Molly M. King
Who owns knowledge and how does this shape inequality?
I am a sociologist who studies knowledge inequalities and the implications of these inequalities for people's lives.
I am an Assistant Professor in the Department of Sociology at Santa Clara University. Learn more >
New publication in Nature Human Behavior
Name-Based Demographic Inference and the Unequal Distribution of Misrecognition
by Jeffrey W. Lockhart, Molly M. King and Christin Munsch
Academics and companies increasingly draw on large datasets to understand the social world, and name-based demographic ascription tools are widespread for imputing information that is often missing from these large datasets. We find substantial inequalities in how these tools misgender and misrecognize the race/ethnicity of authors, distributing erroneous ascriptions unevenly among other demographic traits.
New public sociology piece!
Standing Up for Environmental Justice for People with Disabilities
by Molly M. King and Emily Pachoud
Statement on how people can learn, support, and engage around issues of environmental justice for people with disabilities. (Written on the invitation of SCU's Environmental Justice and the Common Good Initiative.)
New in Nature News & Views!
Self-publishing is Common among Academic-Journal Editors
by Molly M. King
An analysis of the publication records of academic editors shows that one-quarter of them publish 10% of their own papers in the journals they edit and reveals that fewer than 10% of editors-in-chief are women.
New publication in Teaching Sociology!
The Undergraduate RA: Benefits and Challenges for Sociology Faculty and Research Assistants
by Molly M. King and Megan K. Imai
By interviewing 13 undergraduate research assistants and 10 faculty in sociology departments at primarily undergraduate institutions, we outline the benefits and challenges of faculty-directed research with undergraduates.
New publication in Sociological Methodology!
REDI for Binned Data: A Random Empirical Distribution Imputation Method for Estimating Continuous Incomes
by Molly M. King
The method I develop in this paper, random empirical distribution imputation (REDI), converts binned income data to continuous. REDI achieves this through random cold-deck imputation from a real-world reference data set.