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  • Item type:Publication,
    Does a gender-neutral name associate with the research impact of a scientist? Open PDF 
    (2022)
    Zhang, N.
    ;
    He, G.
    ;
    Shi, D.
    ;
    Zhao, Z.
    ;
    Li, J.
    Women have long suffered in both academia and industry from lower status, underrepresentation, unequal treatment, and other challenges based on gender. This paper explores whether a gender-neutral name is associated with the research impact of a scientist by examining three years of citations. The data for this study are derived from publications indexed in ISI's Web of Science (WoS) database from 2009 to 2015. By assigning a name neutralization index score to each author, we investigate the relationship between the neutralization index and citations and find the following results: (1) generally, the more neutral the name, the more citations the publications received; (2) the neutral effect was more pronounced for authors with feminine names than for authors with masculine names. © 2022
  • 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.