Violence risk assessment of Sovereign Citizens: An exploratory examination of the HCR‐20 Version 3 and the TRAP‐18
Bibliographic record
Abstract
Abstract Sovereign Citizens comprise an understudied right‐wing extremist movement in the United States who have grown in notoriety in recent years due to several high‐profile instances of violence. Despite this, little empirical research has been conducted on Sovereign Citizens, including research on assessing their risk for violence. In this study, we sought to replicate and extend a prior study on Sovereign Citizen violence. Using open‐source data, we added several new cases to a pre‐existing dataset of violent and non‐violent Sovereign Citizen incidents, yielding a total sample of 107 cases, 69 of which were scored using the HCR‐20V3, and 83 of which were scored using the TRAP‐18. Our findings indicated that higher scores on both instruments were significantly associated with greater odds of cases being violent. We also observed that several risk factors occurred with significantly more frequency among violent cases than non‐violent ones. Implications for future research and professional practice 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 | 0.009 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".