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Connected Courts: The Diffusion of Precedent Across State Supreme Courts

机译:关联法院:先例在各州最高法院之间的传播

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摘要

State supreme courts are autonomous institutions with significant power. Yet, despite this authority, state supreme courts routinely rely on one another to explain why and how they reached their decisions. This puzzle of why state supreme courts cite each other in their opinions led me to pose two questions. First, under what conditions do state supreme courts cite other states supreme courts? And second, to whom do they turn for guidance? To answer these questions, I propose a new theory for evaluating state supreme court citations, the social learning model. I borrow policy diffusion's learning mechanism and I pair it with network theory and methods to explain peer-to-peer state supreme court citations practices. I argue that courts are social actors who interact, influence, and learn from one another, and the citations are communications by and between the courts.;To model citations between courts, I apply a temporal exponential random graph network analysis model or TERGM. TERGMs simulate the evolution of the state-to-state citation network by including aspects of both the courts and the network structure. I argue that only by understanding how networks and issue areas evolve can we begin to understand how courts and justices make decisions. The network approach to citations specifically tests these endogenous relationships, it also directly models the complex dependencies of citation networks.;My findings demonstrate the courts became more connected over time and no single state supreme court leader emerges. I find that citations are endogenous; what one court does affects other courts. I also discover that the area of law matters a lot and it is insufficient to pool all legal issues into a single model. Finally, state supreme courts do not cite state supreme courts who look like them. Overall, the evidence suggests the courts are learning from each other. The courts' written language discloses the mechanism. Courts state their own case law does not provide a solution to the question presented and they must seek answers elsewhere. Additionally, the courts do not always cite the same state, as we would expect from emulation. Together, these findings demonstrate that state supreme courts are connected, they learn from one another.
机译:州最高法院是具有重要权力的自治机构。然而,尽管拥有这种权力,各州最高法院通常还是相互依靠,以解释其原因和作出裁决的方式。为什么州最高法院互相援引他们的困惑,使我提出了两个问题。首先,州最高法院在什么条件下引用其他州最高法院?其次,他们向谁求助?为了回答这些问题,我提出了一种用于评估国家最高法院引文的新理论,即社会学习模型。我借用了政策传播的学习机制,并将其与网络理论和方法结合使用,以解释点对点国家最高法院引证的做法。我认为法院是相互影响,相互影响和相互学习的社会行为者,引文是法院之间以及法院之间的交流。为了模拟法院之间的引文,我使用了时间指数随机图网络分析模型或TERGM。 TERGM通过包括法院和网络结构的各个方面来模拟州对州引用网络的演变。我认为,只有了解网络和问题领域的发展方式,我们才能开始理解法院和司法机构如何做出决定。网络引文方法专门测试了这些内在关系,它还直接对引文网络的复杂依赖性进行建模。我发现引文是内生的;一个法院的行为会影响其他法院。我还发现法律领域很重要,不足以将所有法律问题集中到一个模型中。最后,州最高法院不会援引看起来像它们的州最高法院。总体而言,有证据表明,法院在相互学习。法院的书面语言公开了该机制。法院指出,他们自己的判例法不能解决提出的问题,因此必须在其他地方寻求答案。此外,法院并不总是像我们从模拟中所期望的那样总是引用相同的状态。这些发现加在一起表明州最高法院是相互联系的,它们相互学习。

著录项

  • 作者

    Matthews, Abigail Anne.;

  • 作者单位

    The University of Iowa.;

  • 授予单位 The University of Iowa.;
  • 学科 Political science.
  • 学位 Ph.D.
  • 年度 2017
  • 页码 166 p.
  • 总页数 166
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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