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Now showing 1 - 9 of 9
  • 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,
    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,
    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.
  • Item type:Publication,
    Gender in 30 Years of IEEE Visualization Open PDF 
    (2022)
    Tovanich, N.
    ;
    Dragicevic, P.
    ;
    Isenberg, P.
    We present an exploratory analysis of gender representation among the authors, committee members, and award winners at the IEEE Visualization (VIS) conference over the last 30 years. Our goal is to provide descriptive data on which diversity discussions and efforts in the community can build. We look in particular at the gender of VIS authors as a proxy for the community at large. We consider measures of overall gender representation among authors, differences in careers, positions in author lists, and collaborations. We found that the proportion of female authors has increased from 9% in the first five years to 22% in the last five years of the conference. Over the years, we found the same representation of women in program committees and slightly more women in organizing committees. Women are less likely to appear in the last author position, but more in the middle positions. In terms of collaboration patterns, female authors tend to collaborate more than expected with other women in the community. All non-gender related data is available on https://osf.io/ydfj4/ and the gender-Author matching can be accessed through https://nyu.databrary.org/volume/1301. © 1995-2012 IEEE.
  • Item type:Publication,
    Scientometric Analysis of Interdisciplinary Collaboration and Gender Trends in 30 Years of IEEE VIS Publications Open PDF 
    (2022)
    Sarvghad, A.
    ;
    Franqui-Nadal, R.
    ;
    Reznik-Zellen, R.
    ;
    Chawla, R.
    ;
    Mahyar, N.
    We present the results of a scientometric analysis of 30 years of IEEE VIS publications between 1990-2020, in which we conducted a multifaceted analysis of interdisciplinary collaboration and gender composition among authors. To this end, we curated BiblioVIS, a bibliometric dataset that contains rich metadata about IEEE VIS publications, including 3032 papers and 6113 authors. One of the main factors differentiating BiblioVIS from similar datasets is the authors' gender and discipline data, which we inferred through iterative rounds of computational and manual processes. Our analysis shows that, by and large, inter-institutional and interdisciplinary collaboration has been steadily growing over the past 30 years. However, interdisciplinary research was mainly between a few fields, including Computer Science, Engineering and Technology, and Medicine and Health disciplines. Our analysis of gender shows steady growth in women's authorship. Despite this growth, the gender distribution is still highly skewed, with men dominating (~75%) of this space. Our predictive analysis of gender balance shows that if the current trends continue, gender parity in the visualization field will not be reached before the third quarter of the century (~2070). Our primary goal in this work is to call the visualization community's attention to the critical topics of collaboration, diversity, and gender. Our research offers critical insights through the lens of diversity and gender to help accelerate progress towards a more diverse and representative research community. IEEE
  • Item type:Publication,
    Gender, science, and academic rank: Key issues and approaches Open PDF 
    (2020)
    Fox, M.F.
    In the social study of science, gender is a critical research site because relations of gender are hierarchical and inequality is a central feature of science. The focus here is on a key dimension of gender and scientific careers: academic rank, particularly that of full professor. This article concentrates on quantitative and qualitative approaches that have occurred in two focal problem areas related to gender, science, and rank: collaboration patterns and evaluative practices. The approaches encompass analyses of large and small groups and comparative cases, with surveys, bibliometrics, experiments, and interviews. This breadth of approaches reflects a search for explanations of the pervasive and persistent relationships between gender and academic rank. The analyses presented here point to the complexities of gender disparities in collaboration. These appear in team compositions, divisions of labor and power dynamics, integration into departmental units, and international coauthorship. The analyses also reveal ways that limited clarity in evaluation bears on gender disparities. Continuing understandings of gender, science, and rank will result in multi level analyses: those at organizational levels along with those of individual scientists. © 2020 Mary Frank Fox. Published under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.
  • Item type:Publication,
    Understanding gender equity in author order assignment Open PDF 
    (2018)
    Early, K.
    ;
    Hammer, J.
    ;
    Hofmann, M.
    ;
    Rode, J.A.
    ;
    Wong, A.
    Academic success and promotion are heavily infuenced by publication record. In many felds, including computer science, multi-author papers are the norm. Evidence from other felds shows that norms for ordering author names can infuence the assignment of credit. We interviewed 38 students and faculty in human-computer interaction (HCI) and machine learning (ML) at two institutions to determine factors related to assignment of author order in collaborative publication in the feld of computer science. Interview outcomes informed metrics for our bibliometric analysis of gender and collaboration in papers published between 1996 and 2016 in three top HCI and ML conferences. Based on our fndings, we make recommendations for assignment of credit in multi-author papers and interpretation of author order, particularly with respect to how these factors afect women. © 2018 Association for Computing Machinery.
  • Item type:Publication,
    Trends in authorship by women at Canadian universities 2006 to 2019 Open PDF 
    (Canadian Association for Information Science - Association canadienne des sciences de l'information, 2021)
    Demaine, Jeffrey
    Despite much progress since the mid-20th century, there still exists a disparity in the number of female academics relative to their male colleagues. This gender gap has come under increased focus as universities take steps to foster diversity and inclusiveness. Bibliometrics can provide a window into the gender disparity in research by measuring the metadata of academic publications. By determining the ratio of female to male authors, the gender bias at the level of the institution can be quantified. This study examines the proportion of female authors of academic articles at thirty Canadian universities across five broad fields of research.
  • Item type:Publication,
    Assessment of gender divide in scientific communities Open PDF 
    (2021)
    De Nicola, A.
    ;
    D’Agostino, G.
    Increasing evidence of women’s under-representation in some scientific disciplines is prompting researchers to expand our understanding of this social phenomenon. Moreover, any countermeasures proposed to eliminate this under-representation should be tailored to the actual reasons for this different participation. Here, we take a multi-dimensional approach to assessing gender differences in science by representing scientific communities as social networks, and using data analytics, complexity science methods, and semantic methods to measure gender differences in the context, the attitude and the success of scientists. We apply this approach to four scientific communities in the two fields of computer science and information systems using the network of authors at four different conferences. For each discipline, one conference is based in Italy and attracts mostly Italians, while one conference is international in both location and participants. The present paper provides evidence against common narratives that women’s under-representation is due to women’s limited skills and/or less social centrality. © 2021, The Author(s).