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  • 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,
    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,
    Gender differences in citation impact for 27 fields and six english-speaking countries 1996–2014 Open PDF 
    (2020)
    Thelwall, M.
    Initiatives addressing the lack of women in many academic fields, and the general lack of senior women, need to be informed about the causes of any gender differences that may affect career progression, including citation impact. Previous research about gender differences in journal article citation impact has found the direction of any difference to vary by country and field, but has usually avoided discussions of the magnitude and wider significance of any differences and has not been systematic in terms of fields and/or time. This study investigates differences in citation impact between male and female first-authored research for 27 broad fields and six large English-speaking countries (Australia, Canada, Ireland, New Zealand, the UK, and the USA) from 1996 to 2014. The results show an overall female first author citation advantage, although in most broad fields it is reversed in all countries for some years. International differences include Medicine having a female first author citation advantage for all years in Australia, but a male citation advantage for most years in Canada. There was no general trend for the gender difference to increase or decrease over time. The average effect size is small, however, and unlikely to have a substantial influence on overall gender differences in researcher careers. © 2020 Mike Thelwall. Published under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.
  • Item type:Publication,
    Representation of women in HPC conferences
    (2021)
    Frachtenberg, E.
    ;
    Kaner, R.D.
    Women are acutely underrepresented in the HPC workforce. Addressing this gap requires accurate metrics on the representation of women and its associated factors. The goal of this paper is to provide current, broad, and reproducible data on this gender gap. Specifically, this study provides in-depth statistics on women s representation in HPC conferences, especially for authors of peer-reviewed papers, who serve as the keystone for future advances in the field. To this end, we analyzed participant data from nine HPC and HPC-related peer-reviewed conferences. In addition to gender distributions, we looked at post-publication citation statistics of the papers and authors research experience, country, and work sector. Our main finding is that women represent only 10% of all HPC authors, with large geographical variations and small variations by sector. Representation is particularly low at higher experience levels. This 10% ratio is lower than even the 20 30% ratio in all computer science. © 2021 IEEE Computer Society. All rights reserved.
  • 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).
  • Item type:Publication,
    A Longitudinal View of Gender Balance in a Large Computer Science Program
    (2020)
    Baer, A
    ;
    DeOrio, A
    ;
    ASSOC COMP MACHINERY
    Computer Science has a persistent lack of women's participation. In order to best effect change, we require a more fine-grain analysis of the gender disparity as it changes throughout an undergraduate Computer Science curriculum. In this paper, we use a quantitative approach to highlight, with greater specificity, the point in an undergraduate career where gender balance changes. We also examine the role of grades in students' decisions to stay in the course sequence. Our goal is to enable targeted interventions that will make Computer Science a more welcoming discipline. Our study examines 30,890 unique student records over ten years at a large, public research institution. The records include students who took a Computer Science course over the past ten years. The dataset contains information about gender, majors, minors, academic level, and GPA. The dataset also includes a record from each course taken by each student and their final grade. We observed a modest increase in women's participation in all Computer Science courses over the past ten years. Despite this increase, the gender disparity is still large. Through our analysis, we found that women consistently choose not to continue through the Computer Science sequence at a higher rate than men. This higher attrition could be linked to women receiving lower grades in most introductory CS courses despite having the same or higher GPAs than men. Our results reveal specific areas where intervention can be the most effective in changing the stubborn gender disparity in Computer Science.