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Record W2592521885

IMPACT OF MICROFINANCE INSTITUTIONS ON EMPOWERMENT, PERFORMANCE, AND LIVING STANDARDS OF FARMERSâÂÂGROUP: A CASE STUDY ON MICROFINANCE INSTITUTIONS IN SOUTH SUMATERA-INDONESIA

2016· article· en· W2592521885 on OpenAlexvenueno aff
Umiyati Idris

Bibliographic record

VenueThe Journal of Internet Banking and Commerce · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicSMEs Development and Digital Marketing
Canadian institutionsnot available
Fundersnot available
KeywordsMicrofinanceEmpowermentProductivityBusinessStandard of livingHuman resourcesProduct (mathematics)MarketingEconomic growthEconomicsManagementMathematics
DOInot available

Abstract

fetched live from OpenAlex

Capital shortage caused farmers working in low productivity, besides that limitedness in human resources is also a serious obstacle for many farmers and group of farmers, especially in the aspects of management and production techniques, developing product, quality control, business organization, marketing techniques, and market research. The existence of microfinance institutions with the provision of loans with easy terms and consultation services program are an alternative solution to solve the problems. Primary data was collected through survey technique from 200 respondents and taken by random sampling method and was analyzed by structural equation modelling (SEM). The result showed that in aggregate program microfinance which consisted of farmer financing program and consultation services program had a positive impact towards farmers’ group empowerment and farmer’s group performance. Farmers’ group empowerment and farmers’ group performance had a positive impact toward farmers’ group living standard. It means the hypothesis was accepted. From goodness of fit indices showed that the analysis model could be accepted or had a special meaning was called unidimensional as a new concept that had been tested base on factual and empirical data.

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)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.044
Threshold uncertainty score0.290

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.037
GPT teacher head0.333
Teacher spread0.296 · 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
Published2016
Admission routes1
Has abstractyes

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