The Integration of Scientific Knowledge in the Practice of Ijtihad in Contemporary \nIslamic Law: Case Study of State Islamic Religious Universities in North Sumatra and \nAceh
Bibliographic record
Abstract
This article examines the integration of knowledge in the practice of ijtihad in \ncontemporary Islamic law at State Islamic Religious Universities in North Sumatra and Aceh. \nBased on the analysis of integration of science in contemporary Islamic law research by \nstudents in Indonesian universities, this article formulates a design of integration of science as \na trend in the method of ijtihad in contemporary Islamic law. The qualitative research in this \narticle is based on data from 40 theses submitted to the Postgraduate Programmes at the North \nSumatra State Islamic University, Medan, al-Raniry State Islamic University, Banda Aceh, and \nSyahada State Islamic University, Padang Sidimpuan. Research data was obtained by \ninterviewing the theses supervisors and observing and analysing the theses contents. The results \nof this research show that the theses written by postgraduate students in the last three years \nhave integrated science in the process of ijtihad but were conducted at the interdisciplinary \nlevel of connecting the concepts of science with Islamic law. The formulation model for science \nintegration used in writing a thesis is vertical and horizontal integration, not yet moral and \nactual integration. The solution to implementing science integration in thesis writing is the need \nfor collaboration in thesis research writing between students and lecturers and a government \npolicy regarding guidelines for writing theses based on science integration, which refers to \nguidebooks published by Islamic Religious Universities.
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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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.020 | 0.023 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".