Bibliographic record
Abstract
Since, the inception of Islamic financial institutions, researchers have drawn attention to the topic of corporate governance in the context of Sharīʻah (Islamic Law). Owing to nascent in nature, the importance of governance is manifold for such institutions. Sharīʻah is not limited to religious rituals but it also deals with politics, social issues, economics, banking, contractual law and routine matters of one’s life. Therefore, whether the word governance is alien in Sharīʻah, or is it deeply rooted? We tried to address the question, to profoundly mine the classical and contemporary literature on conventional and Islamic mechanism of corporate governance. The concept of governance is deeply rooted in Islamic law. General principles of governance are found in the Quran, Ḥadīth, and in classical literature of fiqh. In the Quran, it is ordained to fulfill the obligations, deliver the trust, do justice, testify truth, and not to eat others’ property unjustly. In same lines, Prophet Muhammad ﷺ asked Muslims to show honesty, do not take things unlawfully, and everyone is responsible for his guardianship. Moreover, the concept of Shūra, mutual cooperation, Ḥisbah for accountability, Maqāsid-e-Sharīʻah, and legal maxims of Sharīʻah also provide guidelines to decision-makers for the protection of the rights of stakeholders. Hence, the concept of governance is not alien in Islam rather it is deeply embedded. This study might provide some comprehension to control, organize, and direct economic activities in the context of principles of governance in Sharīʻah.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.979 | 0.981 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".