Comparative Analysis of CVE Policies between Canada, US, UK, Sweden, and North Macedonia
Notice bibliographique
Résumé
In the field of counter-terrorism (CT) and countering violent extremism (CVE), policymakers are in constant need of accurate data to make informed decisions to support existing programs and develop new approaches to prevent radicalization to violence. The goal of the comparative analysis in this presentation is to identify the types of data needed to assess the impact of CT and CVE programs based on each country’s policy goals. A comparative analysis of the five countries’ specific CT/CVE policies was conducted to identify common themes and data needs. The first most widely discussed theme is the need to maintain and expand collaborations and information sharing across countries—all five policies strongly emphasize the importance of such collaborative efforts. All policies address the need for strengthening collaborations at the local level, considering the important role civil society plays in the frontline response to violent extremism. In particular, the North Macedonian policy recognizes the need to fully engage in multidisciplinary interagency efforts that include civil society in the process for reconciliation of ethnic and cultural divides, educate and promote democratic values in schools and faith based communities. According to the policy documents, it can be found that there is a need for a better understanding of what types of collaborative efforts and partnerships are needed to establish effective CT and CVE programs. All policies stress the need to address a range of extremist ideologies including Jihadist, Far Left, and Far Right groups to address radicalization in the online space as well as through in-person interventions. In terms of interventions, there is a need to understand what type of training is most effective to equip frontline professionals with the knowledge and skills to intervene when individuals engaged in VE come to their attention. The United States policy is innovative with respect to the others because it introduces the concept of targeted violence. By doing so, it recognizes the importance of including situations where ideology is not a motivating factor or the motivations are unknown behind the acts of violence. The Swedish policy is distinguished by its detailed legislation supporting the prevention of terrorist acts. The UK policy emphasizes the need to contrast ideologies and views that are not aligned with UK values. All policies recognize the need for evidence on strategic efficacy and recognize the fact that programs and policies have been widely implemented without scientific proof of their effectiveness. In particular, the Canadian policy points to the need for identifying best practices that can be transferred from case to case or country to country. As an area of policy improvement across countries, there is certainly a lack of clarity on the roles and responsibilities of the many agencies that may be potentially involved in prevention efforts, still leaving a nebulous space in terms of when and how security intercepts social work and public health.
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 | 0,004 | 0,011 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,004 | 0,005 |
| Études des sciences et des technologies | 0,004 | 0,002 |
| Communication savante | 0,005 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
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 ».