PUBBLICAZIONE/PUBLICATION

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

Browse

Settings

Discipline: search.filters.discipline.Computer ScienceSubject: search.filters.subject.Collaboration

Search Results

Now showing 1 - 4 of 4
  • 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.