Youth Resilience to Violent Extremism: Development and Validation of the BRAVE Measure
Bibliographic record
Abstract
Building resilience to violent extremism has featured in preventing violent extremism efforts for over a decade. Validated and standardized cross-cultural measures can help identify protective capacities and vulnerabilities toward violent extremism for young people. Because drivers for violent extremism are multi-factorial, a measure of resilience cannot be used to predict who will and will not commit acts of terror. Instead, its purpose is to track the multiple forms of capital available to youth at risk of adopting violence to resolve ideological, religious and political grievances, and to use this data to inform interventions that increase young people’s capacity to resist violent extremism’s push and pull forces. In this study, we developed such a measure, using data from 200 Australian and 275 Canadian participants aged eighteen to thirty years old. Following exploratory and confirmatory factor analysis, a fourteen-item measure emerged consisting of five factors: cultural identity and connectedness; bridging capital; linking capital; violence-related behaviors, and violence-related beliefs. The Building Resilience against Violent Extremism (BRAVE) measure was found to have good internal reliability (α = .76), correlating in expected directions with related measures. The BRAVE shows promise for helping understand young people’s resilience to 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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".