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  • Item type:Publication,
    Predicting the future impact of Computer Science researchers: Is there a gender bias? Open PDF 
    (2022)
    Kuppler, M.
    The advent of large-scale bibliographic databases and powerful prediction algorithms led to calls for data-driven approaches for targeting scarce funds at researchers with high predicted future scientific impact. The potential side-effects and fairness implications of such approaches are unknown, however. Using a large-scale bibliographic data set of N = 111,156 Computer Science researchers active from 1993 to 2016, I build and evaluate a realistic scientific impact prediction model. Given the persistent under-representation of women in Computer Science, the model is audited for disparate impact based on gender. Random forests and Gradient Boosting Machines are used to predict researchers’ h-index in 2010 from their bibliographic profiles in 2005. Based on model predictions, it is determined whether the researcher will become a high-performer with an h-index in the top-25% of the discipline-specific h-index distribution. The models predict the future h-index with an accuracy of R2= 0.875 and correctly classify 91.0% of researchers as high-performers and low-performers. Overall accuracy does not vary strongly across researcher gender. Nevertheless, there is indication of disparate impact against women. The models under-estimate the true h-index of female researchers more strongly than the h-index of male researchers. Further, women are 8.6% less likely to be predicted to become high-performers than men. In practice, hiring, tenure, and funding decisions that are based on model predictions risk to perpetuate the under-representation of women in Computer Science. © 2022, The Author(s).
  • Item type:Publication,
    Making sense of glass ceiling: A bibliometric analysis of conceptual framework, intellectual structure and research publications Open PDF 
    (Cogent OA, 2023)
    Singh, S.
    ;
    Sharma, C.
    ;
    Bali, P.
    ;
    Sharma, S.
    ;
    Shah, M.A.
    In life and employment, women face discrimination. The glass ceiling prohibits women from obtaining top management and leadership positions in male-dominated sectors. This study has conducted bibliometric analyses to analyze and highlight the glass ceiling literature. Authors searched international conferences and journals for relevant papers. This study evaluates 1,199 publications from the Scopus database’s core collection between 1988 and 2021 to visualize the Glass ceiling. This research analyses co-occurrence, co-citation, and bibliographic coupling to discover the field’s most important subjects, authors, and works. The extent of the glass ceiling, current publication patterns, significant regions, key articles, and critical publications were also studied. This research reveals that the glass ceiling is evolving. This research delivers vital insights and conclusions on the glass ceiling’s future. Also, rich and developing nations have significant knowledge differences. © 2023 The Author(s). This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license.