Re-Assessing the Evidentiary Threshold for <i>Zinā’</i> in Islamic Criminal Law: A <i>De Facto</i> Exemption Proposal
Bibliographic record
Abstract
Abstract This article considers the four eyewitness threshold for zinā’ in Islamic criminal law. In some Muslim-majority countries where zinā’ remains an offence, judiciaries have by-passed the threshold by accepting singular confessions from male fornicators or, otherwise, inferring fornication from pregnancy outside of marriage. As a result, a disproportionate number of women have been prosecuted, convicted, and even punished for zinā’ . I assert that the four-eyewitness threshold allows for an alternative way to view zinā’ that can result in a different set of consequences. If the threshold is taken seriously such that it becomes the only evidentiary basis upon which a zinā’ conviction can be entered, it will create an effective or de facto exemption where alleged perpetrators can never be convicted, except in the rarest cases where four independent eyewitnesses can be corralled. If adopted, this approach would provide a principled basis to reject opportunistic confessions that deflect punishment to accused female fornicators. And as an ‘internal’ solution that arises within the framework of the sharī’a , a de facto exemption approach is more likely to be perceived as legitimate when compared with proposed solutions that find their basis in international human rights legal instruments.
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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 | 0.033 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.004 | 0.017 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.010 | 0.016 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".