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  • Item type:Publication,
    The scientometrics of successful women in science
    (2016)
    Madlock-Brown, C.
    ;
    Eichmann, D.
    This paper examines the effects of gender differences in collaboration on research outcomes. We analyzed network characteristics of seventeen medical research institutions that are Clinical and Translational Science Awardees (CTSA) to determine if network connectivity characteristics have the potential to help mitigate the performance gap between the sexes. We determined betweenness centrality to identify well-connected researchers. Then we used clustering coefficient to determine how tightly connected their collaborators were with each other. We correlate these scores with productivity (number of total publications for each author), and h-index (the number of papers h for which an author has h citations). We also provide data on how network characteristics vary by role for each gender studied. Our results indicate that being well connected is more highly correlated with success for women than men for most of the institutions we studied. We believe these results can be leveraged to improve success rates for women in the future. © 2016 IEEE.
  • Item type:Publication,
    An exploration of gender gap using advanced data science tools: actuarial research community Open PDF 
    (2020)
    Yu, M.
    ;
    Krehbiel, M.
    ;
    Thompson, S.
    ;
    Miljkovic, T.
    This paper explores the role of gender gap in the actuarial research community with advanced data science tools. The web scraping tools were employed to create a database of publications that encompasses six major actuarial journals. This database includes the article names, authors’ names, publication year, volume, and the number of citations for the time period 2005–2018. The advanced tools built as part of the R software were used to perform gender classification based on the author’s name. Further, we developed a social network analysis by gender in order to analyze the collaborative structure and other forms of interaction within the actuarial research community. A Poisson mixture model was used to identify major clusters with respect to the frequency of citations by gender across the six journals. The analysis showed that women’s publishing and citation networks are more isolated and have fewer ties than male networks. The paper contributes to the broader literature on the “Matthew effect” in academia. We hope that our study will improve understanding of the gender gap within the actuarial research community and initiate a discussion that will lead to developing strategies for a more diverse, inclusive, and equitable community. © 2020, Akadémiai Kiadó, Budapest, Hungary.