Identifying high-risk firearm owners to prevent mass violence
Identifying high-risk firearm owners to prevent mass violence
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DOI：
10.1111/1745-9133.12477
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发表时间：
2019-12-16
影响因子：
4.6
通讯作者：
Wintemute, Garen J.
中科院分区：
文献类型：
--
作者：
Laqueur, Hannah S.;Wintemute, Garen J.
研究摘要 在本文中，我们详细介绍了加利福尼亚州最近为识别和瞄准高风险枪支拥有者所做的努力，以帮助防止枪支暴力，包括大规模枪击事件。我们首先描述枪支暴力限制令，也称为极端风险保护令，它为枪支追回和限时禁止购买枪支提供了司法机制。接下来，我们讨论加州的武装和禁止人员 (APPS) 数据库和执行系统。 APPS 用于识别合法枪支拥有者中新被禁止的人员，并帮助执法部门追回这些枪支。最后，我们重点介绍早期研究，其中利用机器学习进行罕见事件检测，利用加州数十年的枪支交易记录和其他现成的管理数据来预测个人风险。政策影响 所描述的方法在规模、范围和战略上各不相同，但这三种方法都允许在风险高时进行有针对性的干预。通过这样做，它们有可能为防止大规模暴力的努力提供巨大的好处。
Research Summary In this article, we detail recent efforts in California to identify and target high-risk firearm owners to help prevent firearm violence, including mass shootings. We begin by describing gun violence restraining orders, also known as extreme risk protection orders, which provide a judicial mechanism for firearm recovery and a time-limited prohibition on firearm purchases. Next, we discuss California's Armed and Prohibited Persons (APPS) database and enforcement system. APPS is used to identify newly prohibited persons among legal firearm owners and to help law enforcement recover those firearms. Finally, we highlight early research in which machine learning for rare event detection is employed to forecast individual risk using California's decades worth of firearm transaction records and other readily available administrative data. Policy Implications The approaches described range in scale, scope, and strategy, but all three allow for targeted intervention at times of heightened risk. In so doing, they offer the potential to provide outsized benefits to efforts to prevent mass violence.