A Diagnostic and Statistical Model-5 Experimental Personality Disorder-Based Terrorism Risk/Threat Assessment Instrument
Bibliographic record
Abstract
This chapter aims to provide a review of the literature on the role of personality traits and disorders among terrorist offenders, as well as extant terrorism risk and threat assessment (TR/TA) instruments. We assert that there is an overwhelming need for an instrument that is largely based on DSM-5 personality disorder dimensions and related traits. Specifically, an assessment tool is proposed based largely on the Personality Inventory for DSM-5 (PID-5), in combination with domains borrowed from the Comprehensive Assessment of Psychopathic Personality (CAPP), as well as accounting for ideology and prior criminality. Using open sources, we discuss the prevalence of the included traits in both Omar Mateen and Dylann Roof and argue that their unstable personalities could have led investigators to downplay the risk they posed given that they break the mold of the terrorist as having a stable personality who methodically seeks to avoid detection.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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; both teacher heads agree on what is shown here.
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".