Bibliographic record
Abstract
Abstract Islamic financial literacy (IFL) concerns Muslims’ ability to manage their money while respecting Islamic law and ensuring Shariah compliance. IFL is a pressing concern in Muslim-majority countries where conventional financial literacy rates tend to be very low (<30%) (compared to the 60% global average) and IFL rates even lower (10%). Efforts to study and measure IFL are underdeveloped but growing. The paper begins by exploring what constitutes conventional financial literacy versus IFL, then profiles a detailed compendium of nearly 30 Islamic finance concepts inherent to measuring IFL – both permitted ( halal ) and forbidden ( haram ) (e.g., riba, gharar, takaful, zakat, sukuk , and faraid ). We identified and critiqued seven nascent initiatives (2016–2022) exemplifying efforts to develop IFL measures. Many initiatives only reached the development stage. Those that progressed to instrument validation yielded reliable measures, albeit seldom on a full range of Islamic finance concepts. Virtually no instruments were empirically tested. The paper culminated with recommendations for future research around studying this bourgeoning phenomenon.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".