Berkeley Computational Social Science Forum
Date: Tuesday, January 25, 2022
Time: 4:00-5:00 PM Pacific Time
Location: Virtual Participation – Register to attend via Zoom
Partial Perspectives and Situated Knowledges: Radical Objectivity using Computational Methods
Laura K. Nelson, Assistant Professor of Sociology, University of British Columbia
Abstract: Digitized data and computational methods have revolutionized the way we understand ourselves, society, and our place in society. On the one hand, this moment has revived calls for a social physics: a social science that can identify the underlying laws that govern social interaction and behavior. On the other hand, when it comes to prediction, one of the ways to evaluate the efficacy of computational methods to model social systems, even the most sophisticated methods are themselves inaccurate, and perform only marginally better than basic regression models. In this talk I propose that, despite its claims to elevate social science to the level of the physical sciences, the social physics perspective as it is currently practiced produces a decidedly unscientific and unobjective approach to social science. I propose an alternative framework, that of partial perspectives and situated knowledges, that I argue will enable us to better realize the full potential of this moment to truly advance a radically objective science of society.
The Computational Social Science Forum is an informal setting for the interdisciplinary exchange of ideas and scholarship at the intersection of social science and data science. Participants engage in a variety of activities such as presentations of work in progress, discussions and critiques of recent papers, introductions to new tools and methods, discussions around ethics, fairness, inequality, and responsible conduct of research, as well as professional development. This Forum is organized as part of the Computational Social Science Training Program, and weekly meetings are hosted by researchers from BIDS and D-Lab. The group welcomes social scientists and researchers with interests in data science methods and tools, and data scientists with applications or interests in public policy, social, behavioral, and health sciences. Participants include graduate students, postdocs, staff, and faculty, and members are encouraged to attend regularly in order to foster community around improving computational social science research, supporting the development and research of group members, and fostering new collaborations. Interested UC Berkeley community members are invited to use this registration form to receive the schedule and access links. Please contact css-t32@berkeley.edu for more information or if you are interested in presenting current research for an upcoming session.
Speaker(s)
Laura K. Nelson
Laura K. Nelson is an assistant professor of sociology at the University of British Columbia. She uses computational methods – principally text analysis, natural language processing, machine learning, and network analysis techniques – to study social movements, culture, gender, and organizations and institutions. Previously, she was an assistant professor of sociology at Northeastern University, a postdoctoral research fellow at Northwestern University, and a postdoctoral fellow at the Data Science Institute and Digital Humanities at the University of California, Berkeley, which is also where she received her PhD. She has published in outlets such as the American Journal of Sociology, Gender & Society, Poetics, Mobilization, and Sociological Methods & Research. She is currently on the editorial board of Sociological Methodology and is an associate editor at EPJ Data Science.