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The Diversity – Innovation Paradox in Science

Professor Daniel McFarland
Stanford Graduate School of Education

Monday, May 18, 2020
12:00-1:30 pm

Register in advance to connect via Zoom




Prior work finds a diversity paradox: Diversity breeds innovation, yet underrepresented groups that diversify organizations have less successful careers within them. Does the diversity paradox hold for scientists as well? We study this by utilizing a near-complete population of ∼1.2 million US doctoral recipients from 1977 to 2015 and following their careers into publishing and faculty positions. We use text analysis and machine learning to answer a series of questions: How do we detect scientific innovations? Are underrepresented groups more likely to generate scientific innovations? And are the innovations of underrepresented groups adopted and rewarded? Our analyses show that underrepresented groups produce higher rates of scientific novelty. However, their novel contributions are devalued and discounted: For example, novel contributions by gender and racial minorities are taken up by other scholars at lower rates than novel contributions by gender and racial majorities, and equally impactful contributions of gender and racial minorities are less likely to result in successful scientific careers than for majority groups. These results suggest there may be unwarranted reproduction of stratification in academic careers that discounts diversity’s role in innovation and partly explains the underrepresentation of some groups in academia.


Bio: Professor McFarland studies the social and organizational dynamics of educational systems like schools, classrooms, and universities. In particular, he has performed a series of studies on classroom organization and interaction; on the formation of adolescent relationships, social structures, and identities; on interdisciplinary collaboration and intellectual innovation; and on relational sociology. He has broad research interests and has been drawn into a variety of interdisciplinary collaborations with linguists and computer scientists. This in turn has led to studies of big data and methodological advances in social networks and language modeling.