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Record W4390469620 · doi:10.18326/ijims.v13i2.427-456

Sectarian tensions, islamophobia, and decolonization: comparing Jasser Auda’s and Jonathan Brown’s analysis of the hadiths concerning Aisha’s marital age

2023· article· en· W4390469620 on OpenAlexfundno aff
Muhamad Rofiq Muzakkir, Siti Sarah Muwahidah, Royan Utsany, Rohmansyah Rohmansyah

Bibliographic record

VenueIndonesian Journal of Islam and Muslim Societies · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicIslamic Studies and History
Canadian institutionsnot available
FundersauDA FoundationQueen's UniversityMcGill University
KeywordsPoliticsPhilosophyEpistemologySociologyIslamophobiaPolitical scienceLaw

Abstract

fetched live from OpenAlex

The ongoing debate surrounding the hadiths on Aisha’s age of marriage has givenrise to two main positions among Muslim scholars, namely: the rejectors, thosewho reject the hadiths’ validity and propose the view that Aisha got married atan older age, and the defenders, those who defend them as valid hadiths andaccept that Aisha consummated her marriage at the early age of nine years old. Inthis study, we examine this issue through the opposing arguments offered by twocontemporary Muslim scholars: Jasser Auda, who represents the view of hadithrejectors, and Jonathan Brown, who represents those who accept the validity of thehadiths. These two scholars have been chosen to represent these two standpointsmainly because of their novel and distinctive theoretical contributions to theongoing debate. Entangled in this debate is the issue of whether pre-modernreality can be assessed by using modern norms. We employ critical analysis onthe epistemological and methodological aspects surrounding the two scholars’interpretations of the hadiths of Aisha’s age of marriage. We argue that threesignificant features distinguish Auda and Brown’s dispositions. These are: first,their different conceptions of the interplay between politics, knowledge, andmemory; second, their differing epistemological approaches to hadith science;and third, their opposing assumptions about the universality of modern norms.

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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.427
Threshold uncertainty score0.891

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
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 head0.029
GPT teacher head0.267
Teacher spread0.238 · 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 designObservational
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

Citations2
Published2023
Admission routes1
Has abstractyes

Explore more

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