The US scientific workforce is primarily composed of White men. Studies have demonstrated the systemic barriers preventing women and other minoritized populations from gaining entry to science; few, however, have taken an intersectional perspective and examined the consequences of these inequalities on scientific knowledge. We provide a large-scale bibliometric analysis of the relationship between intersectional identities, topics, and scientific impact. We find homophily between identities and topic, suggesting a relationship between diversity in the scientific workforce and expansion of the knowledge base. However, topic selection comes at a cost to minoritized individuals for whom we observe both between- and within-topic citation disadvantages. To enhance the robustness of science, research organizations should provide adequate resources to historically underfunded research areas while simultaneously providing access for minoritized individuals into high-prestige networks and topics.

Purpose – The purpose of this paper is to provide the first large data-set regression analysis to test Kerlin’s (2013) macro-institutional social enterprise framework in relation to the country social enterprise models that flow from it. Kerlin (2013) offers a conceptual framework for country social enterprise models that allows countries to retain their unique understanding of social enterprise and better understand the factors influencing its development. Design/methodology/approach – This paper draws on the theory of historical institutionalism and multiple global datasets to test formal hypotheses on the relationship between macro-institutional factors and the size of the social enterprise sector across countries. Social enterprise data were obtained from the 2009 Global Entrepreneurship Monitor dataset. Hypotheses were tested using logistic hierarchical linear modeling. Findings – Results provide support for the framework at a significant level. Nearly half of the variance in the size of the social enterprise sector can be attributed to countries-level factors. We also find that the size of the social enterprise sector varies by economic competitiveness rank, size of the welfare state and collectivist cultural orientation. Research limitations/implications – The countries included in this study are not representative of the global landscape. Researchers are encouraged to test the framework with a more representative sample of countries, including those in the Global South. Practical implications – The paper includes implications for policy makers and researchers seeking to facilitate cross-regional dialogue, the transfer and support of social enterprises and research. Originality/value – This paper fulfills an identified need to advance the field of social enterprise by quantitatively testing established frameworks.

Racial disparity in academia is a widely acknowledged problem. The quantitative understanding of racial-based systemic inequalities is an important step towards a more equitable research system. However, because of the lack of robust information on authors’ race, few large-scale analyses have been performed on this topic. Algorithmic approaches offer one solution, using known information about authors, such as their names, to infer their perceived race. As with any other algorithm, the process of racial inference can generate biases if it is not carefully considered. The goal of this article is to assess the extent to which algorithmic bias is introduced using different approaches for name-based racial inference. We use information from the U.S. Census and mortgage applications to infer the race of U.S. affiliated authors in the Web of Science. We estimate the effects of using given and family names, thresholds or continuous distributions, and imputation. Our results demonstrate that the validity of name-based inference varies by race/ethnicity and that threshold approaches underestimate Black authors and overestimate White authors. We conclude with recommendations to avoid potential biases. This article lays the foundation for more systematic and less-biased investigations into racial disparities in science.

Purpose This paper aims to improve upon the initial quantitative assessment of Kerlin’s macro-institutional social enterprise (MISE) framework (Monroe-White et al., 2015) to test for the effect of country-level institutions on the social enterprise sector. Major improvements are the inclusion of the civil society variable and expansion of the culture component in the analysis. Design/methodology/approach By following Kerlin’s (2013) original work that draws on the theory of historical institutionalism, this paper employs multi-level regression analysis to test the effect of country-level institutional factors on organizational-level social enterprise across countries. This analysis uses new macro-level data specifically for civil society and culture components. Findings The initial assessment of the framework found that several country-level factors had a significant effect on the variance in the size of the social enterprise sector across countries. The analysis provided here additionally shows a significant positive influence of civil society on the size of the social enterprise sector and shows that formal institutions capture the effect of informal cultural institutions when included in the model together. Practical/implications This analysis provides policymakers, development actors and researchers with a better understanding of the influence of civil society on social enterprises and the interaction between formal and informal institutional underlying factors. Originality/value This paper’s significant contribution is the addition of civil society in the MISE analysis, which was not possible before owing to lack of data, and additional cultural analysis.

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