Bibliographic record
Abstract
Violent extremism has galvanized public fear and attention. Driven by their concerns, the public has pushed for law enforcement and mental health systems to prevent attacks rather than just respond to them after they occur. The prevention process requires guidance for practitioners and policymakers on how best to identify people who may be at risk, to understand and assess the nature and function of the harm they may cause, and to manage them to mitigate or prevent harm. Violent Extremism provides such guidance. Over 10 chapters, prepared by leading experts, this handbook illuminates the nature of violent extremism and the evolution of prevention-driven practice. Authors draw on the literature and their experience to explain which factors might increase (risk factors) or decrease (protective factors) risk, how those factors might operate, and how practitioners can prepare risk formulations and scenario plans that inform risk management strategies to prevent violent extremist harm. Each chapter is crafted to support thoughtful, evidence-based practice that is transparent, accountable and ultimately defensible. Written for an international audience, the volume will be of interest to law enforcement and mental health professionals, criminal justice and security personnel, as well as criminologists, policymakers and researchers. Praise for Violent Extremism ‘In Violent Extremism, Logan, Borum and Gill have assembled the most celebrated scholars and practitioners in anticipating and mitigating violence. This extraordinary accomplishment could transform the future of risk assessment.’ John Monahan, University of Virginia ‘Scholarly, scientific and so very practical, this is the book we have been waiting for. It should be read, and re-read, by every practitioner and researcher working on violent extremism.’ John Horgan, Georgia State University ‘Since the early 2000s, the field of assessing violent extremism risk has developed apace. This landmark text authoritatively takes stock of past and current theory, research and practice, and provides a coherent vision for the future.’ Christopher Dean, Cardiff Metropolitan University
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.055 | 0.015 |
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".