Reasons of the Difference of Murabaha Accounting Standards in Islamic Banks
Bibliographic record
Abstract
This study investigates the reasons of the difference of murabaha accounting data in the Islamic banks. It depends on the analysis of the murabaha’s contract and compares between resources that affect accounting data. Investigation includes different resources of applying different accounting data of traditional account standards and fiqh pricing rules, it also investigates the difference of murabaha and the developed accounting depends on murabaha’s flexible accounting data. Researchers found that there are many resources that affect murabaha accounting data which give the director of Islamic bank choices. Fiqh pricing rule is voluntary factor for director which limits accounting data. Fiqh rule will rule murabaha of developing acceptance and flexible murabaha managing gives fair to accounting data and evaluation. There is need to limit ignorance of following fiqh. Fiqh pricing is the way to promote murabaha in Islamic bank and fixe accounting data policies to avoid accounting errors; also it limits evaluation to get fair of financing performance result’, Researchers ask to fix accounting data by suggesting model to unified Islamic accounting data and to explain the reason of the change account data of fiqh.
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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 | 0.010 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".