Research Design Based on Fatwa Making Process: An Exploratory Study
Bibliographic record
Abstract
The Fatwa making process is a process used by an Islamic mufti (A Muslim legal expert) to issue a non-binding opinion or fatwa (judicial pronouncement in Islam) on the point of Islamic sharia law when a question is raised by a mustafti (The person who asks a mufti for a fatwa), the person who has asked for a fatwa. The mufti will issue the fatwa through four fatwa-making stages, which are al-taswir (problem description), al-takyif (adaptation), al-hukm (legal explanation) and al-ifta' (fatwa determination). This process is intended to ensure the fatwa issued is accurate and appropriate. The research design is defined as a logical action plan, and it functions as a planning framework that involves all processes holistically to achieve the objectives of the study. This study explored the appropriateness of the fatwa making process in research design, the purposes, steps, and resources of the research design based on the fatwa-making process and why it is not used to conduct Islamic-related researches. The two main objectives of the study was to identify the fatwa-making process, and to analyse the research design based on the fatwa-making process. In order to achieve these objectives, the qualitative study adopted document analysis and content analysis methods. The research found that the fatwa-making process possessed detailed steps that resembled research design and was more appropriate in conducting Islamic-related research based on the purposes, actions, and resources of research designs based on the fatwa-making process.
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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 | 0.047 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".