الحلول الفقهية والتجديد الفقهي لمعالجة تحديات وآثار الثورة الصناعية الرابعة في ضوء مقاصد الشريعة:منهاجا وبيانا
Bibliographic record
Abstract
The emergence of the Fourth Industrial Revolution is distinguished by the utilization of new technologies and inventions such as the artificial intelligence, Internet of Things (IoT), automated machine, and so on. The use of these technologies alleviates many aspects of human life and brings massive changes to the human lifestyle. It has both positive and negative effects which then leads to new issues and challenges requiring evaluation from Shariah’s perspectivesThis paper aims to discuss Shariah’s perspective on the fourth industrial revolution and solutions to the arising issues from the maqāṣid al-sharī‘ah approach. The researcher adopts descriptive methodology to discuss the phenomenon and inductive methodology to resolve the issues in the light of Shariah. The paper concludes that Islam encourages knowledge development and invention as the term ‘ilm and related words, mentioned in the Qur’ān numerously as compared to other holy scriptures. However, any inventions must be consistent and in harmony with the objectives of Shariah and not conflicting with them. With the observation of conditions and guidelines as outlined in this paper, utilization of the inventions in the Fourth Industrial Revolution may serve as a mean to realize the objectives of Shariah and will greatly benefit humankind. In the event of any issues, solutions must be derived by exercising legal reasoning (ijtihād) with consideration of the modern sciences and contemporary context. The effort in renewing the legal reasoning (tajdīd) may be exercised in collective form by the experts in Islamic jurisprudence (fiqh) and other sciences collaboratively.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.075 | 0.050 |
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".