The Impact of Islamic Microcredit on Economic Development of Women in Somalia
Bibliographic record
Abstract
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 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".