CAREER: Mass Incarceration, Racial Segregation, and Spillover Effects in U.S. Communities

职业：美国社区的大规模监禁、种族隔离和溢出效应

• 批准号: 2237580
• 负责人: Jessica Simes
• 金额: $ 60.05万
• 依托单位: Trustees of Boston University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-01 至 2028-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2237580&HistoricalAwards=false
• 关键词: CAREER; Mass; Incarceration; Racial; Segregation

This project investigates the causes and consequences of mass incarceration within U.S. communities and neighborhoods. The overarching goal is to study how residential inequalities drive incarceration rates and disparities, and how neighborhood exposures to the criminal justice system affect the well-being and political participation of community members. The first part of the project evaluates and tests place-based mechanisms, specifically segregation and housing conditions, that may be driving disparities between demographic groups and shifts in incarceration over time. This part of the study seeks to understand how contemporary incarceration rates and disparities emerge from both historical residential patterns and recent changes in U.S. communities. The second part of the project examines how multiple, interrelated criminal justice system exposures, from contact to imprisonment, may accumulate to affect community well-being and political participation. An open criminal justice data project establishes a replicable system of data collection, processing, archiving, and analysis that coincides with an interdisciplinary educational program of courses, practicums, and symposia. The integrated research and educational aims of this project expand the participation of underserved students in STEM, share newly collected data on criminal justice exposures with the public, and provide useful insights for interventions addressing inequality and mass incarceration.To better understand rates of and disparities in criminal justice contact, as well as their influence on community well-being and democratic membership, this project focuses on the measurement and influence of place-based mechanisms—specifically, residential segregation and neighborhood effects. The two studies that comprise this project integrate data science methodologies, machine learning, spatial analysis, and quasi-experimental design to test theories relating segregation to mass incarceration and community-level spillover effects. To do so, these studies combine rarely accessible geocoded data on policing and incarceration with detailed individual-level data on voting and mortality. By combining top-down sociological theories of place and punishment with bottom-up data science and machine-learning techniques, this project brings new evidence to bear on these theories and contributes synergistic new methods to the fields of sociology, data science, and their application.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该项目调查了美国社区和社区内大规模监禁的原因和后果。总体目标是研究居民如何推动监禁率和差异，以及邻里对刑事司法系统的暴露如何影响社区成员的福祉和政治参与。该项目的第一部分评估和测试基于位置的机制，特别是隔离和住房条件，这些机制可能会导致人口统计组之间的差异以及随着时间的推移的变化。该研究的这一部分旨在了解当代的遗产率和差距如何从历史居民模式和美国社区的最新变化中出现。该项目的第二部分研究了多个相互关联的刑事司法系统如何从接触到监禁，可能会积累以影响社区福祉和政治参与。一个开放的刑事司法数据项目建立了可复制的数据收集，处理，归档和分析系统，与课程，实践和专题讨论会的跨学科教育计划相吻合。该项目的整体研究和教育目标扩大了服务不足的学生参与STEM的参与，与公众分享有关刑事司法暴露的新数据，并为解决不平等和大规模监禁的干预措施提供了有用的见解，以更好地了解刑事司法的接触率和对社区的居民的影响，并影响社区福祉和民主党的焦点，并对其进行了衡量的影响，以及对社区居民的影响，以及对统一的群体的影响，以及对社区司法的影响，并在此范围内对自己的影响力，以及对社区司法的影响以及对刑事司法的关注量隔离和邻里影响。完成该项目的两项研究集成了数据科学方法，机器学习，空间分析和准实验设计，以测试将种族隔离与质量影响和社区级别的Spilover效应相关的理论。为此，这些研究将有关政策和进化的地理编码很少访问的数据与有关投票和死亡率的详细个人级别数据结合在一起。通过将自上而下的社会学理论与自下而上的数据科学和机器学习技术相结合，该项目将新的证据带来这些理论，并为社会学，数据科学及其应用领域贡献了协同的新方法。该奖项通过评估了NSF的法定任务，反映了NSF的法定任务，并通过评估了Intelligia的知识范围。