Partial Perspectives and Situated Knowledge: Radical Objectivity using Computational Methods
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, if at all, 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 knowledge, that I argue will enable us to best realize the full potential of this moment to truly advance a radically objective science of society.
Bio: Laura K. Nelson 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. Substantively, her research has examined processes around the formation of collective identities and social movement strategy in feminist and environmental movements, continuities between cycles of activism and the role of place in shaping social movement activity, intersectionality in women’s movements and in the lived experiences during the 19th century in the U.S. South, gender inequality in startups and entrepreneurship, the translation of academic ideas to practice in the National Science Foundation’s ADVANCE program (a program aimed at promoting women in STEM field in higher education), and gender inequality in emergency medicine departments. Methodologically, she has proposed frameworks to combine computational methods and machine learning with qualitative methods, including the computational grounded theory framework and leveraging the alignment between machine learning and the intersectionality research paradigm. She has developed and taught courses introducing social science and humanities students to computational methods and the scripting languages Python and R, data science courses, and graduate-level sociological theory. She is currently a co-PI on a million-dollar grant through the National Science Foundation to study the spread of gender-equity ideas related to STEM fields through higher education networks, primarily in the United States.
