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Record W6960018205 · doi:10.13135/2421-2172/7345

An assessment of Islamic Banking in Asia, Europe, USA, and Australia

2023· article· en· W6960018205 on OpenAlexaboutno aff

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicGenetic and Environmental Crop Studies
Canadian institutionsnot available
Fundersnot available
KeywordsIslamic bankingIslamBanking industryMiddle EastRetail bankingIslamic finance

Abstract

fetched live from OpenAlex

The paper reviews the progress of Islamic banking in two major regions, including Asia and the Western world. The paper offers an inclusive discussion of Islamic banking initiatives in several countries of the globe, specifically the countries from the Middle Eastern region, Europe, Australia, Asia, and North America. The discussion for Asian countries includes Saudi Arabia, Iran, UAE, Qatar, Bahrain, Kuwait, Turkey, Egypt, Malaysia, Oman, Indonesia, Pakistan, and Bangladesh. The paper also appraises the development of the Islamic banking setup in the Western world. The Islamic banking status in the west covers the following countries: United Kingdom, Italy, Australia, Luxembourg, France, Germany, Canada and the USA. The review of Islamic banking in the two major world regions reflects a progressive Islamic banking setup. The paper entails a qualitative approach to evaluate the Islamic banking progress in various countries by extracting data from different sources, including central banks and other important financial and regulatory institutions. The study affirms that the Islamic banking paradigm emerged and flourished from the Asian states, specifically Middle Eastern and Asian countries, including Egypt, Bahrain, Qatar, UAE, Malaysia, Pakistan and Iran. The findings also suggest that the Islamic banking model has progressed gradually in European and other countries like Australia, the USA and Canada, which warrants a promising potential for the global Islamic banking sector.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.341
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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.

Opus teacher head0.280
GPT teacher head0.532
Teacher spread0.252 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2023
Admission routes1
Has abstractyes

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