Criminal justice interventions for preventing radicalisation, violent extremism and terrorism: An evidence and gap map
Bibliographic record
Abstract
Background: Criminal justice agencies are well positioned to help prevent the radicalisation of individuals and groups, stop those radicalised from engaging in violence, and reduce the likelihood of terrorist attacks. This Evidence and Gap Map (EGM) presents the existing evidence and gaps in the evaluation research. Objectives: To identify the existing evidence that considers the effectiveness of criminal justice interventions in preventing radicalisation, violent extremism and terrorism. Search Methods: We conducted a comprehensive search of the academic and grey literature to locate relevant studies for the EGM. Our search locations included the Global Policing Database (GPD), eight electronic platforms encompassing over 20 academic databases, five trial registries and over 30 government and non-government websites. The systematic search was carried out between 8 June 2022 and 1 August 2022. Selection Criteria: We captured criminal justice interventions published between January 2002 and December 2021 that aimed to prevent radicalisation, violent extremism, and/or terrorism. Criminal justice agencies were broadly defined to include police, courts, and corrections (both custodial and community). Eligible populations included criminal justice practitioners, places, communities or family members, victims, or individuals/groups who are radicalised or at risk of becoming radicalised. Our map includes systematic reviews, randomised controlled trials, and strong quasi-experimental studies. We placed no limits on study outcomes, language, or geographic location. Data Collection and Analysis: Our screening approach differed slightly for the different sources, but all documents were assessed in the systematic review software program DistillerSR on the same final eligibility criteria. Once included, we extracted information from studies using a standardised form that allowed us to collect key data for our EGM. Eligible systematic reviews were assessed for risk of bias using the AMSTAR 2 critical appraisal tool. Main Results: = 50). These measures were thematically grouped under nine broad categories including (1) terrorism, (2) extremism or radicalisation, (3) non-terror related crime and recidivism, (4) citizen perceptions/intentions toward the criminal justice system and government, (5) psychosocial, (6) criminal justice practitioner behaviours/attitudes/beliefs, (7) racially targeted criminal justice practices, (8) investigation efficacy, and (9) organisational factors. The most commonly assessed outcomes included measures of terrorism, investigation efficacy, and organisational factors. Very limited research assessed intervention effectiveness against measures of extremism and/or radicalisation. Authors’ Conclusions: Conducting high-quality evaluation research on rare and hidden problems presents a challenge for criminal justice research. The map reveals a number of significant gaps in studies evaluating criminal justice responses to terrorism and radicalisation. We conclude that future research should focus attention on studies that consolidate sound measurement of terrorism-related outcomes to better capture the potential benefits and harms of counter-terrorism programs, policies and practices which involve criminal justice agencies.
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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 | 0.061 | 0.175 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.030 | 0.025 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".