Laura K. Nelson, Assistant Professor of Sociology, Northeastern University.
Machine learning is a rapidly growing research paradigm. Despite its foundationally inductive mathematical assumptions, the machine learning paradigm is currently developing alongside traditionally deductive inferential statistics but largely orthogonally to inductive, qualitative, cultural, and intersectional research — to its detriment.
I argue that we can better realize the full potential of machine learning by leveraging the epistemological alignment between machine learning and inductive research.
I empirically demonstrate this alignment through a word embedding model of first-person narratives of the nineteenth-century U.S. South. Situating social categories in relation to social institutions via an inductive computational analysis, I find that the cultural and economic spheres discursively distinguished by race in these narratives, the domestic sphere distinguished by gender, and Black men were afforded more discursive authority compared to white women. Even in a corpus over-representing abolitionist sentiment, I find white identities were afforded a social status via culture not allowed Black identities.
About the speaker
Laura K. Nelson is an assistant professor of sociology at Northeastern University. She is also core faculty at the NULab for Text, Maps, and Networks and a faculty affiliate at the Network Science Institute. She is on the executive committee of the Women's, Gender, and Sexuality Studies Program, and on the editorial board of Sociological Methodology and Signs.
Professor Nelson has been 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. She has also been a research affiliate at the Northwestern Institute on Complex Systems.
Nelson uses computational tools, principally automated text analysis, to study social movements, culture, gender, institutions, and organizations.
She is an open source and open science enthusiast. She seeks to use open-source tools and computational methods to make the social sciences and humanities more transparent, reproducible, and scalable.
Nelson received her PhD in sociology from the University of California, Berkeley. She holds an MA from UC Berkeley and a BA from the University of Wisconsin, Madison.