Bibliographic record
Abstract
The purpose of this exploratory and to some extent descriptive analysis is to highlight the Islamic banking & finance theory, and to explain the practical disparity all over the Muslim Umma along with commonalities of Islamic banking in them. Islamic banking has been now become a value proposition which transcends cultures and will do speedily in next decades despite of cutting throat competition expected in global banking scenario. The size of Islamic Financial Industry has now reached size of US$ 250 Billion and its growing annually @ 15% per annum. Institutions like Accounting and Auditing Organization for Islamic Financial Institutions (AAOIFI) and Islamic Finance Services Board (IFSB) have been formed. Due to these collective efforts, Islamic banking is now recognized by IMF, World Bank and Basel Committee. While, 27 Muslim countries including Bahrain, UAE, Saudi Arabia, Malaysia, Brunei and Pakistan 15 non-Muslim countries including USA, UK, Canada, Switzerland, South Africa and Australia has already adopted it, but in all these countries a lot of diversities lie over theory “ Shariah Principles” and their implementation as bank products/services. In such context our effort is expected to be fruitful for not only local but international policymakers and scholars to overcome these disparities and to really make it a value proposition which transcends cultures……….
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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 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.054 | 0.012 |
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".