Implementation of Salam Contracts in the Sharia Principles Framework: Surveys and Prospects in the Field
Bibliographic record
Abstract
This study aims to explore the implementation of the salam contract in the view of Sharia and analyze its prospects in the context of practice in the field. The research method used was a survey study involving participants consisting of religious experts, Islamic finance practitioners, and the general public. Data was collected through questionnaires and interviews to gain a comprehensive understanding of their views on the greeting contract. A qualitative analysis was conducted to understand the implications of sharia and the practical prospects of implementing the salam contract. The results showed that the Salam contract can be implemented by taking into account relevant sharia principles, such as fairness and clear legal provisions. However, challenges may arise regarding uniform understanding and the need for appropriate regulation to facilitate widespread practice in the field. The prospect of implementing the contract of greetings in financial and trade transactions promises more inclusive and sustainable economic sustainability under Sharia principles. This research provides a deeper understanding of the relevance and potential of the salam contract in the context of Islamic economics and provides a foundation for further development in its practice in the field.
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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 | 0.052 | 0.051 |
| 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 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".