Citizens, Extremists, Terrorists: Comparing Radicalized Individuals with the General Population
Bibliographic record
Abstract
Empirical research on terrorism has tended to overlook the heterogeneity of the radicalized population, and how, in its heterogeneity, it differs from the general population. This study first asks how radicalized individuals, irrespective of the activities they participated in during their trajectory, differ from the general population. It then divides radicalized individuals into those who use terrorist violence, and those who do not, asking whether the aforementioned distinctions present differently. Using the (Non-) Involvement in Terrorist Violence (NITV) dataset, variables for which general-population comparisons are feasible are presented and contextualized. Compared to the general population, radicalized individuals are disproportionately male, tend to lack perceived political representation, are more likely to be unemployed, have suffered adverse childhood experiences, and have communicated a desire to hurt others. They are also more likely to have violent criminal antecedents. Although radicalized individuals are no more likely to suffer from mental illness than the general population, radicalized individuals who are so afflicted tend to suffer several specific illnesses at slightly above-average rates. If efforts to prevent citizens from becoming extremists, and extremists from turning to terrorist violence, incorporate specific, rather than general, interventions, it is likely that they will produce more robust results.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".