Contexte et efficience des institutions de microfinance au Bénin
Bibliographic record
Abstract
Abstract During the 1990s, the microfinance sector, particularly mutual and cooperative financial institutions, underwent phenomenal growth in West Africa, and today, microfinance financial institutions have established a strong presence in Benin, which can be seen in their sheer numbers and in the range of activities in which they are involved, and in their relatively high territorial coverage. Despite their dynamism, financial cooperatives in Benin seem to be facing serious difficulties managing the funds collected. They operate largely in a position of excess liquidity, thus paving the way to possible agency problems, primarily between managers and members of cooperatives. In this article, we use data from the Federation des caisses d'epargne et de credit agricole mutuel (FECECAM), the largest network of financial cooperatives in West Africa, to assess whether managers have an expense preference for unproductive expenditures. Based on the results of this assessment, we cannot conclude that there is a c...
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".