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Record W3175288686 · doi:10.3126/mef.v11i0.37835

Breaking the Wall of Poverty: Microfinance as Social and Economic Safety Net for Financially Excluded People in Nepal

2021· article· en· W3175288686 on OpenAlexaff
Karun Kishor Karki, Nirajan Dhungana, Bhesh Bahadur Budhathoki

Bibliographic record

VenueMolung Educational Frontier · 2021
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMicrofinance and Financial Inclusion
Canadian institutionsUniversity of the Fraser Valley
Fundersnot available
KeywordsMicrofinanceCollateralFinancial inclusionPovertyBusinessFinancial servicesGovernment (linguistics)Economic growthContext (archaeology)LoanEconomic interventionismFinanceEconomicsPolitical sciencePolitics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.574
Threshold uncertainty score0.723

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.016
GPT teacher head0.249
Teacher spread0.233 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2021
Admission routes1
Has abstractyes

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