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Record W4400620096 · doi:10.5539/ijef.v16n8p31

The Impact of Islamic Microcredit on Economic Development of Women in Somalia

2024· article· en· W4400620096 on OpenAlexvenueno aff
Mohamud Dahir Hilif, Dayah Abdi Kulmie, Burhan Mohamed Osman

Bibliographic record

VenueInternational Journal of Economics and Finance · 2024
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMicrofinance and Financial Inclusion
Canadian institutionsnot available
Fundersnot available
KeywordsIslamMicrofinanceEconomicsDevelopment economicsEconomic growthGeography

Abstract

fetched live from OpenAlex

Islamic financial institutions are obliged to adhere to Shari’ah principles, which promote ethical financial transactions. These institutions prioritize not only their business concerns but also strive towards broader socio-economic development objectives. Microcredit services play a crucial role in facilitating the achievement of these goals and aligning with the overarching Shari’ah objectives. The purpose of this research is to scrutinize the impact of Islamic microcredit on the economic advancement of women in Somalia. To explore the correlation between microcredit provision and women’s economic progression, a correlational research design was selected as an appropriate approach. Primary data collected from 135 respondents were then subject to analysis using SPSS software. Substantive evidence emerged suggesting a substantial positive association between microcredit services and women’s economic development. Consequently, our findings demonstrate that both microcredit provision and overall economic progress exhibit statistically significant positive effects on the economic advancement experienced by women within Somalia. The research findings suggest that a higher degree of adoption of microcredit services has the potential to enhance women’s financial status, self-assurance, and improve their chances for uplifting themselves from poverty. Consequently, these outcomes can contribute to the overall progress and well-being of society as a whole.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.376
Threshold uncertainty score0.440

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.015
GPT teacher head0.251
Teacher spread0.236 · 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 designTheoretical or conceptual
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

Citations2
Published2024
Admission routes1
Has abstractyes

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