Breaking the Wall of Poverty: Microfinance as Social and Economic Safety Net for Financially Excluded People in Nepal
Bibliographic record
Abstract
Microfinance is a financial service aimed at economically underprivileged people who have no or limited access to formal financial institutions such as banks due to the lack of financial resources, collateral, or low income. Microfinance institutions provide a collateral-free loan to low-income individuals with the principle of financial inclusion, which allows them to invest in various self-employment activities. In this article, we critically review the development of microfinance and its issues and challenges in Nepal. More specifically, using the concept of the Grameen Bank model and its relevance in the context of Nepali microfinance institutions, we explore how microfinance can be an effective tool of financial intervention to alleviate rural poverty in Nepal. Methodologically, we utilize secondary data sources such as government and non-government reports and existing empirical studies. We offer recommendations for policymakers to establish appropriate modalities, programs, and microfinance services targeting the socio-economic transformation of rural communities in Nepal. We conclude that the government and financial institutions can stimulate microfinance institutions through multidimensional interventions and facilitation to advance the socio-economic status of financially underprivileged people in rural communities in Nepal.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".