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.

Most research on segregation in social networks considers small circles of strong ties, and little is known about segregation among the much larger number of weaker ties. This article proposes a novel approach to the study of these more extended networks, through the use of data on personal ties in an online social network. We illustrate this method’s potential by describing and explaining the degree of ethnic and gender segregation on Facebook among a representative survey of adolescents in the Netherlands ( N = 2,810; ~1.1 million Facebook friends). The results show that large online networks are more strongly segregated by ethnicity than by gender. Drawing on the same survey data, we find that core networks are more segregated in terms of ethnicity and gender than are extended networks. However, an exception to this pattern is personal networks of ethnic majority members, whose core networks are as segregated by ethnicity as their extended networks. Further analysis suggests this exception is due to their larger population size and the ethnic segregation of their social settings. We discuss the implications of these findings for the role of structural opportunities, homophily, and balance.

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