NGOs in Microfinance: Learning from the Past, Accepting Limitations, and Moving Forward
Bibliographic record
Abstract
Abstract Over the past three decades, microfinance has become an extremely popular and populist development intervention. The Grameen Bank and its founder, Dr. Muhammad Yunus, were awarded the 2006 Nobel Peace Prize and the Global Microcredit Summit in Halifax, Nova Scotia set itself the goal of lending to 175 million people around the world by 2015. Despite such global enthusiasm, the limitations of microfinance are gradually being acknowledged even by its most vociferous proponents. The vast majority of NGOs in microfinance not only face tremendous challenges in balancing outreach and financial sustainability, but there is also growing evidence of their failure to make an aggregate impact on poverty reduction. At the same time, there is evidence that NGOs can – even without offering credit or savings programs – play extremely important roles in areas such as poverty relief, marketing, enterprise development, innovation, and social intermediation. This article looks at strategies NGOs in microfinance can use to meet their social justice goals without becoming completely seduced by commercial values or being lured away from their major objective of serving the poor. As a development methodology, microfinance is firmly embedded within a neo‐liberal framework that seeks to increase poor people’s access to financial resources without really challenging the entrenched status quo of unequal power relations between different groups of people. The continued popularity of microfinance should not foreclose the possibility of more creative and complex engagement with inequality as well as for more boldly original visions and innovative solutions to promote human dignity and social justice.
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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 | 0.032 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.023 |
| Scholarly communication | 0.022 | 0.031 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.011 | 0.015 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".