Risk and Protective Factors Associated with Violent Extremism: A Multilevel and Interdisciplinary Evidence-Based Approach
Bibliographic record
Abstract
This integrative review aims to inform research, policy and practice at the tertiary level of prevention targeting radicalised individuals, whether they have acted on their radicalisation or not. It stresses the need to respond to the terrorist threat with a multilevel and interdisciplinary evidence-based approach in order to account for the complexity of the issue. To do so, drawing from the socio-ecological model of violence, we categorise across four levels of analysis (i.e. individual, relationship, community, and societal) the risk and protective factors associated with violent extremism and reported in the existent systematic reviews and meta-analyses on this issue. As a result, we observe an overemphasis on the study of individual factors, with a few relationship factors, and no community or societal factors reported. To address this limitation, we emphasise the need for future studies to focus on risk and protective factors across the four levels of analysis. We also suggest future systematic reviews and metaanalyses to focus on qualitative data. Finally, based on the individual and relationship factors identified in the examined systematic reviews and meta-analyses, but also on the community and societal factors identified in narrative reviews, we propose a socio-ecological model of violent extremism.
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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 | 0.019 | 0.059 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.009 |
| Bibliometrics | 0.013 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 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".