Haiti's<i>caisses populaires</i>: home-grown solutions to bring economic democracy
Bibliographic record
Abstract
Purpose – Bad governance and corrupt politics have left millions of people disenfranchised. In spite of an oppressive and undemocratic state, poor Haitians have created their own informal groups, cooperatives andcaisses populaires(credit union) movements – a testimony to the democratic spirit of the poor masses. The paper aims to discuss these issues. Design/methodology/approach – A mixed qualitative study using interviews, surveys, focus groups, ethnography techniques and literature review. Findings – Lenders who run thecaisses populairesare not class or race biased; they understand how to make microfinance assist the marginalized poor in a society segregated by class and race. Cooperatives and credit unions (calledcaisses populairesin Haiti) are able to reach hundreds of thousands of people. Originality/value – These lenders one or two generations removed from the people they serve understand their reality and take careful steps and plan in a way to ensure their loans are structured to be socially inclusive. In fact, black microfinance lenders, as well as whitened local elites and foreigners, have a socially conscious philosophy of using microfinance as a vehicle to ensure economic democracy for the masses. In doing this, they take personal risks. Theti machannsrecognize these efforts and as a result trust these credit programs.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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.
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".