On the Fairness of Death-Penalty Jurors: A Comparison of Bayesian Models with Different Levels of Hierarchy and Various Missing-Data Mechanisms
死刑陪审员的公正性:不同层级与缺失机制的贝叶斯模型比较AI 解读 ↓
本页整理公开书目信息,不生成学术质量评分。AI 解读根据题名及所注明的摘要或原文片段生成,未完成全文核验,可能有误;具体假设、证明与结论请核对原文。摘要保留来源文本,未经模型改写。
DOI 首次登记不等于接收或发表日期。部分更正、评论等条目没有独立摘要;缺少可靠材料时保留待补状态。
引用量采用 OpenAlex 单条关联记录,不合计多个记录;关联记录可能包含正式发表版本,不等于此版本单独引用。缺失不视为零,引用指标不代表论文质量。
关注作者按书目中的完整姓名匹配,同名和姓名变体需人工辨别。相关论文先按共享细分方向和具体方法排列,再参考通用方法与发表日期。
AI 根据所提供的论文材料归类:摘要以陪审员有罪或无罪的二元投票选择概率为建模对象,并联系个人信念与群组效应,直接涉及离散选择。
死刑陪审员的公正性:不同层级与缺失机制的贝叶斯模型比较AI 解读 ↓
正在核对关联版本…
正在核对对应版本的资源…
Jurors who are opposed to the death penalty are excluded from serving on trials in which the defendant may be sentenced to death. The exclusion of such jurors from death-penalty trials raises some concern about the fairness of these juries in deciding the guilt or innocence of a defendant. This article uses data from a survey of behaviors and beliefs of jurors conducted at the conclusion of non–capital offense trials to study this issue. A Bayesian hierarchical model is proposed for the probabilities of voting guilty or not guilty on a particular trial given various death-penalty beliefs. The Gibbs sampler is used to fit the model. The posterior distributions of the log odds of voting guilty for jurors who would be excluded from death-penalty juries versus those who would not be excluded suggest that there may be differences between the decisions of jurors on non–capital offense trials. We believe that they are likely to differ on death-penalty trials as well. The results from fitting the hierarchical model, which includes a jury effect, are compared with a previous analysis by Kadane that took the jurors' responses to be independent. At issue is the comparability of the priors under models with different hierarchical structures. Another issue in the analysis is that some jurors did not respond to the survey concerning their death-penalty beliefs. Because the views of nonrespondents may differ from those of respondents, our analysis includes an investigation of how various assumptions about the missing-data mechanism affect the conclusions.