Efficience des institutions de microfinance regroupées en réseau : cas des mutuelles communautaires de croissance du Cameroun
Bibliographic record
Abstract
Réaliser leur mission sociale tout en préservant leur équilibre financier. Tel est le défi auquel sont constamment confrontés les établissements de microfinance. Contrairement aux institutions financières classiques, ceux-ci ne peuvent se contenter des seuls objectifs financiers. Or, la pérennité n’est toutefois pas facile à atteindre lorsque des buts sociaux entrent en ligne de compte. Disposer d’outils appropriés de mesure de l’efficience est fondamental dans ce contexte. Ce travail porte sur l’utilisation de méthode Data Envelopment Analysis comme outil de l’efficience des institutions de microfinance regroupées en réseau. Son application aux données des mutuelles du réseau des mutuelles communautaires de croissance du Cameroun montre que la majorité d’entre elles exploitent rationnellement leurs inputs pour produire des outputs.
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How this classification was reachedexpand
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 | 0.016 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.013 | 0.031 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".