Poverty reduction and rural finance: From unsustainable programs to sustainable institutions with growing outreach to the poor
Bibliographic record
Abstract
Only relief achieves short-term poverty reduction, but is ineffective in the long run. Sustainable poverty reduction can only be attained through well-designed long-term development measures. For example, Indonesia is considered one of the most successful countries with regard to poverty reduction. Between 1970 and 1996, it reduced poverty from 60% to 11.5% of its population, a time span of a quarter century during which local financial institutions expanded rapidly. The Asian financial crisis led to a set-back, but also became the departure point for a more sustainable institutional system. (Getubig, Remenyi and Quinones 1997:89; Seibel and Schmidt 1999:8-10) All our experience tells us: there is no short-cut to sustainable poverty reduction and development; and certainly none outside a solid, prudentially regulated institutional framework.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".