Breaking the Silence: An Islamic Legal Approach to Facilitating Reporting and Testimony by Muslim Victims and Witnesses of Sexual Crimes
Bibliographic record
Abstract
Many Muslims hold the doctrine of the legal schools (madhāhib) in high esteem. As such, the schools’ approaches to rape and sexual assault may impact the behaviour of Muslim victims and witnesses. Through an examination of the legal rules that regulate rape and sexual assault in fiqh and fatwā works associated with the four Sunni schools and Ibn Ḥazm, I aim to determine whether the relevant rules may interfere with the willingness of Muslim victims and witnesses to report or testify to sexual crimes. I argue that although the jurists’ prosecution of sexual assault as a discretionary offence (taʿzīr) is compatible with reporting and testimony, their prosecution of rape as coerced illicit intercourse (zinā), usurpation (ghaṣb), or banditry (ḥirāba) silences victims and witnesses. Further, rules related to financial compensation do not encourage victims to come forward. Reclassifying rape as a discretionary offence would better promote reporting and testimony by victims and witnesses. The central role of ijtihād in creating the historical rules on rape, the jurists’ intent behind those rules, and modern knowledge regarding the reality of rape support this reclassification. Fully resolving issues related to civil compensation is difficult without broader reforms of Islamic tort law.
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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".