Adverse childhood experiences, education, and involvement in terrorist violence: Examining mediation and moderation
Bibliographic record
Abstract
Most perpetrators of terrorist violence have had some level of post-secondary school education, with many enrolled in education at the time of their attacks. Exploring this premise in the context of prevention, this article draws from data gathered on a purposive sample (N = 206) of radicalized individuals from Europe and North America, half of whom became involved in terrorist violence at the end of their radicalization trajectories. Through a lens of educational participation, we propose novel, non-linear frameworks for understanding radicalization outcomes. To do so, two factors are explored that uniquely intersect when an individual enters a school setting: Adverse childhood experiences (ACE) and the human capital provided by education. As hypothesized, exposure to ACE was found to be associated with radicalization trajectories culminating in terrorist violence at the bivariate level (OR = 2.08). Consistent with the developmental-assets framework, it was further hypothesized that this relationship would be mediated by enrollment in education; however, results did not support this hypothesis. Instead, consistent with resiliency-based models, it was found that the relationship between ACE and involvement in terrorist violence was significant for those who abandoned education during radicalization (OR = 2.07). As well as contributing to theoretical models of radicalization to terrorist violence, identifying the furtherance of education as a moderator of risk may signal an important preventative strategy for violent extremism. Keeping enrolled students engaged in their programs, even if only nominally, may also forego the need for educators to engage in potentially controversial practices such as alerting the authorities to students who display signs of radicalization.
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 | 0.007 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".