Calibrating risk for violent political extremists and terrorists: the VERA 2 structured assessment
Bibliographic record
Abstract
Purpose The purpose of this paper is to outline the process of risk assessment for terrorists and violent political extremists and to present an example of such an approach. The approach proposed is referred to as the VERA 2 or violent extremism risk assessment protocol (Consultative Version 2). Design/methodology/approach A review of the knowledge base relating to risk assessment and risk assessment methodology was undertaken with a focus on relevance to individual terrorists and violent extremists. The need for a specific approach for the risk assessment of terrorists that differs from approaches used for ordinary violent criminals was identified. A model that could be used for the risk assessment of terrorists was identified with pertinent risk indicators. This was structured into a protocol referred to as the VERA (Consultative Version 2). The approach is intended to be applied to different types of violent extremists, terrorists and unlawful violent offenders motivated by religious, political or social ideologies. Findings First, risk assessments of adjudicated terrorists and violent extremists should be undertaken with risk indicators that are relevant to ideological motivated violence. Indicators used for ordinary common violence differ in substantive ways from those relevant to terrorists and therefore may have questionable relevance for the assessment of risk in terrorists. Second, it is possible to construct an evidence‐based risk assessment approach for the range of violent extremists and terrorists using a structured professional judgment approach with pertinent risk indicators. The VERA 2 is an example of this type of risk assessment protocol for terrorists and unlawful violent extremists. Research limitations/implications Risk assessment tools that have been developed for ordinary violent criminals and members of organised criminal gangs should be used with caution with terrorists, violent extremists and other perpetrators of ideologically motivated unlawful violence. Specific risk assessment approaches for terrorists with relevant indicators should be used. At this time, terrorist oriented approaches such as the VERA 2 are to be considered consultative and used as an add‐on to other established approaches. Originality/value There are few transparent, structured risk assessment approaches that use indicators specifically relevant to violent political extremists and terrorists. One new approach, the VERA 2 is outlined in the paper using risk indicators that differ in substantive ways from those used for other ordinary violent criminals.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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; 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".