From social adversity to sympathy for violent radicalization: the role of depression, religiosity and social support
Bibliographic record
Abstract
BACKGROUND: Discrepancies among studies suggest that the relation between social adversity and sympathy for violent radicalization (SVR) is multifaceted and may differ according to social context. This paper examines the role of depression, religiosity and social support in the relation between social adversity (i.e., discrimination and exposure to violence) and SVR among college students in Quebec, Canada. METHODS: A total of 1894 students responded to an online questionnaire posted on the internet of eight colleges. Multilevel analyses were first conducted to account for the clustered nature of the data, followed by mediation and moderation analyses. RESULTS: First generation migrants reported less SVR than second generation youth and non-immigrants. The mediating and/or moderating role of depression, religiosity and social support was examined through causal inference models. Depression mediated the relation between social adversity and SVR, with depression scores accounting for 47% and 25% of the total effect between discrimination and exposure to violence and SVR scores, respectively. Religiosity and social support moderated the association between social adversity and SVR. CONCLUSIONS: These results suggest that prevention programs should consider violent radicalization as a systemic issue which involves both minorities and the majority, although the specific balance between risk and protective factors may be influenced by local dynamics. They also question intervention measures targeting specifically migrants or ethno-cultural communities because of the risk of increasing profiling and stigmatization. Prevention programs should prioritize decreasing discrimination in colleges, as well as the provision of psychosocial support to depressed youth who experience social adversity.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".