Development of Social Cost and Benefit Analysis (SCBA) in the Maqāṣid Shariah Framework: Narratives on the Use of Drones for Takaful Operators
Bibliographic record
Abstract
Takaful operators are part of the Islamic financial institutions that are expected to achieve the commercial and social objectives by their stakeholders particularly the takaful participants (policyholders). First, this study aims to postulate a new framework to measure cost effectiveness by including the social and economic benefits of drone-assisted technology in the context of maqāṣid Shariah. Second, the study intends to investigate how the takaful industry can benefit from the drone-assisted technology, particularly in terms of cost reduction. This paper presents an early finding that forms part of a bigger research project which is focusing on the use of drone for disaster victim identification (DVI). This study employs thematic analysis of qualitative research method by engaging key informants who are Shariah expert, drone practitioner and accounting expert. In the context of emerging economies like Malaysia, the adoption of drone is sporadic when some industries such as military and agriculture are quite experienced with it; but for the takaful sector is almost none. This study provides preliminary findings that suggests there is potential of cost effectiveness for drone usage from the perspectives of SCBA in the maqāṣid Shariah framework. The main contributions from this paper are: (1) the new SCBA framework derived from the maqāṣid Shariah perspective and, (2) the application of this framework in examining the cost effectiveness on the use of drones by the takaful operators especially during disaster.
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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 | 0.012 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.006 | 0.015 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".