Doctoral Dissertation Research: Community Partisanship: Analyzing the Local Processes that Produce National Politics

人们居住的社区如何塑造他们理解国家政治的方式？虽然红州和蓝州的概念对大多数美国人来说都很熟悉，但我们对这种拼凑如何从生活在其中的人们的日常过程和互动中产生的了解甚少。为了了解当地环境如何产生美国政治的地理，这个项目在2020年总统竞选期间跟踪了三个社区。这些社区在社会经济上是相似的，但在历史上投票非常不同。这三个城镇都是类似的蓝领，中西部城镇位于主要的农村县，但他们在总统选举中投票给反对党：一个是坚定的民主党，另一个是虔诚的共和党，第三个的投票历史更为复杂。这些社区是什么影响了相似的人以不同的方式投票？该项目的调查结果将对所有有兴趣了解和预测总统选举投票模式的政治行为者以及对构建有效的政治运动感兴趣的人感兴趣。 为了理解为什么人口统计学上相似的小城镇在总统竞选中的投票不同，这个项目使用了定性和定量的方法。根据1932-2016年的人口普查和投票记录，使用分层聚类算法根据1964-2016年的人口统计相似性和不同的投票历史来识别这三个案例。该研究的定性部分将利用三种不同类型的数据：在2020年美国总统大选期间的四个不同时间点对三个城镇的90名选民进行深入访谈;对来自教会，非营利组织，工会，政党和地方政府的75名社区领袖进行深入访谈;并对运动期间主要社区组织的动员工作进行人种学观察。将使用RQDA定性分析包分析所有定性数据。 访谈和观察的数据将展示组织和社区生活的中观层面如何塑造选民对国家政治的理解。在回答这个问题时，本研究通过重新定位其焦点，从个人的人口统计数据转向以地方为基础的意义决策过程，帮助人们理解复杂的政治世界，从而推进了美国政治中的党派偏见和两极分化。 该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

How do the communities where people live shape the way they understand national politics? Although the concepts of red and blue states are familiar to most Americans, we have little understanding of how that patchwork emerges from the everyday processes and interactions of the people who live within it. To understand how local contexts produce the geography of American politics, this project follows three communities through the 2020 presidential campaign. These communities are similar socioeconomically, but have historically voted very differently. The three towns are similar blue-collar, Midwestern towns located in predominately rural counties, and yet they vote for opposing parties in presidential elections: one is staunchly Democratic, another is devoutly Republican, and a third has a voting history that is more mixed. What is it about these communities that influences similar people to vote differently? Findings from the project will be of interest to all political actors who have an interest in understanding and predicting voting patterns in presidential elections, as well as those interested in constructing effective political campaigns. To understand why demographically similar small towns vote differently in presidential campaigns, this project uses both qualitative and quantitative methods. Relying on Census and voting records from 1932-2016, a hierarchical clustering algorithm was used to identify the three cases based on their demographic similarities and their different voting histories from 1964-2016. The qualitative portion of the study will draw on three different types of data: in-depth interviews conducted with 90 voters across the three towns at four different points during the 2020 U.S. presidential election; in-depth interviews with 75 community leaders from churches, non-profits, unions, political parties, and local government; and ethnographic observation of key community organizations’ mobilization efforts during the campaign. All qualitative data will be analyzed using the RQDA qualitative analysis package. Data from the interviews and observation will demonstrate how the meso-level of organizations and community life shape voters’ understandings of national politics. In answering this question, this study advances the scholarship on partisanship and polarization in American politics by re-orienting its focus away from individuals’ demographics and toward the place-based processes of sense-making that help people understand the complex political world. Findings from the project will inform sociological theories of political identity and political participation.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.