Balancing Shariah Authenticity and Market Stability: A Scenario-Based Framework for Implementing AAOIFI Shariah Standard No. 62 in the Global Sukuk Market
Bibliographic record
Abstract
This work develops a scenario-based policy framework for the prospective implementation of AAOIFI Shariah Standard No. 62 in global sukuk markets. The analysis suggests that immediate, rigorous enforcement would advance Shariah authenticity yet risk near-term destabilisation: issuance could retrench, the pricing premia could widen, and the rating treatment could bifurcate or even become inapplicable for instruments with pronounced risk-sharing. By contrast, calibrated sequencing, targeted legal reforms to perfect title transfer, and harmonised supervisory guidance can mitigate fragmentation and sustain investor confidence while re-anchoring sukuk to their risk-sharing foundations. Taken together, aligning religious fidelity with market pragmatism is achievable: a measured adoption of Standard 62 can reinforce the ethical underpinnings of Islamic capital markets without compromising their capacity for resilient growth.
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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 | 0.017 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".