An analysis of terrorist attack perpetrators in England and Wales: Comparing lone actors, lone dyads, and group actors.
Bibliographic record
Abstract
Three types of terrorist attackers, sentenced between 1983 and 2021, were compared using a sample of 143 individuals convicted of extremist offenses in England and Wales. Attackers were classified as either lone actors, lone dyads, or group actors, and these groups were compared in relation to sociodemographics, ideological affiliation, mental health status, online activities, plot characteristics, and assessments of risk. Data were obtained from coding the content of specialist risk assessment reports. Key findings include that lone actors and lone dyads were significantly more likely to present with mental health issues than group actors. Attackers affiliated with the extreme right wing were more likely to commit attacks alone or in pairs, in contrast to Islamist extremists who were more likely to attack as a group. In terms of trends over time, lone-actor attacks have become increasingly prominent, while the opposite is true for group attacks. The internet was also found to play an important role in radicalization pathways and attack preparation for lone actors and lone dyads, but a lesser role for group-based attackers. No differences were found between attacker groups in assessments of risk by professionals. Gaining an increased understanding of those assuming attacker roles can help guide counterterrorism approaches and future policy.
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How this classification was reachedexpand
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".