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Organizing Committee
University of Colorado at Boulder
Harvey Mudd College (Claremont, CA, US)
Abstract
The goal of this workshop is to introduce participants to interdisciplinary collaborations and conversations in network science that are advancing social justice research through the study of social structures. This workshop will bring together social scientists, digital humanists, computational scientists, and mathematicians with experience in network theory and network analysis in social systems. This workshop will also showcase how mixed methods research (which combines qualitative analysis and quantitative analysis) with multidisciplinary perspectives leads to deeper insights and more ethical and responsible approaches. Workshop organizers will lead tutorials in the mathematics of network theory, finding and working with network data, and qualitative methods for networks in the social sciences. We will emphasize and showcase the use of a critical lens throughout the process, from model building to data collection and analysis, connecting us to a broader dialogue about algorithmic justice and the potential benefits and pitfalls of mathematical models. Participants will also have the option to engage in virtual interdisciplinary working groups continuing after the workshop, focused on problems in application areas including community organizing, education, social media and information dissemination, and healthcare.
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Workshop Schedule
Monday, June 27, 2022
8:30 - 8:50 AM EDT
Check In
11th Floor Collaborative Space
8:50 - 9:00 AM EDT
Welcome
11th Floor Lecture Hall
Brendan Hassett, ICERM/Brown University
9:00 - 10:00 AM EDT
Mathematical and Computational Approaches to Social Justice: A Practical On-Ramp
Tutorial - 11th Floor Lecture Hall
Chad Topaz, Institute for the Quantitative Study of Inclusion, Diversity, and Equity
Abstract
This tutorial provides an overview of some concepts, tools, and approaches relevant to quantitative work in the social justice sphere. We will work together to answer questions like the following: What does research at the interface of data science and social justice even look like? What are the theoretical underpinnings of this work? How can one acquire, clean, explore, visualize, and analyze relevant data? What are typical technical and theoretical challenges a researcher might encounter? What does ethical research look like? What opportunities exist for mathematical sciences researchers to apply their skills to promote justice?
10:15 - 10:45 AM EDT
Coffee Break
11th Floor Collaborative Space
10:45 - 11:45 AM EDT
Mathematical and Computational Approaches to Social Justice: A Practical On-Ramp
Tutorial - 11th Floor Lecture Hall
Chad Topaz, Institute for the Quantitative Study of Inclusion, Diversity, and Equity
Abstract
This tutorial provides an overview of some concepts, tools, and approaches relevant to quantitative work in the social justice sphere. We will work together to answer questions like the following: What does research at the interface of data science and social justice even look like? What are the theoretical underpinnings of this work? How can one acquire, clean, explore, visualize, and analyze relevant data? What are typical technical and theoretical challenges a researcher might encounter? What does ethical research look like? What opportunities exist for mathematical sciences researchers to apply their skills to promote justice?
12:00 - 2:00 PM EDT
Lunch/Free Time
2:00 - 3:00 PM EDT
Project Introductions
Group Presentations - 11th Floor Lecture Hall
Nathan Alexander, Morehouse College
Philip Chodrow, University of California, Los Angeles
Michelle Feng, CalTech
Chad Topaz, Institute for the Quantitative Study of Inclusion, Diversity, and Equity
Abstract
Each group leader will be given 15 minutes to introduce their project.
3:15 - 3:45 PM EDT
Coffee Break
11th Floor Collaborative Space
3:45 - 5:00 PM EDT
Group Work
11th Floor Break Out Space - TBA
5:00 - 6:30 PM EDT
Reception
11th Floor Collaborative Space
Tuesday, June 28, 2022
9:00 - 10:00 AM EDT
An introduction to network analysis and modeling with applications to social contagion processes
Tutorial - 11th Floor Lecture Hall
Juan Restrepo, University of Colorado Boulder
Abstract
Networks can be used to study and create models of social, engineering, and biological systems. In this tutorial I will introduce basic concepts and methods of network analysis and provide some examples of simple social system models. Some of the topics that will be discussed are methods of representing networks, basic characteristics of network structure, tutorials on importing network datasets and generating synthetic networks, and examples of various social contagion models. The goal of this tutorial is to give the audience the tools to create and explore basic models of social dynamics on synthetic or existing network datasets.
10:15 - 10:45 AM EDT
Coffee Break
11th Floor Collaborative Space
Projects
Project 1: The Nobel Prize Selection Pipeline: Race, Gender, and Network Analysis
Chad Topaz (Institute for the Quantitative Study of Inclusion, Diversity, and Equity)
First awarded 121 years ago, the Nobel Prizes are arguably the most famous and prestigious prizes in the world for contributions to humankind. Unfortunately, the group of Nobel laureates does not itself reflect humankind's diversity. The exclusion of minoritized/marginalized groups among prizewinners is already well-known. What lacks is an understanding of the specific loci of decision-making responsible for this exclusion. In this research-to-action project, we will gather publicly available data not just about Nobel Prize winners, but about all known nominees, nominators, and members of the prize selection committees. Focusing on gender and race/ethnicity, we will construct networks to represent the nomination-selection pipeline, looking for patterns of demographic association and other insights that might shed light on mechanisms of exclusion. Depending on the results and conclusions of the study, we will work to leverage our results into change. You do not need to have any particular mathematical or computational background to participate in this project, though experience programming in a language such as R or Python will be very helpful. This project is best suited for participants who are enthusiastic about learning and/or applying data scraping and network analysis techniques, and who are accepting of the idea of translating research results into activism, when warranted.
Project 2: Dynamics of Female Gender Representation in Mathematical Subfields
Philip Chodrow (University of California, Los Angeles)
Academic mathematics has a long and well-documented history of excluding female, queer, BIPOC, disabled, and first-generation scholars from career advancement. Math is not a monolith, however. Different institutions and mathematical subfields have experienced different degrees of success in diversifying their faculty and PhD cohorts. In this project, we will study the evolution of female gender representation in mathematical institutions and subfields. We will use a data set compiled from the Mathematics Genealogy Project, combined with inferred gender from several automated services. This data was collected by Ben Brill (UCLA), with guidance from Phil Chodrow (UCLA), Mason Porter (UCLA), and Heather Zinn Brooks (Harvey Mudd).
Using this data set, we will build understanding of how some subfields and institutions have reached relatively high levels of female gender representation, while others remain badly male-dominated. We will study the possible roles of accumulation mechanisms, homophily, and prestige as mechanisms supporting or inhibiting female gender representation in mathematics. A long-term aim of this work is to develop actionable strategies to promote inclusive representation by leveraging these mechanisms.
Useful areas of expertise for this project include network analysis and modeling, inferential statistics, dynamical systems, and data visualization.
Using this data set, we will build understanding of how some subfields and institutions have reached relatively high levels of female gender representation, while others remain badly male-dominated. We will study the possible roles of accumulation mechanisms, homophily, and prestige as mechanisms supporting or inhibiting female gender representation in mathematics. A long-term aim of this work is to develop actionable strategies to promote inclusive representation by leveraging these mechanisms.
Useful areas of expertise for this project include network analysis and modeling, inferential statistics, dynamical systems, and data visualization.
