Doctoral Dissertation Research: Partisanship, Gender, and Policy Frames

尽管在过去30年里取得了进展，但相对于普通民众，女性在国会中的代表性仍然最低。这个项目的目的是为了更好地了解国会中的女性是如何代表女性的。为此，该项目审查了党派和性别对国会关于妇女问题的通信内容以及选民对这些信息的反应的相对影响。为了完成这项任务，该项目分析了4万多份关于妇女问题的国会通讯的内容，并利用这些发现进行了一项调查实验，以测试选民中的男性和女性如何根据信使的性别对有关妇女问题的信息进行不同的解读。这个项目有助于正在进行的关于代表权的重要辩论--妇女是否实质上代表了妇女，所有成员都纯粹是党派之争，还是介于两者之间？而且，选民对妇女问题的意见如何受到提出这些意见的人的性别的影响？这项研究将推动我们对国会中妇女代表的性质的理解，并将扩大选民和国会议员对妇女在妇女问题上的描述性和实质性代表的质量的认识。本项目的目的是评估党派和性别对国会关于妇女问题的沟通内容和选民对这些信息的反应的相对影响。为了完成这项任务，该项目使用结构化主题建模来确定性别和党派意识如何影响国会通讯中关于妇女问题的传播。它在一项调查实验中利用这些发现，将传递内容的代表的性别随机化，以测试选民中的男性和女性如何根据信使的性别对有关妇女问题的信息进行不同的解读。这个项目总体上对美国政治领域做出了几项不同的贡献，特别是在代表和政治沟通方面的学术贡献。在方法上，它利用一种创新的方法工具--结构主题建模--来分析大型语料库。它将这些文本的内容与元数据中提供的类别进行比较，包括性别和党派之争。然后，该项目在一项调查实验中使用这些发现来评估不同内容对选民的影响。到目前为止，这个项目将是第一个使用这种方法来确定立法机构男女成员之间不同的沟通模式并评估这些差异对选民的影响的奖学金。这项贡献，以及未来利用类似方法进行的研究，将使我们能够更好地了解女性代表的特征，并将有助于更广泛地了解政治信息的内容和影响。这一奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

Despite gains made over the past three decades, women remain the most underrepresented group in Congress relative to the general population. The purpose of this project is to better understand how women in Congress represent women generally. To this end, the project examines the relative influence of partisanship and gender on the content of congressional communications about women's issues and voters' responses to those messages. To accomplish this task, the project analyzes the content of more than 40,000 congressional newsletters about women's issues and uses the findings to generate a survey experiment to test how men and women in the electorate interpret messages about women's issues differently based on the gender of the messenger. This project contributes to an important, ongoing debate about representation --do women substantively represent women, are all members purely partisan, or is it something in between? And, how are voter opinions about women's issues affected by the gender of the person who presents them? This research will push forward our understanding of the character of women's representation in Congress, and will broaden the knowledge held by both voters and members of Congress concerning the quality of descriptive and substantive representation of women on women's issues.The purpose of this project is to evaluate the relative influence of partisanship and gender on the content of congressional communications about women's issues and voters' responses to those messages. To accomplish this task, the project uses structural topic modelling to identify how gender and partisanship influence communication about women's issues in congressional newsletters. It utilizes these findings in a survey experiment that randomizes the gender of the representative delivering the content to test how men and women in the electorate interpret messages about women's issues differently based on the gender of the messenger. This project presents several distinct contributions to the field of American politics generally, and scholarship on representation and political communication in particular. Methodologically, it utilizes an innovative methodological tool --structural topic modelling--to analyze a large corpus. It compares the content of these texts between categories available in the metadata, including gender and partisanship. The project then employs these findings in a survey experiment to assess the effect of the differing content on the electorate. This project will be the first scholarship, as of this date, to use this method to identify distinct patterns of communication between male and female members of legislatures and to assess the impact of these differences on the electorate. This contribution, and future research that utilizes a similar methodological approach, will allow us to better understand the character of women's representation and will facilitate a broader understanding of the content and impact of political messages generally.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.