Obstacles and facilitators to intimate bystanders reporting violent extremism or targeted violence
Bibliographic record
Abstract
The first people to suspect someone is planning an act of terrorism or violent extremism are often those closest to them. Encouraging friends or family to report an “intimate” preparing to perpetrate violence is a strategy for preventing violent extremist or targeted mass violence. We conducted qualitative-quantitative interviews with 123 diverse U.S. community members to understand what influences their decisions to report potential violent extremist or targeted mass violence. We used hypothetical scenarios adapted from studies in Australia, Canada, and the United Kingdom. Factors influencing reporting decisions include fears of causing harm to the potential violent actor, self, family, or relationships; not knowing when and how to report; mistrust of law enforcement; access to mental health services; and perceptions that law enforcement lacks prevention capabilities. White and non-White participants were concerned about law enforcement causing harm. Participants would contact professionals such as mental health before involving law enforcement and Black-identified participants significantly preferred reporting to non-law enforcement persons, most of whom are not trained in responding to targeted violence. However, participants would eventually involve law enforcement if the situation required. They preferred reporting in-person or by telephone versus on-line. We found no difference by the type of violent extremism or between ideologically motivated and non-ideologically motivated violence. This study informs intimate bystander reporting programmes in the U.S. To improve reporting, U.S. policymakers should attend to how factors like police violence shape intimate bystander reporting. Our socio-ecological model also situates intimate bystander reporting beside other population-based approaches to violence prevention.
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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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".