Analysing person-exposure patterns in lone-actor terrorism: Implications for threat assessment and intelligence gathering
Bibliographic record
Abstract
The lone-actor terrorist population can be extremely heterogenous and difficult to detect. Intelligence is key to countering this threat. This study devises a typology of person-environment interactions which could serve as a framework for intelligence-gathering and risk assessment. We use cluster analysis and a previously-developed Risk Analysis Framework (RAF) to identify relations between three components: propensity, situation and network. The analysis reveals four person-exposure patterns (PEPs): solitary, susceptible, situational and selection. The solitary PEP lacks common indicators of a propensity to pursue terrorist action. What indicators are present may not manifest until late in the offending process. The susceptible PEP suggests a style of interaction whereby cognitive susceptibility, manifesting as mental illness, is a key factor in the emergence of the propensity/motivation to commit a terrorist attack. This configuration typifies cases where radicalisation may occur in a short time span. The situational PEP demonstrates how situational stressors may act as warnings of acceleration towards violent action; the challenge being to capture evidence of these stressors and their effects. Lastly, the selection PEP demonstrates higher frequencies of leakage and antecedent violent behaviours. These offenders may be known to the community or other agencies, suggesting specific opportunities for detection and disruption.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".