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  • Item type:Publication,
    Feed the Tree: Representation of Australia-based Academic Women at HCI Conferences
    (2020)
    McKay, D.
    ;
    Buchanan, G.
    Conference attendance is an important part of any career in academic computing, facilitating both the citations that are key for academic advancement, and the networking opportunities that result in job and collaboration opportunities. For some authors, though, the barriers to participation in academic conferences are significant: beyond having a paper accepted, logistical considerations around care responsibilities and financial considerations may limit participation. One axis along which these barriers become particularly obvious is gender: barriers to conference participation, particularly in distant locations, are higher for women, resulting in negative career impacts. Previous research established that women make up 41% of the authors for OzCHI in the five years from 2014-19, but how does this compare to other major conferences in HCI, such as CHI and DIS? We use a scientometric analysis to examine this question in this paper, finding that Australia-based women are under-represented in these conferences. This is likely to result in negative effects on the careers of female HCI academics in Australia, whether they remain here or attempt to find work abroad. © 2020 ACM.
  • Item type:Publication,
    Who am I, and who are you, and who are we? A Scientometric Analysis of Gender and Geography in HCI
    (2022)
    McKay, D.
    ;
    Zhang, H.
    ;
    Buchanan, G.
    Conference authorship, attendance and presentation is a key measure of quality in the HCI academic, yet we know conferences are not equally accessible for reasons that have nothing to do with quality. In this paper we examine two axes of diversity: gender, and geographic location (of both authors and conferences) and examine how they affect participation at major HCI conferences. Diversity in a group is associated with better outcomes from its work, and HCI has made numerous contributions to increasing representation in other communities. Reflecting on our own situation can produce recommendations for the planning of future HCI conferences, and identify challenges of representation that the HCI community should endeavor to address. © 2022 ACM.
  • Item type:Publication,
    A bibliometric approach for detecting the gender gap in computer science Open PDF 
    (2020)
    Mattauch, S.
    ;
    Lohmann, K.
    ;
    Hannig, F.
    ;
    Lohmann, D.
    ;
    Teich, J.
    Identifying female CS scientists by combining a robust bibliographic database and name filtering tools. https://youtu.be/bmy-ei13itg
  • Item type:Publication,
    A Pilot Experience to Raise Awareness Among Computer Science Undergraduates About the Gender Biases of Algorithms
    (Springer Science and Business Media Deutschland GmbH, 2023)
    Lacave, C.
    ;
    Molina, A.I.
    ;
    García-Peñalvo, F.J., García-Holgado, A.
    Many of the decisions we make are increasingly entrusted to algorithms, although there is evidence that many of them are biased, which aggravate the inequalities of the affected groups. Gender bias is considered the biggest contributor to gender stereotypes and social inequalities. To avoid this type of bias, it is necessary that future developers of algorithms be aware of its existence. This work describes an experience carried out to make Computer Science undergraduates aware of the gender biases of algorithms and the consequences they can have. The results reveal that the main objective has been raised. From the gender segregation of the data collected, previous findings are also confirmed: the need for gender analysis in science, and the greater awareness of girls of the need to address the gender gap in Computer Science studies. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
  • 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,
    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 disparities in science? Dropout, productivity, collaborations and success of male and female computer scientists Open PDF 
    (World Scientific Publishing Co. Pte Ltd, 2018)
    Jadidi, M.
    ;
    Karimi, F.
    ;
    Lietz, H.
    ;
    Wagner, C.
    Scientific collaborations shape ideas as well as innovations and are both the substrate for, and the outcome of, academic careers. Recent studies show that gender inequality is still present in many scientific practices ranging from hiring to peer-review processes and grant applications. In this work, we investigate gender-specific differences in collaboration patterns of more than one million computer scientists over the course of 47 years. We explore how these patterns change over years and career ages and how they impact scientific success. Our results highlight that successful male and female scientists reveal the same collaboration patterns: Compared to scientists in the same career age, they tend to collaborate with more colleagues than other scientists, seek innovations as brokers and establish longer-lasting and more repetitive collaborations. However, women are on average less likely to adopt the collaboration patterns that are related with success, more likely to embed into ego networks devoid of structural holes, and they exhibit stronger gender homophily as well as a consistently higher dropout rate than men in all career ages. © 2018 The Author(s).
  • Item type:Publication,
    Advance Gender Prediction Tool of First Names and its Use in Analysing Gender Disparity in Computer Science in the UK, Malaysia and China
    (Institute of Electrical and Electronics Engineers Inc., 2018)
    Zhao, H.
    ;
    Kamareddine, F.
    Global gender disparity in science is an unsolved problem. Predicting gender has an important role in analysing the gender gap through online data. We study this problem within the UK, Malaysia and China. We enhance the accuracy of an existing gender prediction tools of names that can predict the sex of Chinese characters and English characters simultaneously and with more precision. During our research, we found that there is no free gender forecasting tool to predict an arbitrary number of names. We addressed this shortcoming by providing a tool that can predict an arbitrary number of names with free requests. We demonstrate our tool through a number of experimental results. We show that this tool is better than other gender prediction tools of names for analysing social problems with big data. In our approach, lists of data can be dynamically processed and the results of the data can be displayed with a dynamic graph. We present experiments of using this tool to analyse the gender disparity in computer science in the UK, Malaysia and China. © 2017 IEEE.
  • Item type:Publication,
    Gender Differences in Collaboration Patterns in Computer Science Open PDF 
    (2022)
    Yamamoto, J.
    ;
    Frachtenberg, E.
    The research discipline of computer science (CS) has a well-publicized gender disparity. Multiple studies estimate the ratio of women among publishing researchers to be around 15–30%. Many explanatory factors have been studied in association with this gender gap, including differences in collaboration patterns. Here, we extend this body of knowledge by looking at differences in collaboration patterns specific to various fields and subfields of CS. We curated a dataset of nearly 20,000 unique authors of some 7000 top conference papers from a single year. We manually assigned a field and subfield to each conference and a gender to most researchers. We then measured the gender gap in each subfield as well as five other collaboration metrics, which we compared to the gender gap. Our main findings are that the gender gap varies greatly by field, ranging from 6% female authors in theoretical CS to 42% in CS education; subfields with a higher gender gap also tend to exhibit lower female productivity, larger coauthor groups, and higher gender homophily. Although women published fewer single-author papers, we did not find an association between single-author papers and the ratio of female researchers in a subfield. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
  • Item type:Publication,
    Mind the gap gender and computer science conferences
    (2018)
    Van Herck, S.
    ;
    Fiscarelli, A.M.
    Computer science research areas are often arbitrarily defined by researchers themselves based on their own opinions or conference rankings. First, we aim to classify conferences in computer science in an automated and objective way based on topic modelling. We then study the topic relatedness of research areas to identify isolated disciplinary silos and clusters that display more interdisciplinarity and collaboration. Furthermore, we compare career length, publication growth rate and collaboration patterns for men and women in these research areas. © IFIP International Federation for Information Processing 2018.