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Race and Collaboration in Computer Science: A Network Science Approach

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Conference

2023 ASEE Annual Conference & Exposition

Location

Baltimore , Maryland

Publication Date

June 25, 2023

Start Date

June 25, 2023

End Date

June 28, 2023

Conference Session

Equity, Culture & Social Justice in Education Division (EQUITY) Technical Session 9

Tagged Divisions

Equity and Culture & Social Justice in Education Division (EQUITY)

Tagged Topic

Diversity

Page Count

15

DOI

10.18260/1-2--44009

Permanent URL

https://peer.asee.org/44009

Download Count

281

Paper Authors

biography

Crystal E. Peoples Duke University Orcid 16x16 orcid.org/0000-0001-6312-3495

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I am currently a Postdoctoral Research Associate with the Alliance for Identity-Inclusive Computing Education (AiiCE) at Duke University. My research interests include the areas of racial inequality, social networks, higher education, and knowledge creation. Currently, my research focuses on how racialized networks in computer science disproportionately advantage whites and leads to racialized knowledge production and legitimization. I received a B.S. in Mathematics from Longwood University in 2012, an M.S. in Sociology with graduate minors in Mathematics and Statistics from Iowa State University in 2015, and a Ph.D. in Sociology from Duke University in 2022.

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biography

Alicia Nicki Washington, Ph.D. Duke University Orcid 16x16 orcid.org/0000-0001-9180-6234

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Dr. Nicki Washington is a professor of the practice of computer science and gender, sexuality, and feminist studies at Duke University and the author of Unapologetically Dope: Lessons for Black Women and Girls on Surviving and Thriving in the Tech Field. She is currently the director of the Cultural Competence in Computing (3C) Fellows program and the NSF-funded Alliance for Identity-Inclusive Computing Education (AiiCE). She also serves as senior personnel for the NSF-funded Athena Institute for Artificial Intelligence (AI). Her career in higher education began at Howard University as the first Black female faculty member in the Department of Computer Science. Her professional experience also includes Winthrop University, The Aerospace Corporation, and IBM. She is a graduate of Johnson C. Smith University (B.S., ‘00) and North Carolina State University (M.S., ’02; Ph.D., ’05), becoming the first Black woman to earn a Ph.D. in computer science at the university and 2019 Computer Science Hall of Fame Inductee.

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Shaundra Bryant Daily Duke University Orcid 16x16 orcid.org/0000-0002-6612-2049

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Shaundra B. Daily is a professor of practice in Electrical and Computer Engineering & Computer Science at Duke University and Levitan Faculty Fellow, Special Assistant to the Vice Provosts. Prior to joining Duke, she was an associate professor with tenure at the University of Florida in the Department of Computer & Information Science & Engineering. She also served as an associate professor and interim co-chair in the School of Computing at Clemson University. Her research focuses on the design, implementation, and evaluation of technologies, programs, and curricula to support diversity, equity, and inclusion in STEM fields. Currently, through this work, she is the Backbone Director for the Alliance for Identity-Inclusive Computing Education as well as Education and Workforce Director for the Athena AI Institute. Having garnered over $40M in funding from public and private sources to support her collaborative research activities, Daily’s work has been featured in USA Today, Forbes, National Public Radio, and the Chicago Tribune. Daily earned her B.S. and M.S. in Electrical Engineering from the Florida Agricultural and Mechanical University – Florida State University College of Engineering, and an S.M. and Ph.D. from the MIT Media Lab.

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Abstract

This research paper discusses the creation and analysis of the collaboration networks of computer scientists in the context of race. It is well known that there is a lack of racial diversity in computer science (CS). Recent events such as the Black Lives Matter protests of 2020 led to various organizational commitments to antiracism across the tech industry as well as academia. Although there is an increased focus on the systemic inequalities resulting from technologies such as healthcare, recidivism, and facial recognition software, there is also a need to examine the inequalities present in the professional trajectories of computer scientists from historically excluded racial groups. Analyzing collaboration networks (i.e., mapped connections between scholars and their coauthors) through the lens of race can provide further insight into the inequities present in computing environments.

In this work, data from the dblp computer science bibliography was used to create the collaboration networks of a sample of 147 Black and Latinx CS doctorate recipients. Specifically, we analyzed the resulting network for each recipient, where two authors are connected by an edge if they coauthored a publication. Typical network properties such as degree (the average number of unique coauthors), density (the proportion of possible coauthorships present), and clustering (the degree to which scholars coauthor with the same people) are investigated and compared to analogous measures reported in the discipline’s overall collaboration networks that have neglected to consider race [1], [2]. Using the lens of explicit and implicit marginalization of scholars of color, we posit that this analysis uncovers “hidden” structural mechanisms that impact the success of computer scientists of color. That is, the overrepresentation of CS Ph.D.’s identifying as white or Asian leads to an “average” or “typical” measure of a computer scientist’s network that is heavily skewed in their favor.

Since research correlates collaboration networks with scholarly productivity, citation counts, and career development and success, this analysis demonstrates the far-reaching impact that a lack of diversity in CS has on Black and Latinx scholars and, further, provides opportunities to influence future intervention strategies designed to correct hidden mechanisms impeding their success.

[1] M. Franceschet, "Collaboration in Computer Science: A Network Science Approach," Journal of the American Society for Information Science and Technology, vol. 62, iss. 10, pp. 1992-2012, 2011.

[2] A.M. Jaramillo, H.T.P. Williams, N. Perra, and R. Menezes, “The Community Structure of Collaboration Networks in Computer Science and its Impact on Scientific Production and Consumption,” arXiv:2207.09800v1 [cs.SI], July 2022.

Peoples, C. E., & Washington,, A. N., & Daily, S. B. (2023, June), Race and Collaboration in Computer Science: A Network Science Approach Paper presented at 2023 ASEE Annual Conference & Exposition, Baltimore , Maryland. 10.18260/1-2--44009

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