Making Community Punishments more Effective

让社区惩罚更加有效

• 批准号: 2887193
• 金额: --
• 依托单位: University of Leeds
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2887193
• 关键词: Making; Community; Punishments; more; Effective

Sentencing research has been traditionally restricted by limitations on data availability. Researchers have had to rely on aggregate date from the Ministry of Justice, or one-off surveys of judges such as the Crown Court Sentencing Survey which is now outdated (data collection ceased in 2014). The new linked administrative databases released by ADR UK will permit researchers to explore a much wider range of sentencing questions and to greater depth. This project will make an important contribution to the literature on Effective Sentencing. Specifically, the analyses will explore the relative effectiveness of different requirements attached to a community order. Courts have the discretion to impose a range of requirements on offenders serving community orders, yet almost nothing is known about which conditions or combinations of requirements, are most effective in reducing re-offending. Sentencers are effectively sentencing in the dark, relying on the individual experiences and intuitions of judges and magistrates. Furthermore, this dataset will enable the analysis of hitherto under-explored aspects of sentencing that carry potential consequences for social inequalities. In particular, this project will examine whether the number, nature, and onerousness of requirements varies according to the ethnicity of the offender. If, for instance, the requirements imposed on BAME defendants differed from those imposed on White defendants convicted of the same offence, then this could, in turn, help us understand differences in re-offending rates. Given the great desire by central government to make sentencing more consistent, effective, cost-effective and fair, it is anticipated that this research will have an important impact on policy and practice in the field.Given the absence of empirical research on this area, there is scope to produce impactful findings just from simple descriptive statistics. However, the PhD student will have the opportunity to be trained in two areas of advanced statistical analysis, data visualisation and longitudinal data analysis. Creating intuitive data visualisation tools will be key to be able to summarise and convey large amounts of findings in a way that is meaningful to non-statistically trained audiences. Similarly, the longitudinal dimension of the dataset and the research questions, will require the student to be trained in advanced statistical techniques like sequence analysis and growth curve and autoregressive models. Many of these techniques are currently covered in the MSc in Data Analytics organised by the CDT Data Analytics & Society. Those techniques that are not covered there will be learn through participation in specific short courses such as those delivered by LIDA Societies, the Consumer Data Research Centre, or external courses such as those advertised by the National Centre for Research Methods or the Royal Statistical Society.

传统上，量刑研究受到数据可用性的限制。研究人员不得不依赖司法部的汇总数据，或对法官的一次性调查，如现已过时的刑事法院量刑调查（数据收集于2014年停止）。ADR UK发布的新链接的行政数据库将允许研究人员探索更广泛的量刑问题，并进行更深入的研究。这一项目将对有效判决的文献作出重要贡献。具体而言，分析将探讨不同的要求，附加到社区秩序的相对有效性。法院有权酌情对执行社区命令的罪犯施加一系列要求，但几乎不知道哪些条件或要求的组合对减少重新犯罪最有效。判决者实际上是在黑暗中判决，依靠法官和地方法官的个人经验和直觉。此外，这一数据集将有助于分析迄今为止尚未充分探讨的、可能对社会不平等产生影响的判刑方面。特别是，该项目将审查要求的数量、性质和繁重程度是否因罪犯的种族而异。例如，如果对BAME被告的要求与对被判犯有相同罪行的白色被告的要求不同，那么这反过来又可以帮助我们理解重新犯罪率的差异。鉴于中央政府希望使量刑更加一致、有效、具有成本效益和公平的强烈愿望，预计这项研究将对该领域的政策和实践产生重要影响。鉴于缺乏这一领域的实证研究，仅从简单的描述性统计数据就可以得出有影响力的结果。然而，博士生将有机会在高级统计分析，数据可视化和纵向数据分析两个领域接受培训。创建直观的数据可视化工具将是能够以一种对非统计培训受众有意义的方式总结和传达大量调查结果的关键。同样，数据集和研究问题的纵向维度将要求学生接受高级统计技术的培训，如序列分析、增长曲线和自回归模型。这些技术中的许多技术目前都涵盖在CDT数据分析与社会组织的数据分析硕士课程中。那些没有涉及的技术将通过参加具体的短期课程来学习，如利达协会、消费者数据研究中心提供的课程，或外部课程，如国家研究方法中心或皇家统计学会宣传的课程。