Organizing Committee
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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Confirmed Speakers & Participants

Talks will be presented virtually or in-person as indicated in the schedule below.

  • Speaker
  • Poster Presenter
  • Attendee
  • Virtual Attendee

Workshop Schedule

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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
  • 10:45 - 11:45 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.
  • 12:00 - 2:00 pm EDT
    Lunch/Free Time
  • 2:00 - 3:00 pm EDT
    Group Work
    11th Floor Break Out Space - TBA
  • 3:00 - 3:30 pm EDT
    Coffee Break
    11th Floor Collaborative Space
  • 3:30 - 5:00 pm EDT
    Group Work
    11th Floor Break Out Space - TBA
Wednesday, June 29, 2022
  • 9:00 - 10:00 am EDT
    Policing police networks: An invitation
    11th Floor Lecture Hall
    • Tian An Wong, University of Michigan-Dearborn
    Abstract
    40 minutes for talk & 20 minutes for discussion
    In the summer of 2020, a large subset of the mathematical community agreed to boycott collaboration with the police. But how exactly have mathematicians collaborated with the police? In this talk, I will present a selective overview of predictive policing and predicting police, with a focus on network approaches, ranging from social network analysis of crime to network models of police misconduct, with a view towards a critical mathematical theory of policing. In doing so, I will make the case that there remains much interesting and urgent work to be done in this space.
  • 10:15 - 10:45 am EDT
    Coffee Break
    11th Floor Collaborative Space
  • 10:45 am - 12:00 pm EDT
    Group Work
    11th Floor Break Out Space - TBA
  • 12:00 - 2:00 pm EDT
    Lunch/Free Time
  • 2:00 - 3:00 pm EDT
    Data Feminism in Action
    11th Floor Lecture Hall
    • Virtual Speaker
    • Lauren Klein, Emory University
    Abstract
    40 minutes for talk & 20 minutes for discussion
    What is data feminism? How is feminist thinking being incorporated into data-driven work? And how are scholars in the humanities and social sciences, in particular, bringing together data science and feminist theory in their research? Drawing from her recent book, Data Feminism (MIT Press, 2020), coauthored with Catherine D’Ignazio, Klein will present a set of principles for doing data science that are informed by the past several decades of intersectional feminist activism and critical thought. In order to illustrate these principles, she will focus on a recent research project which involves the development of a model of lexical semantic change that, when combined with network analysis, tells a new story about antislavery activism and interracial solidarity (or the difficulties thereof) in the nineteenth-century United States. This example demonstrates how feminist thinking can indeed be operationalized into more ethical, more intentional, and more capacious data practices--particularly with respect to networks--in the digital humanities, computational social science, and beyond.
  • 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
Thursday, June 30, 2022
  • 9:00 - 10:00 am EDT
    Public Discourse on Twitter and the COVID-19 Pandemic: A Look at Two Case Studies on Black Maternal Health and #MyBodyMyChoice
    11th Floor Lecture Hall
    • Ashley Champagne, Brown University
    • Diana Grigsby-Toussaint, Brown University
    • Shahrzad Haddadan, Brown University
    • Bjorn Sandstede, Brown University
    • Justin Uhr, Brown University
    Abstract
    40 minutes for talk & 20 minutes for discussion
    How has public discourse changed on Twitter over the COVID-19 pandemic? This talk will offer two case studies: the first on #MyBodyMyChoice, a hashtag originally created to advocate for women's rights but that has now drifted towards conversations around Covid-19, and the second on how advocates for Black maternal health changed the focus of their tweets over the pandemic. co-presented by Diana Grigsby, Justin Uhr, Shahrzad Haddadan, Bjorn Sandstede
  • 10:15 - 10:45 am EDT
    Coffee Break
    11th Floor Collaborative Space
  • 10:45 - 11:45 am EDT
    Supporting Network Analysis at the Center for Digital Scholarship
    11th Floor Lecture Hall
    • Speakers
    • Ashley Champagne, Brown University
    • Patrick Rashleigh, Brown University
    • Virtual Speaker
    • Maiah Letsch, Utrecht University
    Abstract
    40 minutes for talk & 20 minutes for discussion
    The Center for Digital Scholarship at Brown University offers hands-on instruction on a variety of digital scholarship methodologies, including network analysis, and this presentation will offer a snapshot of our services. First, we will share one of the ways we introduce students to network analysis. Next, this presentation will demo how we have used network analysis for one of our projects, Stolen Relations: Recovering Stories of Indigenous Enslavement in the Americas, which is led by Professor Linford Fisher (Department of History, Brown University). The Stolen Relations project is a community-based effort to build a database of enslaved Indigenous people in order to promote greater understanding of the historical circumstances and ongoing trauma of settler colonialism. The team has used the biographical information related to enslaved Indigenous people to chart networks and relations. These are hard realities and difficult histories, but they need to be told fully so we can start to be more honest about the history of this country and think more clearly about how to make amends going forward. Network analysis is one such way we can tell part of this story.
  • 11:45 - 11:50 am EDT
    Group Photo (Immediately After Talk)
    11th Floor Lecture Hall
  • 12:00 - 2:00 pm EDT
    Lunch/Free Time
  • 2:00 - 3:00 pm EDT
    Group Work
    11th Floor Break Out Space - TBA
  • 3:00 - 3:30 pm EDT
    Coffee Break
    11th Floor Collaborative Space
  • 3:30 - 5:00 pm EDT
    Group Work
    11th Floor Break Out Space - TBA
Friday, July 1, 2022
  • 9:00 - 10:00 am EDT
    Promoting effective and enduring collaboration in networks
    11th Floor Lecture Hall
    • Speaker
    • Carrie Diaz Eaton, Bates College
    • Virtual Speaker
    • Jason Williams, Cold Spring Harbor Laboratory
    Abstract
    40 minutes for talk & 20 minutes for discussion
    We synthesize ideas and experience from research coordination networks to discuss how to foster success for the future of social justice research involving data science and computational methods. Together, we will lead discuss goals for the collaboration network. To achieve those goals, we consider conversations that the community should have as they move towards collaborative teams, introducing tools and techniques for both team communication and computational collaboration. We also ask that individuals reflect on how their personal goals and strengths will help the research team and the network.
  • 10:15 - 10:45 am EDT
    Coffee Break
    11th Floor Collaborative Space
  • 10:45 - 11:45 am EDT
    Big Challenges and Resources in Interdisciplinary Teams
    Panel Discussion - 11th Floor Lecture Hall
    • Panelists
    • Manuchehr Aminian, California State Polytechnic University, Pomona
    • Jude Higdon, QSIDE Institute
    • Katherine Kinnaird, Smith College
    • Sarah Shugars, NYU
  • 12:00 - 2:00 pm EDT
    Lunch/Free Time
  • 2:00 - 2:30 pm EDT
    Project 1 Presentation
    Group Presentations - 11th Floor Lecture Hall
    • Project Leader
    • Chad Topaz, Institute for the Quantitative Study of Inclusion, Diversity, and Equity
  • 2:45 - 3:15 pm EDT
    Project 2 Presentation
    Group Presentations - 11th Floor Lecture Hall
    • Project Leader
    • Philip Chodrow, University of California, Los Angeles
  • 3:15 - 3:45 pm EDT
    Coffee Break
    11th Floor Collaborative Space
  • 3:45 - 4:15 pm EDT
    Project 3 Presentation
    Group Presentations - 11th Floor Lecture Hall
    • Project Leader
    • Michelle Feng, CalTech
  • 4:30 - 5:00 pm EDT
    Project 4 Presentation
    Group Presentations - 11th Floor Lecture Hall
    • Project Leader
    • Nathan Alexander, Morehouse College

All event times are listed in ICERM local time in Providence, RI (Eastern Daylight Time / UTC-4).

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Associated Program Workshops

Project Descriptions

Project 1: The Nobel Prize Selection Pipeline: Race, Gender, and Network Analysis

Leadership: 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

Leadership: 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.

Project 3: Modeling the effects of housing supply on mass displacement

Leadership: Michelle Feng, CalTech

Rising housing costs are a subject of much discussion in many major cities. While policy proposals around housing generally agree that an increase in housing supply is needed, there is a great deal of disagreement on how best to achieve this increase. In this project, we aim to build on an existing quantitative literature around measuring gentrification and displacement by studying simple theoretical models of potential solutions. Specifically, we are interested in modeling the effects of policies around public, low-income, and affordable housing, as well as zoning restrictions.

This project will likely combine spatial analysis, housing policy, census data, and dynamical systems. If you have interest or expertise in these areas, please feel welcome to join!

Project 4: Theories and Methods in Problems on Distributive and Transitional Justice

Leadership: Nathan Alexander, Morehouse College

References to and use of the term 'social justice' have increased across various disciplines of study seeking to advance equity. If we take a view of equity in relation to justice that centers the ability to sustain and improve a social impact in increasingly inequitable and marginalizing contexts, many longer-term effects of movements for justice must be centered to their material outputs. There are related terms - such as distributive justice and transitional justice - that present cross-disciplinary opportunities for mathematics and data science communities to examine more concrete (read: materializing/materialized) conceptions of change and justice.

Distributive justice relates to fairness in the allocation and distribution of goods or services with a particular focus on outcomes, which may benefit inquiries in more localized contexts. Transitional justice, more broadly, considers the shift from discourses on ethics and social values to broad policies and material outputs that redress legacies of human rights abuses, especially through law and public policy. For this project, we will examine disciplinary approaches to distributive justice and transitional justice in order to consider the role of mathematics, data science, and network analysis in advancing interdisciplinary methodologies.

No prior knowledge will be needed.