Social Movements in Higher Education, 2012-18

该项目研究了2012年至2018年期间大学校园的社会运动，这是一个社会运动活动水平很高的时期。通过社会运动的参与，学生和校园社区成员从事言论自由和公民生活，发展自己的身份和领导能力，并影响大学的政策和做法。本研究调查：1）鼓励高等教育社会运动及其在校园和国家传播的因素; 2）大学用来回应的策略和言论。该项目有助于社会运动，组织，高等教育和社交媒体的社会学研究。对运动积极性和大学反应的阐述告诉我们校园社区如何更有效地工作，使大学具有包容性和支持表达。本项目为了解2010年代的社会运动，构建了一个创新的定量数据集，记录了高等教育的社会运动和大学行政部门的反应。通过机器学习和手工编码，研究人员从两个国家的550份不同的学生报纸中提取了16，000多篇新闻文章。研究人员对同一社会运动的不同说法进行了调和。关于社会运动的数据与关于高等教育机构、校外活动和社交媒体的组织特征的二级数据集合并。该数据集通过一个互动网站向公众提供，该网站说明并绘制了社会运动模式。一种新的基于浏览器的开源工具，用于协调不同来源的差异，提高了研究人员收集和使用高质量社会运动事件数据的能力。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。

This project examines social movements on university campuses during 2012 to 2018, a period with high levels of social movement activity. Through social movement participation, students and campus community members engage in free speech and civil life, develop their identities and leadership skills, and influence university policies and practices. This study investigates: 1) the factors that encourage higher education social movements and their spread across campuses and countries; and 2) the strategies and rhetoric that universities use to respond. The project contributes to the sociological study of social movements, organizations, higher education, and social media. Explanations of movement activism and university responses inform how campus communities may work more effectively to make universities inclusive and supportive of expression. Beneficiaries include students, staff, and university administrators.To understand social movements in the 2010s, this project constructs an innovative quantitative dataset that documents higher education social movements and university administrations’ responses. Using machine learning and hand-coding, researchers draw from more than 16,000 news articles from 550 different student newspapers across two countries. Researchers reconcile different accounts of the same social movement. Data on social movements are merged with secondary datasets on the organizational characteristics of post-secondary institutions, off-campus events, and social media. The dataset is made available to the public through an interactive website that illustrates and maps social movement patterns. A new open-source browser-based tool for reconciling differences across sources improves the ability of researchers to collect and use high quality social movement event data.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.