Anticipatory concerns about violence within social networks: Prevalence and implications for prevention
Bibliographic record
Abstract
Most research on exposure to violence focuses on direct victimization, offending, or witnessed violence, yet many people also experience concerns about potential violence in their environments and social networks. Using a state-representative survey of California adults (n = 2870) administered in July 2020, we estimate the prevalence of anticipatory concerns about violence within respondents' social networks and describe characteristics of the persons at perceived risk of violence, reasons for respondents' concerns, and actions undertaken by respondents to reduce that risk. Approximately 1 in 5 respondents knew at least one person, usually a friend or extended family member, whom they perceived to be at risk of other- or self-directed violence. Among respondents living with the person at perceived risk, about one-quarter reported household firearm ownership. Alcohol and substance misuse and a history of violence were among respondents' top reasons for concern; serious mental illness and firearm access also contributed to concerns. About one-quarter of respondents with such concerns said harm was likely or very likely to occur in the next year. Most respondents reported having taken action to reduce the risk of violence, including providing resources and asking family or friends to help; few acted to reduce access to lethal means. The most common reasons for inaction were the perception that a dangerous situation was unlikely and that it was a personal matter. Our findings can help inform a broader understanding of exposure to violence and interventions that leverage the knowledge of those close to persons at risk to prevent violence.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".