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  • Item type:Publication,
    Historical comparison of gender inequality in scientific careers across countries and disciplines Open PDF 
    (Proceedings of the National Academy of Sciences, 2020)
    Huang, Junming
    ;
    Gates, Alexander J.
    ;
    Sinatra, Roberta
    ;
    Barabási, Albert-László
    There is extensive, yet fragmented, evidence of gender differences in academia suggesting that women are underrepresented in most scientific disciplines and publish fewer articles throughout a career, and their work acquires fewer citations. Here, we offer a comprehensive picture of longitudinal gender differences in performance through a bibliometric analysis of academic publishing careers by reconstructing the complete publication history of over 1.5 million gender-identified authors whose publishing career ended between 1955 and 2010, covering 83 countries and 13 disciplines. We find that, paradoxically, the increase of participation of women in science over the past 60 years was accompanied by an increase of gender differences in both productivity and impact. Most surprisingly, though, we uncover two gender invariants, finding that men and women publish at a comparable annual rate and have equivalent career-wise impact for the same size body of work. Finally, we demonstrate that differences in publishing career lengths and dropout rates explain a large portion of the reported career-wise differences in productivity and impact, although productivity differences still remain. This comprehensive picture of gender inequality in academia can help rephrase the conversation around the sustainability of women’s careers in academia, with important consequences for institutions and policy makers.
  • Item type:Publication,
    Bibliometric studies on gender disparities in science
    (2019)
    Halevi, G.
    Understanding gender related disparities in science is an essential step in tackling these issues. Through the years, bibliometric studies have designed several methodologies to analyze scholarly output and demonstrate that there are significant gaps between men and women in the scientific arena. However, gender identification in itself is an enormous challenge, since bibliographic data does not reveal it. These bibliometric studies not only focused on publication output and impact, but also on cross-referencing output, promotions and tenure data, and other related curriculum vitae (CV) information. This chapter discusses the challenges of tracking gender disparities in science through bibliometrics and reviews the various approaches taken by bibliometricians to identify gender and analyze the bibliographic data in order to point to gender disparities in science. © Springer Nature Switzerland AG 2019.
  • Item type:Publication,
    Understanding current causes of women's underrepresentation in science Open PDF 
    (2011)
    Ceci, S.J.
    ;
    Williams, W.M.
    Explanations for women's underrepresentation in math-intensive fields of science often focus on sex discrimination in grant and manuscript reviewing, interviewing, and hiring. Claims that women scientists suffer discrimination in these arenas rest on a set of studies undergirding policies and programs aimed at remediation. More recent and robust empiricism, however, fails to support assertions of discrimination in these domains. To better understand women's underrepresentation in math-intensive fields and its causes, we reprise claims of discrimination and their evidentiary bases. Based on a review of the past 20 y of data, we suggest that some of these claims are no longer valid and, if uncritically accepted as current causes of women's lack of progress, can delay or prevent understanding of contemporary determinants of women's underrepresentation. We conclude that differential gendered outcomes in the real world result from differences in resources attributable to choices, whether free or constrained, and that such choices could be influenced and better informed through education if resources were so directed. Thus, the ongoing focus on sex discrimination in reviewing, interviewing, and hiring represents costly, misplaced effort: Society is engaged in the present in solving problems of the past, rather than in addressing meaningful limitations deterring women's participation in science, technology, engineering, and mathematics careers today. Addressing today's causes of underrepresentation requires focusing on education and policy changes that will make institutions responsive to differing biological realities of the sexes. Finally, we suggest potential avenues of intervention to increase gender fairness that accord with current, as opposed to historical, findings.
  • Item type:Publication,
    A push for inclusive data collection in STEM organizations Open PDF 
    (American Association for the Advancement of Science, 2022)
    Burnett, Nicholas P.
    ;
    Hernandez, Alyssa M.
    ;
    King, Emily E.
    ;
    Tanner, Richelle L.
    ;
    Wilsterman, Kathryn
    Professional organizations in science, technology, engineering, and mathematics (STEM) are well-positioned to improve the recruitment and retention (R&R) of underrepresented groups (1, 2) by providing targeted professional development, networking opportunities, and political advocacy (3, 4). Tailoring these initiatives to specific underrepresented groups can enhance their impact (5), but this is predicated on organizations knowing their demographic make-up (6). Here, we report patterns in STEM organizations’ collection and usage of demographic data from members and conference attendees, based on information from 73 professional societies representing 712,000 constituents. In light of inconsistencies and limitations that we observed, we suggest survey programs that can serve as models for inclusive survey designs by organizations and, where possible, provide demographic information for benchmarking relative to the general population. With improved surveys, organizations can leverage demographic data to prioritize and evaluate R&R efforts, and share effective strategies for R&R of underrepresented groups across STEM. Baseline demographic data, when compared to the general population (across STEM or across a country), can help organizations set and prioritize R&R goals and, when monitored over time, help organizations evaluate the effectiveness of R&R efforts. Government agencies often provide the most relevant demographic data for a general population in STEM (e.g., the US National Science Foundation) and in a country (e.g., the US Census Bureau) because of their surveys’ large sample sizes and broad distributions. As a result, organizations may be compelled to use government surveys as a model for demographic survey design and for benchmarking (i.e., comparing their organization’s demographic diversity to the general population). Although these agencies survey many categories of demographic information (e.g., gender identity, family status, citizenship, abilities, race and ethnicity), they do not necessarily collect all the demographic information that is considered meaningful to describe the STEM community—i.e., treating some groups as homogeneous (7) and ignoring other groups completely (8). By contrast, inclusive demographic surveys acknowledge the full diversity of identities that are meaningful among members of the STEM community. Thus, organizations seeking to describe their demographic composition are pressured to choose between following the examples of government agencies versus creating new, inclusive surveys to recognize additional (and evolving) identities within the STEM community.