Transparência em Instituições de Microfinança Africanas
Bibliographic record
Abstract
The lack of transparency in African microfinance institutions (MFIs) is believed to delay the dissemination of microfinance in the continent. In this study, we focus on the question of how organizational characteristics and governmental corruption influence the transparency of African MFIs. Our results show that MFIs’ maturity and larger size improve transparency in African MFIs, while a nonprofit status or a focus on small businesses do not lead to the same improvement. Regulation, in turn, makes African MFIs less transparent. Our findings also reveal the impact of governmental corruption on the relationship between organizational characteristics and MFIs’ transparency in Africa: less corruption enhances the transparency of all MFIs, excepting for the regulated ones. Finally, we show that in countries with high levels of corruption, the MFIs that should be supported in becoming more transparent are the nonprofits, those dedicated to small businesses, and the regulated MFIs.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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 teacher head, 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".