Bibliographic record
Abstract
Abstract The changing and increasingly complex nature of violent extremism has prompted important changes to the ways police in Canada and the United States understand and respond to this violence. A burgeoning research literature examines the reasons underpinning these changes, documents the corresponding expansion of national security apparatuses in both countries and the policy and operational innovations that drove this expansion, and raises important questions about the way forward: Can an operational combination of enforcement and prevention-based programming complement and supplement one another in ways that improve the police response to extremist violence? How have public health frameworks been deployed in the context of police co-led extremism prevention programming? What are the most salient operational challenges for police and partner agencies? Is it possible to ensure that the police response is equitable and effective in responding to the current threat landscape? What role can research and evidence-based practice play to inform and assess the efficacy of current approaches? Such questions mirror the ways traditional, reactive, and enforcement-based approaches to policing violent extremism have been morally interrogated as the primary response to this violence. Lessons learned from an overemphasis on such approaches are imperative to the development of effective, legally and socially responsible approaches for responding to extremist violence in North America.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".