RADICALISM LEADING TO VIOLENT EXTREMISM IN CANADA: A MULTI-LEVEL ANALYSIS OF MUSLIM COMMUNITY AND UNIVERSITY BASED STUDENT LEADERS’ PERCEPTIONS AND EXPERIENCES
Bibliographic record
Abstract
Recently, more than 150 Canadians have joined the Islamic State (IS) in the Middle East, causing alarm among both Canadian policy makers and the general citizenry. One of the most salient questions related to this development pertains to the factors that drove these young people to extremism. Most experts posit that extremism is caused by multiple factors embedded in our social, economic, geopolitical, and cultural processes. However, evidence to support such claims is still poor as limited primary research has been done in Canada to understand, explain, and identify radicalism’s causes, its main drivers, and its global-local linkages. Due to their emphasis on policy and federal law enforcement, studies of radicalism in Canada have proven inadequate in outlining a comprehensive understanding of the psycho-social conditions that might be associated with the radicalization process. Considering the above gaps, this research draws upon three studies in order to map the perceptions of the leadership of Islamic community-based organizations and university-based student organizations with regards to issues related to social conflict, terrorism, and counter-terrorism in Canada. The objective of this study is to both document the existence of radicalism and to determine the role of critical social issues that may potentially contribute to this phenomenon. In addition, this paper will hopefully elaborate on the results of a multi-level (macro, meso, and micro) analysis of the social factors which act as key drivers of radicalism through the use of qualitative methods and the aid of social conflict and social-psychological theoretical lenses. Finally, the study concludes by exploring the Canadian national counter-terrorism (CT) strategy’s effectiveness in countering radicalization.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".