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Record W4380484469 · doi:10.3390/rel14060780

Norms and Values in Islamic Legal Reasoning: The Case of Listening to Music (Samāʿ)

2023· article· en· W4380484469 on OpenAlexafffund
Omar Farahat

Bibliographic record

VenueReligions · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicIslamic Studies and History
Canadian institutionsMcGill University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsActive listeningConvictionEpistemologyMeaning (existential)Action (physics)IslamFunction (biology)PsychologyValue (mathematics)SociologyLawPolitical sciencePhilosophyComputer science

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.589
Threshold uncertainty score0.963

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.022
GPT teacher head0.299
Teacher spread0.277 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2023
Admission routes2
Has abstractyes

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