‘That’s how Muslims are required to view the world’: Race, culture and belief in non-Muslims’ descriptions of Islam and science
Bibliographic record
Abstract
Islam’s positioning in relation to Western ideals of individuality, freedom, women’s rights and democracy has been an abiding theme of sociological analysis and cultural criticism, especially since September 11 2001. Less attention has been paid, however, to another concept that has been central to the image of Western modernity: science. This article analyses comments about Islam gathered over the course of 117 interviews and 13 focus groups with non-Muslim members of the public and scientists in the UK and Canada on the theme of the relationship between science and religion. The article shows how participants’ accounts of Islam and science contrasted starkly with their accounts of other religious traditions, with a notable minority of predominantly non-religious interviewees describing Islam as uniquely, and uniformly, hostile to science and rational thought. It highlights how such descriptions of Islam were used to justify the cultural othering of Muslims in the West and anxieties about educational segregation, demographic ‘colonization’ and Islamist extremism. Using these data, the article argues for: (1) wider recognition of how popular understandings of science remain bound up with conceptions of Western cultural superiority; and (2) greater attentiveness to how prejudices concerning Islamic beliefs help make respectable the idea that Muslims pose a threat to the West.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".