Understanding (non)involvement in terrorist violence: What sets extremists who use terrorist violence apart from those who do not?
Bibliographic record
Abstract
Abstract Research summary We compare European and North American radicalization trajectories that led to involvement in terrorist violence ( n = 103) with those for which this outcome did not occur ( n = 103). Regression analyses illustrate how involvement in terrorist violence is determined not only by the presence of risk, but also the absence of protective factors. Bivariate analyses highlight the importance of considering the temporality of these factors; i.e., whether they are present before or after radicalization onset. The most salient risk factors identified were alignment with a group or movement with an exclusively violent strategic logic, and access to weapons. In terms of protective factors, parenting children during radicalization, self‐control, and participation in extremist groups with a strategic logic that was not exclusively focused on violent means were all associated with noninvolvement in terrorist violence. Policy implications Different patterns of risk and protective factors influence whether radicalization will, or will not, lead to involvement in terrorist violence. One‐size‐fits‐all radicalization‐prevention efforts may therefore be less effective than programs tailored to address a particular outcome. Even when terrorist violence is prevented, the targeted individual is likely to remain radicalized. Preventative efforts must carefully assess whether the measures used to avert terrorist violence in the short‐term risk contributing to a longer term societal threat. The efficacy of preventative efforts depends in part on when they are deployed, that is, before or after radicalization onset.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.011 |
| 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 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".