Bibliographic record
Abstract
Terrorism and violent extremism have undoubtedly become among the top security concerns of the 21st century. Despite a robust agenda of counterterrorism since the September 11, 2001 attacks, the evolution of global terrorism has continued to outpace the policy responses that have tried to address it. Recent trends such as the foreign fighter phenomenon, the rampant spread of extremist ideologies online and within communities, and a dramatic increase in terrorist incidents worldwide, have led to a recognition that “traditional” counterterrorism efforts are insufficient and ineffective in combatting these phenomena. Consequently, the focus of policy and practice has shifted towards countering violent extremism by addressing the drivers of radicalization to curb recruitment to extremist groups. Within this context, the field of countering violent extremism (CVE) has garnered attention from both the academic and policy-making worlds. While the CVE field holds promise as a significant development in counterterrorism, its policy and practice are complicated by several challenges that undermine the success of its initiatives. Building resilience to violent extremism is continuously challenged by an overly securitized narrative and unintended consequences of previous policies and practices, including divisive social undercurrents like Islamophobia, xenophobia, and far-right sentiments. These by-products make it increasingly difficult to mobilize a whole of society response that is so critical to the success and sustainability of CVE initiatives. This research project addresses these policy challenges by drawing on the CVE strategies of Canada, the US, the UK, and Denmark to collect best practice and lessons learned in order to outline a way forward.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".