Cryptocurrency Prospect in the International Market: Pre-sent Value Management and Islamic Perspective
Bibliographic record
Abstract
Cryptocurrency is an investment commodity that can generate returns and already has a license to trade in exchange. This study aims to examine the prospects of digital cryptocurrency assets more deeply by summarising the results of literature studies in various countries. As a result of this study, it is known that many countries whose governments make strict regulations on support for legality and allow cryptocurrency transactions include: European Union members, namely Germany and Italy, and non-member countries of the European Union, such as Gibraltar. Furthermore, in the Americas, there are Canada and Venezuela. In East Asia and the Asia Pacific, Australia and Japan support the existence of Cryptocurrency. Meanwhile, in Southeast Asian countries, there are contradictions between several countries, including Indonesia, Malaysia, Vietnam, and the Philippines, that reject cryptocurrency transactions because they are considered threats to money laundering and the problem of terrorism. Of course, this is a prospect that cryptocurrency transactions can meet the expectations of all users in the world by making regulations regarding the legality of Cryptocurrency so that the transaction model can be integrated between users, both as an asset and a substitute for international payment currencies.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.000 | 0.001 |
| 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".