Proposing an Integrated Islamic Microfinance Model in Alleviating Poverty and Improving the Performance of Microenterprises in Indonesia
Bibliographic record
Abstract
Objective – This study proposes an integrated Islamic microfinance model in alleviating poverty and improving the performance of microenterprises based on a case study of Indonesia, by focusing specifically on BRI Microbanking. Design/methodology – This study adopts the exploratory study to construct the integrated Islamic microfinance with the purpose to alleviate poverty and enhance the business performance of enterprises. Results – As Islamic microfinance is known widely due to the high demand from Muslim countries. Since, it plays a crucial role effectively in alleviating poverty and developing the business performance on enterprises, particularly on microenterprises. Presently, many scholars attempted to build a successful Islamic microfinance model by using Islamic financing instruments such as mudarabah, musyarakah, and murabahah. This study attempts to build an integrated Islamic microfinance model by using BRI Syariah Micro as a case study. It is expected that this integrated Islamic microfinance model can enrich existing models in terms of social and economic aspects. Originality/Value – This research concentrates on proposing an integrated Islamic microfinance model based on the case study of BRI Syariah Microbanking. There seems to be a gap in the literature on the actual implementation of integrated Islamic microfinance in the world. The study highlights major factors to be emphasized to ensure the effectiveness of proposing an integrated Islamic microfinance model for BRI Syariah micro banking to alleviate poverty and to improve the performance of microenterprises.
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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 | 0.001 |
| 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 | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".