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  • Item type:Publication,
    Digital technology helps remove gender bias in academia Open PDF 
    (2021)
    Fortin, J.
    ;
    Bartlett, B.
    ;
    Kantar, M.
    ;
    Tseng, M.
    ;
    Mehrabi, Z.
    Science attempts to be a meritocracy; however, in recent years, there has been increasing evidence for systematic gender bias against women. This bias is present in many metrics commonly used to evaluate scientific productivity, which in turn influences hiring and career success. Here we explore a new metric, the Altmetric Attention Score, and find no evidence of bias across many major journals (Nature, PNAS, PLOS One, New England Journal of Medicine, Cell, and BioRxiv), with equal attention afforded to articles authored by men and women alike. The exception to this rule is the journal Science, which has marked gender bias against women in 2018, equivalent to a mean of 88 more tweets or 11 more news articles and a median of 20 more tweets or 3 more news articles for male than female first authors. Our findings qualify Altmetric, for many types and disciplines of journals, as a potentially unbiased measure of science communication in academia and suggest that new technologies, such as those on which Altmetric is based, might help to democratize academic evaluation. © 2021, Akadémiai Kiadó, Budapest, Hungary.
  • 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 Based on a review of the past 20 y of data, we suggest that some of these claims are no longer valid and, if uncritically accepted as current causes of women's lack of progress, can delay or prevent understanding of contemporary determinants of women's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.