Do arrests (and killings) deter violent extremism? A comparative analysis
Bibliographic record
Abstract
There is an ever-growing body of evidence that suggests that there exists a significant degree of overlap between violent extremism (VE) and ordinary crime, both at the conceptual level and in terms of patterns and predictors.Countries differ considerably in their approaches to countering violent extremism (CVE).Yet, at least in the west, one common feature is the criminal justice system, whose role is essentially the same for VE as it is for other forms of crime.Despite this, there is little quantitative research on policing and criminal justice system effects on VE.Among the few studies that do exist, most focus on single countries, and examine long observation periods.Our analysis compares two key democratic countries that have received less attention, Canada and Sweden, and finds evidence of heterogeneous effects and patterns concerning how arrests impact the risk of future VE.This suggests that studies focusing on single contexts may have limited generalizability and that current wisdom concerning deterrence-backlash effects is more limited than previously thought.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".