Integrating Islamic Principles and Values into the Fabric of Governance
Bibliographic record
Abstract
Important Islamic principles and values in governance are identified from the Qur’an, Sunna and the essential Shariah objectives (Maqasid al-Shari’ah). The principles (vicegerency, rule of law, justice, social welfare, separation of powers, accountability, consultation, obedience, public participation, inclusiveness, equality, dignity, hisbah or enjoining the good and forbidding the evil, limiting money politics, and ensuring a free, independent media, and peace and security) have considerable bearing on the structure of government institutions and in ensuring good governance, while the values (trustworthiness, transparency, cooperation towards good, moderation, eagerness for knowledge, frugality and valuing time, and good neighbourliness) relate more to the moral qualities required by rulers, leaders and government officers alike. The proposed process of integrating these into governance is based on a systems thinking approach which acknowledges that governance works in a systemic framework. It also stresses the modern discipline of change management but points out that both it and a systems approach were already applied most successfully by Prophet Muhammad [pbuh]. Working examples, which can be adapted for use in Muslim countries for government departments and civic society have been identified from Canada and Malaysia, respectively.
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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 | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.037 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".