Revisiting Creative Accounting in the Context of Islamic Economic and Finance System
Bibliographic record
Abstract
The paper investigates the possibility and spread of creative accounting (CA) related practices in the emerging financial system based on Islamic ideology and economics principles. Analytical methodology has been adopted in order to answer the research questions with a view to improving clarity of concepts. At the doctrinal level, there is little likelihood for practices associated with creative accounting as Islamic finance forbids use of misleading information in business transactions. At the operational level, however, factors like adoption of conventional accounting standards, use of income smoothing practices, and the flexibility in subjective interpretation of scripture based laws (Sharia) open up possibilities for the new financial system to be permeated by creative accounting practices. Islamic finance can be plagued by the pernicious effects of CA practices, therefore, any complacency on the part of advocates of Islamic finance can be misplaced. If practices like creative accounting continue to prevail in Islamic financial system, it will negatively affect the image of Islamic finance as an ethical, just, equitable and fair system. The authors have made a contribution by drawing attention of the research community to the neglected but important issue of creative accounting by critically analysing it and arraying the doctrinal view against the operational view.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.026 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".