Criminal justice interventions for preventing radicalisation, violent extremism and terrorism: An evidence and gap map
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,061 | 0,175 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,002 |
| Méta-épidémiologie (sens large) | 0,008 | 0,006 |
| Bibliométrie | 0,030 | 0,025 |
| Études des sciences et des technologies | 0,002 | 0,003 |
| Communication savante | 0,008 | 0,010 |
| Science ouverte | 0,004 | 0,005 |
| Intégrité de la recherche | 0,006 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,008 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».