Support for violent extremism is not on a continuum: identification of subgroups that justify violence differently
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Violent radicalization (VR) within a community has typically been measured as a continuous variable; however, the concept may be better understood as consisting of discrete groups that justify violence of different types and under different circumstances. This paper explores this question through person-centered analyses of 13 items drawn from two commonly-used measures of violent radicalization (the Activism and Radicalism Intention Scale by Moskalenko and McCauley and the Sympathies for Violent Radicalization scale by Bhui et al.). We conducted latent class analyses across items from both the RIS and SyfoR within two general population samples from the U.S. (Dataset 1, n = 1042; Dataset 2, n = 999). As hypothesized, distinct classes were identifiable, and these classes were largely the same within the two distinct datasets. Findings suggest that person-centered analysis may be a highly meaningful approach to understanding violent radicalization, and that prevention and intervention programs may benefit from understanding these different groups.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it