Haiti's<i>caisses populaires</i>: home-grown solutions to bring economic democracy
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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 and caisses 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 the caisses populaires are 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 (called caisses populaires in 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. The ti machanns recognize these efforts and as a result trust these credit programs.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it