Doctoral Dissertation Research: The Differential Influence of Three Types of Elites

博士论文研究：三类精英的不同影响

• 批准号: 2001886
• 负责人: Aaron Major
• 金额: $ 1.55万
• 依托单位: SUNY at Albany
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-03-01 至 2021-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2001886&HistoricalAwards=false
• 关键词: Doctoral; Dissertation; Research; Differential; Influence

This project examines and compares the effects of influence exerted by three different categories of elites on an important security-related area of deliberation. The project will investigate the proposition that the usual assumption of the preeminence of the economic elite is not valid in this area. Data will come from a variety of compiled journals, magazines, and official texts from 1990 to 2019. These will be analyzed using computer-assisted content analysis and narrative analysis. Findings are likely to be relevant for other areas of deliberation as well and will be informative for those concerned with security decision-making.The data come from a representative corpus of texts compiled from official reports, journals and magazines, and different kinds of official records. The corpus covers the three categories of elites and their discourse in this area, with approximately one million words for each one. The dates covered are from 1990 to 2019. Data analysis will integrate multiple computer-assisted content analysis and qualitative narrative analysis techniques. Structural Topic Model and Concept Mover’s Distance will be used to compare and contrast the focus of each category of elite. Qualitative narrative analysis, Concept Mover’s Distance, and Text Network Analysis will be used on two different issues to investigates how different elites have shaped narratives.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.

该项目研究并比较了三类不同精英对与安全相关的重要审议领域所施加的影响。该项目将调查以下命题：经济精英的卓越地位的通常假设在这一领域是不成立的。数据将来自 1990 年至 2019 年的各种汇编期刊、杂志和官方文本。这些数据将使用计算机辅助内容分析和叙述分析进行分析，结果可能与其他审议领域相关。这些数据来自官方报告、期刊和杂志以及不同类型的官方记录汇编而成的代表性文本语料库，该语料库涵盖了三类精英及其话语。该领域的内容大约为 100 万字，涵盖的日期为 1990 年至 2019 年。数据分析将采用多种计算机辅助内容分析和定性叙事分析技术。用于比较和对比每类精英的关注点。定性叙事分析、概念移动者距离和文本网络分析将用于两个不同的问题，以调查不同的精英如何塑造叙事。该奖项反映了 NSF 的法定使命，并已被通过使用基金会的智力优点和更广泛的影响审查标准进行评估，认为值得支持。