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Record W6893842634 · doi:10.5281/zenodo.5702413

Microcredit and savings associations for building rural household resilience: A case study of selected village development fund and savings groups in Koh Kong and Mondul Kiri, Cambodia

2021· article· en· W6893842634 on OpenAlexaff

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2021
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMicrofinance and Financial Inclusion
Canadian institutionsInternational Development Research Centre
Fundersnot available
KeywordsSustainabilityLoanFocus groupPaymentEquity (law)RevenueSustainable developmentPosition (finance)Coping (psychology)

Abstract

fetched live from OpenAlex

This case study was conducted to generate insights on the financial sustainability of selected VDFSGs and to gather information on members’ perceptions of the usefulness of these institutions in coping with household and climate change-related shocks or stresses. Financial sustainability was analyzed by conducting a detailed financial analysis of six selected VDFSGs to determine the sufficiency of interest payments as revenue to cover total costs as well as to evaluate loan recovery and equity build- up. Members’ perception of the usefulness of VDFSGs in helping them to cope with and adjust to family and climate change-related shocks/stresses was determined by conducting Focus Group Discussions (FGDs) and Key Informant Interviews (KIIs) among selected representatives of VDFSG members. Useful feedback of the financial performance and areas for improvement were generated. The Pu Hong, Pu Chhob, and Prek Svay VDFSGs were considered financially sustainable based on the results of the study. The study also revealed that the VDFSGs are considered most useful when there are crop failures due to extreme weather events and when there are medical emergencies in the household. The FGD participants and key informants expressed confidence that they are in a better position to cope with their vulnerabilities due to the presence of a VDFSG in their village.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.470
Threshold uncertainty score0.885

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.049
GPT teacher head0.240
Teacher spread0.192 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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