Chapter three. ROSCAs: An Antidote to Business Exclusion
Bibliographic record
Abstract
ROSCAs are a form of mutual aid and co-operativism practised around the globe.Russian theorist Peter Kropotkin ([1902] 1976), who wrote extensively on mutual aid, found that all species rely on one another for their survival and that humans are no different.Mutual aid, and coming together, also genuinely makes us happy.During the COVID-19 pandemic, strangers rallied to help those in need by buying groceries or running errands.These stories were some of the most important during the lockdown.Commoning and informal networks based on trust and reciprocity have been brought back into the modern world.In 2020, Black protests in the United States, Canada, and Europe highlighted anti-Black racism as systemic for those who live in whitedominated societies.Exclusion of various kinds have driven women and especially those of the African diaspora to rely on each other; the women who organize ROSCAs hide what they do because of the dangers they perceive.The Banker Ladies in this book told me that they fear reprisals for organizing banking co-ops and they are scared that their activities will be seen as "illegal."A film called The Banker Ladies (Mondesir 2021) was produced to expose the anti-Black racism in business and society at large, as well as to illustrate the gendered aspects of why and how Black women engage in co-operativism. 1 c h a p t e r t h r e eROSCAs: An Antidote to Business Exclusion 1 Canadian filmmaker Luke Willms will release a film, Unbankable, which looks at how African people worldwide, including the Banker Ladies, have had to discreetly engage in banking co-ops.See the trailer for the film online at Willms (2023).
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".