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“The Radical Inductiveness of Machine Learning”
February 24 @ 12:10 pm - 1:00 pm
This talk by Dr. Laura Nelson from Northeastern University is co-hosted by DataLab and Dr. Kim Shauman, Department of Sociology.
Abstract: Machine learning is often framed in the social sciences as a more sophisticated way to do regression analysis. In this talk I argue that this is an epistemological distortion: the mathematical assumptions behind machine learning are much closer to the epistemology of inductive methods than they are to the deductive requirements of regression analysis. I use three examples from my own research to demonstrate how machine learning and other computational methods can be used in interpretive research: (1) measuring the meaning-making function of social movement strategy via discourse, (2) explaining the recent rapid growth of the concept of implicit bias as a way to address gender equality, and (3) using word embedding models to reveal intersectional lived experiences. Through these examples I discuss in concrete ways how machine learning and computational methods can be combined with qualitative methods to advance interpretive research. I ultimately contend that machine learning can not only be used in qualitative ways, it is, down to its most basic assumptions, a radically inductive method.
Bio: Dr. Nelson is an assistant professor of sociology at Northeastern University where she uses computational tools, principally automated text analysis, to study social movements, culture, gender, institutions, and organizations. She seeks to use open-source tools and computational methods to make the social sciences and humanities more transparent, reproducible, and scalable. She is core faculty at the NULab for Text, Maps, and Networks, a Faculty Affiliate at the Network Science Institute, and serves on the Executive Committee of the Women’s, Gender, and Sexuality Studies Program, and on the Editorial Board of Signs. Prior to joining Northeastern, Dr. Nelson was a postdoctoral research fellow at Digital Humanities @ Berkeley, the Berkeley Institute for Data Science, and the Management and Organizations Department in the Kellogg School of Management at Northwestern University, and was also a research affiliate at the Northwestern Institute on Complex Systems. She received her PhD in sociology from UC Berkeley.
- February 24
12:10 pm - 1:00 pm
- Event Category:
- DataLab: Data Science and Informatics (DSI)
- Shields Library, room 360