From Conversion to Violent Extremism: Empirical Analysis of Three Canadian Muslim Converts to Islam
Bibliographic record
Abstract
The scholarship on radicalization to violence often treats born Muslims and converts interchangeably; far too little research is focused on understanding the factors and processes driving converts in particular. This is a problem given that there is overwhelming evidence demonstrating that Muslim converts are overrepresented among Western foreign fighters. Data from Canada corroborates this larger point: converts are highly representative in attempted and successful domestic terrorist attacks. Our article explores conversion to Islam and political violence as it relates to recent trends in Canadian Jihadist militancy. We distill the theoretical literature on conversion and radicalization to seven explanatory factors, including ideology; social networks; charismatic authority; political grievances; psychology; socio-economic and criminal circumstance; and enabling environments. We then build original empirical case studies – based on expert interviews and open-sourced documents – of three Canadian converts who engaged in terrorism, including John Maguire, Michael Zehaf-Bibeau, and Damian Clairmont. Using these case studies, we contextualize, analyze, and expand our collective understanding of conversion to violence, providing lessons for theory and methodology.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.021 | 0.007 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".