Sharia Screening Methodology: Does Its Non-Unification Affect Its Implementation?
Bibliographic record
Abstract
This paper attempts to empirically assess the possibility of applying Sharia screening criteria in the Sudanese corporate sector and investigate the possibility of extending the external Auditors’ role to include reporting on Sharia compliance of corporate activities. The hypotheses of the study revolved around identifying whether the Sudanese stakeholders accept the application of the Screening Criteria as part of the compliance methodologies to cater for companies with mixed activities, whether the Sudanese stakeholders consider available Screening Criteria as effective and robust in the control of Sharia impermissible economic activities, and whether expanding the responsibilities of external auditors to include Sharia screening compliance will increase the creditability of financial information and hence attracting more investors. The paper employs a cross-sectional survey research design and depends mainly on primary data, which is collected through a structured questionnaire. To examine the accuracy of the data and conduct the analysis a number of statistical methods including the Kaiser-Meyer-Olkin (KMO), Bartlett’s Test of Sphericity, Exploratory Factor Analysis (EFA) Confirmatory Factor Analysis (CFA), and Structural Estimation Modeling (SEM) are employed. The analysis supports the hypotheses set by the study and reveals the readiness of Sudanese stakeholders to accept applying the Sharia screening Criteria and their belief in the notion that the present screening criteria are effective in controlling Sharia impermissible economic activities and the ability of external auditors to identify and report on their customers’ compliance with screening criteria.
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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 | 0.121 | 0.302 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".