Bibliographic record
Abstract
ThThe Quran and the Sunnah prescribe death punishment for various offences, such as qatl-e-‘amd, apostasy (including blasphemy by a Muslim), ḥirābah when it involves qatl and zina by a muhsan. Moreover, there are instances of death punishment under the doctrine of fasād fi ‘l-ard for habitual offenders or when the offence was committed in a brutal and shocking manner. Muslim jurists divide these various instances of death punishment into three categories on the basis of the applicable legal principles. They are: Qisās, hudud and ta‘zīr (also called siyāsah). Although qiṣāṣ and hudud have some differences in legal consequences, yet they also have a few common legal consequences, such as the strict standard of proof, the special relaxations given to the accused (and even to the convict) and the immutable nature of the punishment. As opposed to qiṣāṣ and hudud, the matters related to ta‘zīr (or siyāsah) punishment have been left to the Muslim ruler who can prescribe details keeping in view the objectives, and within the constraints of the general principles, of Islamic law. Among these various consequences, the present paper focuses on the mode of execution of death punishment only.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.987 | 0.987 |
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".