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  • × author_ss:"Chen, J."
  • × year_i:[2020 TO 2030}
  1. Zheng, X.; Chen, J.; Yan, E.; Ni, C.: Gender and country biases in Wikipedia citations to scholarly publications (2023) 0.02 
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    Abstract
    Ensuring Wikipedia cites scholarly publications based on quality and relevancy without biases is critical to credible and fair knowledge dissemination. We investigate gender- and country-based biases in Wikipedia citation practices using linked data from the Web of Science and a Wikipedia citation dataset. Using coarsened exact matching, we show that publications by women are cited less by Wikipedia than expected, and publications by women are less likely to be cited than those by men. Scholarly publications by authors affiliated with non-Anglosphere countries are also disadvantaged in getting cited by Wikipedia, compared with those by authors affiliated with Anglosphere countries. The level of gender- or country-based inequalities varies by research field, and the gender-country intersectional bias is prominent in math-intensive STEM fields. To ensure the credibility and equality of knowledge presentation, Wikipedia should consider strategies and guidelines to cite scholarly publications independent of the gender and country of authors.
    Date
    22. 1.2023 18:53:32
    Type
    a
  2. Jiang, X.; Zhu, X.; Chen, J.: Main path analysis on cyclic citation networks (2020) 0.00 
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    Abstract
    Main path analysis is a famous network-based method for understanding the evolution of a scientific domain. Most existing methods have two steps, weighting citation arcs based on search path counting and exploring main paths in a greedy fashion, with the assumption that citation networks are acyclic. The only available proposal that avoids manual cycle removal is to preprint transform a cyclic network to an acyclic counterpart. Through a detailed discussion about the issues concerning this approach, especially deriving the "de-preprinted" main paths for the original network, this article proposes an alternative solution with two-fold contributions. Based on the argument that a publication cannot influence itself through a citation cycle, the SimSPC algorithm is proposed to weight citation arcs by counting simple search paths. A set of algorithms are further proposed for main path exploration and extraction directly from cyclic networks based on a novel data structure main path tree. The experiments on two cyclic citation networks demonstrate the usefulness of the alternative solution. In the meanwhile, experiments show that publications in strongly connected components may sit on the turning points of main path networks, which signifies the necessity of a systematic way of dealing with citation cycles.
    Type
    a
  3. Qin, C.; Liu, Y.; Ma, X.; Chen, J.; Liang, H.: Designing for serendipity in online knowledge communities : an investigation of tag presentation formats and openness to experience (2022) 0.00 
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    Type
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