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Record W3111921112 · doi:10.51325/ijbeg.v3i2.29

<b>The Role of Islamic Legitimacy Basis </b><b>t</b><b>o Combat the Financial </b><b></b><b>Corruption in Kuwait </b><b></b><b></b>

2020· article· en· W3111921112 on OpenAlexaff
Melfi Muhammed Al Azemi, Abdel Majeed Al Omari, Tawfeeq Al Omrani

Bibliographic record

VenueEuroMid Journal of Business and Tech-innovation (EJBTI) · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicIslamic Finance and Banking Studies
Canadian institutionsCouncil of Ministers of Education
Fundersnot available
KeywordsShariaLanguage changeIslamLegitimacyIntervention (counseling)BusinessAccountingPolitical scienceLawPoliticsPsychology

Abstract

fetched live from OpenAlex

The study aims to identify the role of Islamic Sharia to alleviate the problem of financial ‎corruption in ‎Kuwait. The researchers rely on the descriptive method. An extensive literature ‎review was performed with regards to the legitimate ways to combat the ‎financial corruption. The study has conducted interviews with Kuwaiti religious officials ‎who have a long experience in the field of financial ‎corruption. Results indicate that the role ‎of Islamic Sharia is almost absent due to the lack of ‎religious officials’ intervention. Their role in ‎the field of combating financial corruption should be activated and not only restricted to ‎media. This fact had increased the level of the financial corruption according to the annual financial corruption ‎index. The study conveys important remarks. Kuwaiti official ‎ministries and related organizations should embrace Islamic Sharia and rely on the wisdom of religious officials. To combat financial corruption, governments should reinforce the application of Sharia law in Kuwait since it was completely ignored in the past.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.003
Scholarly communication0.0040.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.001

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.

Opus teacher head0.015
GPT teacher head0.215
Teacher spread0.200 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations9
Published2020
Admission routes1
Has abstractyes

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Same venueEuroMid Journal of Business and Tech-innovation (EJBTI)Same topicIslamic Finance and Banking StudiesFrench-language works237,207