Radicalization Leading to Violence: A Test of the 3N Model
Bibliographic record
Abstract
The present research examines the social cognitive processes underlying ideologically-based violence through the lens of the 3N model of radicalization. To test this theory, we introduce two new psychometric instruments-a social alienation and a support for political violence scale-developed in collaboration with 13 subject matter experts on terrorism. Using these instruments, we test the theory's hypotheses in four different cultural settings. In Study 1, Canadians reporting high levels of social alienation (Need) expressed greater support for political violence (Narrative), which in turn positively predicted wanting to join a radical group (Network), controlling for other measures related to political violence. Study 2a and 2b replicated these findings in Pakistan and in Spain, respectively. Using an experimental manipulation of social alienation, Study 3 extended these findings with an American sample and demonstrated that moral justification is one of the psychological mechanisms linking social alienation to supporting political violence. Implications and future directions for the psychology of terrorism are discussed.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".