WESTERN ISLAMIC SCHOOLS AS INSTITUTIONS FOR PREVENTING BEHAVIORAL RADICALIZATION: THE CASE OF QUEBEC
Bibliographic record
Abstract
Understanding radicalization in the West is more important than ever. Since the onset of the Syrian civil war, there has been increasing media and academic attention on the radicalization process of individuals and foreign Muslim fighters, leaving the comfort of their homes to join ISIS. Numerous initiatives, governmental as well as community-based, were created to combat and prevent this phenomenon. This inquiry sets out the core components to developing a critically reflective approach to Islamic schooling in the West for the purposes of preventing behavioral radicalization of Muslim youth. Extensive research on Islamic education in North America is lacking; in fact, it is scarcer in Quebec. This paper examines the role of Montreal’s Islamic schools in countering or encouraging radicalization. I seek to address two main questions: (1) Do Islamic schools advance radicalization by providing cognitive radical platforms to students? And (2) how and why do certain parents consider these schools a safe haven from the radicalization of their children? I conclude that modern Islamic schooling, at least in part or in some cases, can be regarded as itself a preventive measure to Islamic behavioral radicalization. Indeed, such schooling can help in creating balanced western Islamic identities that are functional from both western and Islamic worldviews.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".