Bibliographic record
Abstract
This chapter introduces a new approach to the risk assessment for violent extremism that is focused on cyber-related behaviour and content. The Violent Extremist Risk Assessment (VERA-2) protocol, used internationally, is augmented by an optional cyber-focused risk indicator protocol referred to as CYBERA. The risk indicators of CYBERA are elaborated and the application of CYBERA, conjointly with the VERA-2 risk assessment protocol, is described. The combined use of the two tools provides (1) a robust and cyber-focused risk assessment intended to provide early warning indicators of violent extremist action, (2) provides consistency and reliability in risk and threat assessments, (3) determines risk trajectories of individuals, and (4) assists intelligence and law enforcement analysts in their national security investigations. The tools are also relevant for use by psychologists, psychiatrists, communication analysts and provide relevant information that supports Terrorism Prevention Programs (TPP) and countering violent extremism (CVE) initiatives.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".