Reprint of ‘The Myth of a Normal Muslim: ‘Aql, Taklīf, and New Islamic Approaches to Neurodivergence’
Bibliographic record
Abstract
This paper explores the intersection of Islamic concepts, such as ‘aql (intellect) and taklīf (legal responsibility), with the experiences of neurodivergent individuals, particularly those with autism. It argues for a re-evaluation of traditional Islamic understandings of disability to incorporate modern perspectives on neurodiversity. The research highlights that high-functioning autistic individuals possess the capacity to engage with religious obligations and that Islamic practices can be adapted to accommodate their unique needs. By advocating for an inclusive approach, the paper calls for a dialogue between classical Islamic jurisprudence and contemporary insights into neurodiversity, aiming to foster a supportive environment for neurodivergent Muslims within their communities. Ultimately, it seeks to provide a framework for understanding neurodiversity through an Islamic lens, promoting both spiritual engagement and social inclusion.Editorial Notes:A publisher’s error resulted in this article appearing in the wrong issue. The article is reprinted here for the reader’s convenience and for the continuity of this issue. This article is a reprint of a previously published article. For citation purposes, please use the original publication details; Choudhury , N. U.-A. (2024). P. 43-67
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".