The development of an evidence base for assessing and managing the risk of terrorism in the UK
Bibliographic record
Abstract
This thesis tracks my learning journey from a dedicated three-year project developing a professional risk assessment methodology for terrorist offenders in HMPPS, through advising Prevent, to academia where I have continued to contribute to a growing evidence base for understanding the psychology of terrorism. As a co-I for CREST, I compiled a Directory of extremism risk assessment frameworks and networked with subject matter experts in the USA, Canada and Australia. This led to invitations to contribute chapters to the second edition of the International Handbook for Threat Assessment, and to the NATO Science for Peace and Security Programme on Terrorism Risk Assessment Instruments. As an expert member of the European Radicalisation Awareness Network I contributed two papers on countering extremism, one advising on ethical practice for mental health practitioners and the other on the increasing challenge of detecting lone actor terrorists pre-crime. The as yet unpublished paper is informed largely by my work as an expert witness for those charged under counterterrorist legislation and completes my learning journey to date. It exposes a gap between legal and psychological approaches to assessing terrorist risk with the potential for miscarriages of justice where national security is prioritised over individual rights.
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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 | 0.091 | 0.350 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.017 | 0.008 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".