Developing a Digital Currency from an Islamic Perspective: Case of Blockchain Technology
Bibliographic record
Abstract
Numerous studies ranging from concept papers and reviews were conducted on the matter of blockchain and digital currencies. However, those two areas are not well researched due to its being a new area of research. Furthermore, the research on blockchain applications in the Islamic financial system precisely the potential of digital currency in providing a better alternative to current fiat money system which will be the scope of this article. The aim of revolves around exploring the potential and capability of introducing a digital currency that fulfills the Islamic law (Shari’ah) functions of money and provides a more stable currency than fiat money. The method used for analyzing this object includes a library research on related topics that helps understanding the functions of money and digital currencies and study of several cases that can assist in fulfilling the objectives of this paper in introducing an Islamic digital currency through detailed research of Islamic theory of money and civilization as well as the developments of blockchain, our findings point towards the ability of introducing a Shari’ah-compliant digital currency if all the issues on validity are addressed and resolved. However, the area of digital currencies and blockchain requires further research from a Shari’ah perspective to facilitate a better understanding on the topic.
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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 | 0.003 | 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 | 0.004 | 0.006 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".