Risk factors for terrorism: a comparison of family, childhood, and personality risk factors among Iraqi terrorists, murderers, and controls
Bibliographic record
Abstract
Terrorism represents a major threat to global security; however, psychosocial risk factors for terrorism are insufficiently explored in the literature. Using a cross-sectional design, we examined group differences in family, childhood, and personality factors, and attitudes towards terrorism among individuals convicted of terrorism (n = 160); those convicted of murder (n = 65); and a control group (n = 88). Using regression models, we consequently analyzed the risk factors for group membership, with a focus on terrorism. Compared to controls, terrorists had higher odds of persistent childhood disobedience, a conduct disorder factor, and endorsing statements on the causes of and justifications for terrorism, but lower odds of harsh treatment as a child. Murderers had greater odds of antisocial personality disorder (ASPD), of endorsing statements on the causes of and justifications for terrorism, but lower odds of being easily provoked and harsh treatment before age 15. Compared to murderers, terrorists had higher odds of endorsing statements on the causes of and justifications for terrorism, but lower odds of ASPD, having a family member murdered, and being easily provoked. Although psychosocial risk factors for terrorism overlap significantly with violent criminal behaviors, certain factors may help distinguish terrorists from other groups. These factors merit further investigation.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".