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. I received my Ph.D. and M.A. in Sociology from Stanford University in California. I earned a B.A. in Biology from Reed College in Portland, Oregon.
Latest News
Bernard Hubbard, S.J., Creative Collaboration Award
"In recognition of having established a well-deserved reputation for excellence in educating students by including them in professional research projects or creative activity, thereby transcending traditional teaching models to reach the heart of the research and creative process."
New publication in Social Currents
Who Authors Social Science? Demographics and the Production of Knowledge
by Jeffrey W. Lockhart, Molly M. King, and Christin Munsch
Author demographics are fundamental to how knowledge is created. Using an original survey of 20,000 authors in sociology, economics, and communication from the Web of Science, the study explores the demographics of not only gender and race/ethnicity but also sexuality, disability, parental education, and employment. We find notable differences between social science authorship and disciplinary membership and faculty: social science author are considerably less diverse than other metrics of disciplinary membership. These descriptive findings have important implications for knowledge prediction in the social sciences as well as inequality and diversity.
New publication in Scientometrics
by Kjersten Bunker Whittington, Molly M. King, and Isabella Cingolani
This article studies gender disparities in scientific collaboration several countries and disciplines. Using network analysis on 1.2 million authors and 144 million co-authorship relationships, we look at how connected authors are, tendencies to author with same-gender collaborators, and the nature of men’s and women’s interdisciplinary and international ties. This is the first paper to take a global, multi-discipline approach to study gender patterns in collaboration without artificially restricting co-authorship networks.
New article in Nature Careers
Computer algorithms infer gender, race and ethnicity. Here's how to avoid their pitfalls
by Jeffrey W. Lockhart, Molly M. King and Christin Munsch
Studies of diversity in academic publishing arrive regularly in the scientific literature. But where do the data come from? In this column, we present the various challenges of demographic-prediction algorithms, and some best practices to minimize the harms based on our longer-form research article published in Nature Human Behaviour.
New publication in Nature Human Behaviour
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.