PUBBLICAZIONE/PUBLICATION

Permanent URI for this communityhttps://gendermore.unimore.it/handle/123456789/1575

Browse

Settings

Subject: search.filters.subject.Information Retrieval

Search Results

Now showing 1 - 2 of 2
  • Item type:Publication,
    Using Bibliometric Analysis to Measure and Understand the Gender Gap in Published Computing Books: Gender Gap in Computer Science Open PDF 
    (2021)
    Kadriu, A.
    ;
    Sahatqija, K.
    ;
    Abazi-Bexheti, L.
    The purpose of the research presented in this paper is the investigation of the gender gap in published computing books. The book titles from the DBLP computer science bibliography were the basis for this investigation. The conducted research involves co-authorship network exploration using social network analysis methods, as well as content learning by keyword extraction and ranking from book titles. The findings show that female authors tend to publish fewer books in computing than their male colleagues, and there is a huge gap of women regarding the collaboration. There are just two women names within the 50 author names with the highest social network top metrics, indicating collaboration. Regarding the extracted keywords, though there are differences, results do not show some huge divergences when it comes to the used language for computing titles. Copyright © 2021, IGI Global.
  • Item type:Publication,
    Gender Representation on Journal Editorial Boards in the Mathematical Sciences Open PDF 
    (Public Library of Science, 2016)
    Topaz, Chad M.
    ;
    Sen, Shilad
    We study gender representation on the editorial boards of 435 journals in the mathematical sciences. Women are known to comprise approximately 15% of tenure-stream faculty positions in doctoral-granting mathematical sciences departments in the United States. Compared to this group, we find that 8.9% of the 13067 editorships in our study are held by women. We describe group variations within the editorships by identifying specific journals, subfields, publishers, and countries that significantly exceed or fall short of this average. To enable our study, we develop a semi-automated method for inferring gender that has an estimated accuracy of 97.5%. Our findings provide the first measure of gender distribution on editorial boards in the mathematical sciences, offer insights that suggest future studies in the mathematical sciences, and introduce new methods that enable large-scale studies of gender distribution in other fields.