The Potential of Bait al-M l wa Tamwil (BMT) in Developing The Border Area of Indonesia - Malaysia
Bibliographic record
Abstract
This research aims at investigating the potential of Bait al-Māl wa Tamwil (BMT) in the development of border areas of Indonesia-Malaysia by prioritizing the welfare approach. Primary data was obtained through in-depth interviews with respondents from academics, practitioners, and regulators. The Analytic Network Process (ANP) method and Benefit Opportunity Cost Risk (BOCR) network are used to review the interview results. The results reveal that BMT is able to help the development of the border areas. Moreover, the aspects of benefit and opportunity in exploiting BMT is more significant than the cost and risk aspects. There are three alternative strategies to be implemented in order to maximize the benefits and opportunities and also minimize the costs and risks, namely the capital of third parties, linkage programs, and special regulations. Penelitian ini mengkaji potensi Bait al-Māl wa Tamwil (BMT) dalam pembangunan di wilayah perbatasan Indonesia-Malaysia dengan memprioritaskan pada pendekatan kesejahteraan. Data primer penelitian ini diperoleh melalui wawancara mendalam dengan responden yang berasal dari akademisi, praktisi dan pembuat kebijakan. Metode Analytic Network Process (ANP) and Benefit Opportunity Cost Risk (BOCR) network digunakan dalam analisis hasil wawancara. Dalam studi ini ditemukan bahwa BMT berpotensi untuk dapat membantu pembangunan di wilayah perbatasan, Selain itu, aspek manfaat dan peluang dari pemanfaatan BMT lebih signifikan daripada kerugian dan resikonya. Ada tiga strategi alternative yang dapat diimplementasikan untuk memaksimalkan manfaat dan peluang dari BMT dan juga meminimalisir kerugian dan resikonya, yaitu modal pihak ketiga, keterkaitan antar-program, dan peraturan-peraturan khusus terkait.
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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 | 0.002 |
| 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 | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".