Pathway to terrorist behaviors: The role of childhood experiences, personality traits, and ideological motivations in a sample of Iraqi prisoners
Bibliographic record
Abstract
Radicalization to terrorism is a multifaceted process with no single theory or approach to explain it. Although research has focused on understanding the process, there is still a dearth of studies that examine an empirically driven pathway to terrorism behavior. This study examines a cross-sectional sample of incarcerated men convicted of terrorism in Iraq (N = 160). A questionnaire-guided interview included adverse childhood experiences (ACEs), conduct disorder (CD), antisocial personality disorder (ASPD), religious and political ideology, views about causes of terrorism, and the severity of terrorist acts. Path analysis was employed to examine the relationships between these factors and to identify the model with the best fit. After adjusting for age, employment, and location, results indicated that ACEs positively impacted CD, ASPD, religious guidance, and terrorism attitudes. ASPD positively affected political commitment and terrorism attitudes, but inversely affected current religious commitment. Political commitment inversely influenced terrorism attitudes. Religious commitment positively influenced the prioritization of religion in life, which subsequently impacted terrorism attitudes and behavior severity. Additionally, attitudes toward terrorism directly affected the severity of terrorism behavior. All paths in the final model were statistically significant at p < 0.05. Although these findings may be limited in generalizability due to the unique sample, results support the complex and interdependent nature of childhood and adult experiences on the development of both terrorism attitudes and the severity of terrorism behavior.
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".