Racialization and the construction of the problem of the Muslim presence in Western societies
Bibliographic record
Abstract
This chapter considers political Islam as a transnational concern, as a shared political issue in Europe and North America and a multilevel source of anxiety that has unleashed an intense public debate, moral panic, and security concerns about Muslims since 9/11. Our main contention in this chapter is to show how racialization became the central axis for the politicization of Islam and Muslims by non-Muslim majorities in many sectors of social and public life. Drawing on the comparison of countries, with national specificities always defining the relationship between church (religion) and state, and different models of incorporation of ethnocultural diversity, we propose that the construction of the problem of the Muslim presence in Western societies shares common traits. The first section briefly describes the context in which narratives about Muslims and Islam have developed in the European Union, and in Canada and the United States. Secondly, we highlight how the transnationalization of feelings of insecurity is founded on the racialization of Muslims in non-Muslim contexts. Finally, the third section examines the “moral panic” surrounding the fear of radicalization. The domestic pursuit of the “war on terror” based on counterterrorism policies “combatting religious radicalization” tends to presuppose that Islamic religiosity is a threat to peaceful cohabitation. In this context, the fetishization of Islamic religious signs and motives by the secular gaze is at the core of the controversies reviewed in this chapter and of the “racialized surveillance” of Muslim minorities in Western societies since 9/11.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.035 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".