Using crime place networks to understand terrorist attacks: the importance of in-person and online crime-involved places
Bibliographic record
Abstract
Traditionally, terrorism studies have focused more on why extremist violence occurs and less on how it occurs. This study applies crime place networks, a concept from environmental criminology, to analyse extremist violence. Crime place networks, made up of crime-involved places (CS4), help uncover the infrastructure underlying criminal activities. Comprised of crime sites, convergence settings, comfort spaces, and corrupting spots, CS4 offers a comprehensive way to examine how extremist violence occurs. Using an example, we illustrate the utility of CS4 in the context of extremist violence. Findings demonstrate the potential of CS4 to unveil previously overlooked places crucial to extremist violence and inform counter-extremism efforts. Furthermore, CS4 provides an alternative illustration of how extremists use online places to facilitate their attacks. Ultimately, this paper offers a unique theoretical contribution by introducing a new application of CS4 and underscoring the significance of integrating it with crime script analysis and geographic profiling efforts.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".