Norms and Values in Islamic Legal Reasoning: The Case of Listening to Music (Samāʿ)
Bibliographic record
Abstract
This essay examines the ways in which pre-modern Muslim jurists adapted their legal methods to accommodate the complexity of the act of listening to music. I classify those methods from the least to the most inclusive of underlying notions of moral value. This study shows that models on opposite ends of the spectrum function in similar ways. Whether, as in Ibn Ḥazm’s work, the scope of legal norms is confined to the immediate textual meaning, or, as in Ibn Taymiyya’s thought, the formulation of norms corresponds to an underlying moral aim, the result is a broad treatment of all phenomena that relate to music (samāʿ). By contrast, Ghazālī’s discussion of samāʿ is guided by the need to attain conviction of the appropriate course of action rather than the pursuit of an objective truth about the legal-moral status of the act of listening to music, resulting in a subtle case-by-case evaluation, rather than an overarching judgment. While this study does not attempt to give a comprehensive historical account of how and why scholars of Islamic law attempted to restrict or permit certain musical experiences, we can ultimately see how the sharīʿa, a legal system that is fundamentally concerned with moral behavior, purported to advance reasonable models for the assessment and regulation of complex social phenomena.
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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 | 0.018 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.104 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.006 | 0.007 |
| 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".