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 UniversityI 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.

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Latest News

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

Structure, Status, and Span: Gender Differences in Co-authorship Networks Across 16 Region-Subject Pairs (2009–2013)

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 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 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.