Visualizing the relationship among indicators for lone actor terrorist attacks: Multidimensional scaling and the TRAP‐18
Bibliographic record
Abstract
This validation study analyses data from a sample of North American terrorist attackers (n = 33) and non-attackers (n = 23) through the lens of the Terrorist Radicalization Assessment Protocol (TRAP-18; Meloy, 2017) utilizing a multivariate statistical approach - multidimensional scaling - to visualize potential clustering (co-occurrence) of risk factors. Rarely done in terrorism research, the results plotted in two-dimensional space show the clustering and co-occurrence of most of the eight proximal warning behaviors among the attackers, but not among the non-attackers, and less of a clustering and association of distal characteristics, but their presence in both attackers and non-attackers. These findings provide further empirical support for the rational-theoretical model of the TRAP-18, a structured professional judgment instrument for threat assessment of lone actor terrorists. It advances the quantitative analysis of operationally relevant and behaviorally observable indicators for use by law enforcement and counterterrorism professionals and their consultants. Findings are discussed in relation to other research on pre-offense behaviors of lone actor terrorists, and recommendations are made for both operational use and further research.
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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 | 0.008 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".