Reviewing the links between violent extremism and personality, personality disorders, and psychopathy
Bibliographic record
Abstract
Many early published analyses of the terrorist placed psychopathy as the core explanatory variable for terrorist behaviour. This speculative opinion was derived mainly from popular culture, and the desire to attribute mental disorders to those committing such violent acts. Poor research designs and a lack of empiricism ultimately undermined these arguments in favour of terrorism being rooted in disorders of personality. Multiple studies supporting psychopathic and personality-level explanations were conducted in the absence of rigorous clinical diagnostic procedures. Despite the methodological issues, concluding remarks from this research continues to hold instinctive appeal across the research field. This incentivises a need for a rigorous synthesis of the evidence base. The objective of this systematic review is to assess the impact of personality upon attitudes, intentions, and behaviours in the context of radicalisation and terrorism. This paper follows the same systematic process as the Gill et al. paper in this special issue. However, we use the model to interrogate the existing empirical literature on personality and terrorism in terms of its coverage, common themes, methodological strengths and weaknesses and implications. The search strategy for the systematic review is based on the Campbell Collaboration method. Results and their implications are discussed.
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 | 0.010 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.014 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".