Bibliographic record
Abstract
A judge is a pillar of our entire justice system. He is supposed to be a righteous, virtuous, pious, honest, and unbiased person who led a blameless life. Judges must endeavor for the utmost standards of integrity in both their professional and personal lives. He should be knowledgeable about the law (fiqh, Sharī’ah), probing, willing to undertake in-depth legal research, and able to write decisions that are clear, logical and cogent. In Islamic law, a Qazi is comparable to a magistrate or judge in contemporary Western judicial systems. As well as adjudicating disputes, the qazi performs extrajudicial functions, including mediation and managing public works. Within customary justice systems, the Qāzis and religious leaders at large — imams, muftis and the like — occupy a special place. Given their social status, and their knowledge of the Qur’an, they are perceived as the key institution for the ‘preservation of Muslim law’. This study will explore the qualities that an ideal judge (Qāzī) should embody, and the required skills in decision making, conflict resolution and problem solving in the light of Islamic teachings. This study will also point-out the barriers to achieving and exercising those skills and identify number of ways of for judges to be manifested as good judge.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.975 | 0.968 |
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".