Comparative Analysis of CVE Policies between Canada, US, UK, Sweden, and North Macedonia
Bibliographic record
Abstract
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.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".