Are there Local Differences in Support for Violent Radicalization? A Study on College Students in the Province of Quebec, Canada
Bibliographic record
Abstract
Support for violent radicalization (VR) is a multidimensional phenomenon determined by individual, social, and contextual variables. However, how local contexts influence the configurations of risk and protective factors leading to the process of VR remains an open question. In line with a socioecological framework, this study aims to investigate local differences in support for VR and its associated risk factors (i.e., immigrant status, social adversity, depression, and collective identity) among college students in Quebec, a Canadian province with a variety of social and political contexts (i.e., Francophone Montreal, Quebec City, rural/suburban areas, and Anglophone communities). A total of 1765 college students (71% women; 73% aged between 16 and 21 years) completed an online survey. Mixed‐effects models were implemented to test local differences in support for VR and its risk factors. Results showed that the association between social adversity (i.e., discrimination and exposure to violence) and support for VR varied by local context. Specifically, social adversity was a risk factor across all contexts but Quebec City. Although prevention programs may target common determinants of support for VR, they need to be tailored according to local realities, and in particular the level of social diversity and the relative prevalence of mainstream radical discourses.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".