbean hold onto informal money collectives as a way to preserve their heritage and to deal with business exclusion. As explained, this work contributes to the global conversation on alternative social practices that empower traditionally marginalized social groups. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.013 | 0.038 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".