Double and Triple Binds

A Former Codephobe’s Story

When I first read Miriam Posner’s “Some Things to Think About Before You Exhort Everyone to Code,” I thought, “THAT’S IT! As a woman of color, I have been socialized not to be able to code.” I reassured myself that I could put aside the debate of whether one must be able to code to be a digital humanist, content myself with Omeka projects, and move on with my life. For years, I maintained that stance unapologetically: No, I can’t code. I can’t code because I am a woman of color, not a white man, and I was not encouraged to do so. In the great debate between “hack” (building) and “yack” (talking) in digital humanities (Nowviskie, 66), I was Team Yack. Out-of-the-box tools like Omeka, Scalar, and Voyant were all I needed to accomplish my goal of using digital humanities to address what I had identified as the gaps and omissions in the cultural record that are being rapidly reproduced and amplified as digital humanists build the digital cultural record (Risam, New Digital Worlds, 4–6).

Fast-forward to summer 2018. The administration of Donald Trump announced a new immigration policy, instructing Customs and Border Protection (CBP) and Immigration and Customs Enforcement (ICE) to separate families arriving at the Mexico–U.S. border in search of asylum. Transforming our collective despair into action, several friends and I created a team of librarians, faculty, and graduate students, wondering if our digital humanities skills might help. The result was Torn Apart/Separados, volume 1, a series of data visualizations demonstrating the landscape of immigrant detention in the United States, created in the span of a week. As we worked, my inability to code was not an issue. My solid research skills led to our team’s clear working knowledge of the processes the government used to manage children in custody, and I took the lead on the data, curating a dataset of the locations where children were being housed by the U.S. government. My knowledge of data ethics and data visualization also figured prominently, as I repeatedly raised concerns about the children’s safety as we prototyped the project. Alex Gil built the Jekyll wrapper to house the project. Several years before, Gil had patiently spent a morning teaching me to work in the command line environment and use Jekyll, so I was able to make my contributions to Torn Apart/Separados with ease. Coder extraordinaire Moacir P. de Sá Pereira handled the bulk of the coding in Leaflet.js, a JavaScript library for mapping, and in JavaScript to create the visualizations. The experience was, in many regards, the microcosm of a perfect digital humanities collaboration: each participant brought their knowledge to the table, everyone’s expertise was respected and valued, and we successfully executed a project that was well received.3 And never once did I think, “Oh no! I can’t code.”

That quickly changed when we started work on volume 2. At the Digital Humanities 2018 conference in Mexico City, we launched Torn Apart/Separados, volume 1, and Gil and I held a design sprint and hackathon sponsored by the Humanities, Arts, Sciences, and Technology Advanced Collaboratory (HASTAC) to share our data and explore new ways of using digital humanities to address the issue of immigrant detention. Among the promising ideas was “following the money”—understanding the fiscal landscape of immigrant detention. A week after the conference, on July 4, 2018, I was poking around government websites and discovered that we could scrape all the government contracts that ICE had given out across multiple years and explore who was reaping financial gain from immigrants’ pain. Unlike the weeklong sprint to create volume 1, we took volume 2 slowly, though the process was similar. We began by scraping the data, examining it closely, and brainstorming ideas for the kinds of stories we might be able to tell. We were joined by new team members, including one with substantial coding experience. I led these conversations as we drew out complicated plans to use D3.js, a JavaScript library for data visualization. Half the time, I had no idea what our coding experts were talking about, and I did not understand the fundamentals of computation well enough to even know whether what they were proposing was feasible. But that was okay—after all, I was a woman of color, so I could not code!—until one of the coders decided they no longer had time for the project, after adding significant complexity to our design. We were faced with a choice: either we could scale back our expectations, or it was all hands on deck and, gulp, I would need to learn to code.

The answer was obvious: we should have focused on less ambitious plans. But, no, Roopika Risam, an ardent defender of the right to do digital humanities without programming knowledge, was going to learn how to code. And the team members (particularly Gil, de Sá Pereira, and Rachel Hendry) were going to have to suffer through it.

I purchased roughly every book on Amazon with the words “JavaScript,” “D3,” “Beginners,” and “Dummies” in the title. Elijah Meeks, author of D3.js in Action and founder of the Data Visualization Society, sent me a copy of his book in appreciation of our work on Torn Apart/Separados. I read the books. I asked teammates de Sá Pereira, Hendry, Gil, and my friend Scott Weingart erudite questions like, “How do I get the code into a thingy”? (Translation: Where do I execute the code?) They didn’t laugh—at least, not to my face. I printed “Hello, world.” I made rectangles with lines of code. I wondered why I was making rectangles with lines of code. I realized that my knowledge of HTML and CSS meant I was not completely starting at square one. I sat through lessons where de Sá Pereira gamely taught me how to convert CSV into JSON. I learned about the Document Object Model (DOM). I called the DOM. I yelled at the DOM. I figured out how to bind data to elements in the DOM. I created force-directed graphs. I realized I should have paid more attention in physics class. Hendry pointed out countless times that my code would not run because I had missed a semicolon. And, in the end, I created a “murderboard”-style data visualization, intentionally designing a bad data visualization to emphasize the messy connections between companies and goods and services they offered ICE.

That experience, as chaotic as it was in retrospect, was a turning point for me. No, the issue wasn’t that I had not been socialized to learn how to code—it was that I had not had a compelling enough reason to sit down and learn. Like many digital humanists, I had once attempted to “learn Python” for no reason beyond thinking, “I’m a digital humanist and thus should learn Python.” When it didn’t stick, I attributed that to race and gender, not to the fact that I did not have a strong sense of why I should learn it and what it could offer as a research tool. That all changed from my work on Torn Apart/Separados, volume 2. I realized that coding offered more control over what I could do with data than the myriad out-of-the-box tools I had been using.

The following summer, I was ready to explore the possibilities of code even further and embarked on a project called “The Global Du Bois,” a series of data visualizations intended to refute the idea that W. E. B. Du Bois’s investment in anticolonialism happened later in his life. This had been the topic of my dissertation, but I did not think it would have the impact I would like as a book. As a data visualization project, on the other hand, it could open up data-driven inquiry into the issue and serve as a resource for researchers. Knowing I could do this from my experience with Torn Apart/Separados, I spent the summer immersing myself in JavaScript and D3 from the ground up. I eagerly studied the fundamentals of computation. I realized that no, programmers do not have a head full of code just waiting to spill out—they build on their foundational knowledge, they use trial and error, and they talk with each other, one-to-one or through Stack Overflow. I built the backend of a web app using Node.js, Babel, and Webpack. I began designing the front end for the project. I prototyped some data visualizations. And once I knew I could do this, I spent the time since working on my datasets, with the goal of a 2022 launch. I bought a hoodie. I watched every Marvel movie (yes, including Thor: The Dark World). I had become a codebro. And, perhaps more critically, I became convinced that it is critical that more digital humanists of color learn to code—if it suits the methodological interventions they wish to make—because it can open a new world of possibilities in computational humanities: new directions for humanities research, new questions to ask about data, and new ways of engaging audiences.

Double (and Triple) Binds of Computational Ethnic Studies

While Jula Damerow, Abraham Gibson, and Manfred Laubichler describe the debate over whether computational humanists need to know how to code (settled with a firm “yes” in “Of Coding and Quality,” in this volume), they fail to take into account that there are a number of barriers to entry. From interventions like Posner’s blog post to the lived realities of racial disparities in STEM pipelines and the emergence of targeted interventions like Black Girls Code and the Hidden Genius Project, it may appear, at first glance, that the primary barrier to entry in computational humanities for people of color working in areas like African diaspora, Indigenous, Latinx, Asian American, postcolonial, and ethnic studies is primarily a technical one.4 However, the true barriers to the development of computational approaches within these areas lie in the broader challenges that people of color and Indigenous people in these fields have experienced within higher education. Thus, the primary issue lies in the double bind of undertaking ethnic studies and digital humanities research together and the triple bind of undertaking ethnic studies and digital humanities as people of color and Indigenous people in the academy.5

Far from simply being a matter of technical barriers to entry, the double and triple binds of computational ethnic studies lie in the structural challenges of the academy. The first is in biases within scholarly knowledge production. Computational humanities methods favor datasets and tools designed with white dominant cultures of the Global North in mind. This bias is compounded by the many factors that have shaped humanities knowledge production: the dominance of the English language, the concentration of academic capital in the Global North, and grant funding schemes that favor canonical topics with self-evident value. As Safiya Noble and Ruha Benjamin have noted, these technologies are built on embedded biases that marginalize communities of color and Indigenous communities. Because digitization schemes have focused on canonical histories and voices, reflecting long-standing biases within digital knowledge production (Risam, New Digital Worlds; Singh, “Visualizing the Uplift”), access to corpora or metadata needed for computational humanities poses a challenge for ethnic studies. Therefore, what Kim Gallon describes as the “technology of recovery” (42) is a precursor to computational humanities. Furthermore, the methodologies and workflows behind digitization efforts often fail to account for the particularities of ethnic studies materials, which in turn limits the use of computational humanities methods. In the case of HathiTrust, Nicole Brown and colleagues encountered multiple challenges identifying experiences of Black women in the 800,000 texts in the database (“Mechanized Margin to Digitized Center,” 110–3). Despite these issues, which stem from the embedded biases in computational analysis, they propose that computational approaches have promise as a technology of recovery that can expand the scope of digital humanities (Brown et al., 124–5). Undertaking this work, however, can rely on a significant amount of research and intellectual labor to create datasets as a precursor to computational analysis. In the case of “The Global Du Bois,” for example, I began work on my datasets, which required archival and secondary source research, then paused for three months to prototype with the data I had curated to ensure the data was appropriately structured for my research questions and that the project was feasible. I have since spent a year continuing to curate data. Similarly, in a related project visualizing key figures in Pan-Africanism undertaken with undergraduate students, we spent six months on archival and secondary source research to identify participants in Pan-Africanist gatherings between 1900 and 1959 just to be in the position to be able to begin data visualization.

Computational humanities, moreover, is poorly understood in the context of tenure and promotion decisions (Nyhan and Flinn), exacerbating the challenges that ethnic studies scholars already experience (Matthew). The phenomenon of working “double” is not unfamiliar to digital humanists, particularly those in faculty roles who are obligated to publish in traditional scholarly venues in addition to their digital scholarship. Those who work in computational humanities in any field understand the implicit expectation to do twice the work—write the books and articles and do the digital projects—for tenure and promotion (Wernimont and Losh). Black, brown, and Indigenous scholars, too, are frequently expected to work at least twice as hard to gain recognition within the white academy, particularly those in ethnic studies fields whose work is not viewed as comparatively rigorous (Ruiz and Machado-Casas; Onwuachi-Willig; Hernández). High-profile tenure denials like those of Dr. Lorgia García Peña at Harvard University and Paul Harris and Tolu Odumosu at the University of Virginia demonstrate that universities have proved to be anywhere between ambivalent to downright hostile to ethnic studies in general and ethnic studies of color and Indigenous scholars in particular (Chetty, Gonzales Seligmann, and Gil; Flaherty).

Those undertaking computational ethnic studies bear the burden of both demonstrating the value of ethnic studies and proving the scholarly value of computational methodologies. This is, of course, compounded harm for Black, brown, and Indigenous scholars who are additionally expected to undertake invisible diversity work, such as advising and mentoring colleagues and students of color (Risam, “Diversity Work”). Thus, the growth of computational ethnic studies is beset by the greater risk that scholars face when undertaking scholarship with experimental and poorly understood outputs because the interdisciplinary nature of their scholarship is already regarded with suspicion and ignorance (Ervin). However, this inequitable labor is magnified for those in ethnic studies and even further intensified for those of us who are scholars of color or Indigenous scholars as well. There is, thus, a triple bind for computational ethnic studies, where the additional workload (research, data curation) simply to be in the position to undertake computational analysis is accompanied by the broader challenges of evaluating and valuing digital scholarship as a form of scholarly communication and the devaluation of ethnic studies research.

A further issue is that resources for digital scholarship remain concentrated in predominantly white, research 1 institutions, limiting access to funding and training. Digital humanities and data science centers are most typically found at elite institutions and focus on topics of greatest interest to dominant white university cultures. As Quinn Dombrowski, Tassie Gniady, David Kloster, Megan Meredith-Lobay, Jeffrey Tharsen, and Lee Zickel note (“Voices from the Server Room,” in this volume), local support is crucial to facilitating computational research by humanities scholars. They note that the people providing such support must understand the nuances of translating humanities research into digital research as well as the local infrastructure needs. In the context of computational ethnic studies, effective support also requires working knowledge of the particularities of ethnic studies. With rare exceptions like the African American Digital Humanities Initiative at the Maryland Institute for Technology in the Humanities (MITH), initially led by Catherine Knight Steele and now led by Marisa Parham; the new Center for Digital Black Research led by P. Gabrielle Foreman, Shirley Moody-Turner, and Jim Casey at Penn State; and U.S. Latinx Digital Humanities led by Gabriela Baeza Ventura and Carolina Villarroel at the University of Houston, few centers emphasize the importance of supporting computational ethnic studies. Thus, training opportunities remain limited for both early career researchers and senior scholars interested in undertaking this work. In the broader landscape of humanities graduate education, there is limited guidance for computational analysis, and institutes like the Digital Humanities Summer Institute (DHSI), Humanities Intensive Learning and Teaching (HILT) institute, the Institute for Liberal Arts Digital Scholarship (ILiADS), and DREAMLab have been providing an important service in response to this gap. Despite the work of these institutes to provide bursaries and support to make their resources accessible, being able to take advantage of such opportunities relies both on additional institutional resources and favorable personal circumstances that are not universally available. This is particularly the case for ethnic studies scholars who already contend with institutional inequities in resource allocation (Prashad; Khanmalek) and those who are Black, brown, or Indigenous and thus are more likely to have caregiving responsibilities (Griffin; Szelényi and Denson).

A final challenge to the growth of computational ethnic studies is a lack of opportunities for mentorship. Many of us who undertake this work do so at universities without doctoral programs; in libraries; or in archives, galleries, and museums. We are therefore not in positions to mentor doctoral students. When we were beginning our research, we typically did not have access to advisers or committee members who worked specifically in computational ethnic studies. Rather, we had to construct our own networks of mentors and peers with the range of expertise necessary to support our work and hope for the best. The newest generation is being forced to do the same, and this is perhaps the most significant barrier to computational ethnic studies. These circumstances can be attributed to multiple factors: the casualization of academic labor and decline in tenure-track jobs; the challenges that scholars who undertake interdisciplinary work face in the academic labor market; and the skepticism that both computational humanities and ethnic studies work can engender in departmental contexts. Consequently, scholars of computational ethnic studies cannot rely on the academic genealogies that have served those in other fields. Rather, we must construct our own scholarly ecologies and lateral and nonhierarchical networks to support our work. These networks have sustained us and held us up, but the fact that we have to rely on them, rather than on academic genealogies, means that we are in danger of being shut out from the power that those genealogies accord.

Our Collective Ways Forward

Given how deeply the challenges to computational ethnic studies are embedded in the power dynamics that shape the contemporary academy, imagining how we might address them is, at times, confounding. First, if you see value in computational humanities for your research, particularly if you are a scholar of color or an Indigenous scholar, know that you can do this work (hoodie optional but strongly encouraged). Yes, you may have been socialized not to, but that doesn’t mean you can’t.

There are a number of resources that you can use to teach yourself to code. I found the free apps for Codeacademy and SoloLearn useful for ensuring I understood the fundamentals of computation. I also used the free apps for Programming Hub and Khan Academy to find coding problems for practice. Once I began wrapping my mind around those concepts, I worked through Moacir P. de Sá Pereira’s excellent free resource, The JavaScripting English Major. I chose to learn JavaScript because my interest in coding primarily involves data visualization. But with knowledge of the fundamentals of computation, as well as the emphasis on computational thinking in de Sá Pereira’s book, I have been able to translate my knowledge to work with other languages, such as Python, when necessary. Other colleagues have found the University of Toronto’s “Learn to Program: The Fundamentals” and the University of Michigan’s “Python for Everybody” free courses useful.6 I have also found the volumes published by O’Reilly (including Becoming a Better Programmer by Pete Goodliffe and Tom Stuart’s Understanding Computation) incredibly helpful after exhausting freely available resources. (If you are specifically interested in data visualization, Elijah Meeks’s D3.js in Action is indispensable.)

If you prefer learning with others and are working or studying at a university, connect with the research or teaching and learning centers on your campus to see what they offer—and express your interest in this type of programming if they are not supporting it. Talk to colleagues in computer science, information science, and data science about resources their departments offer or the possibility of auditing a course. In the realm of external resources, digital humanists have also resourcefully created their own courses, though they require access to funds for tuition and, in some cases, travel: the Programming4Humanists course has regularly run online through Texas A&M University for a number of years now and focuses on Python; Digital Mitford Coding School, which typically meets in-person, has a solid record of teaching students to work with TEI, XPath, and regular expressions (regex); and DHSI and DREAMLab are highly regarded on-site institutes.7

But the responsibility for creating the conditions to overcome the double and triple binds of computational ethnic studies cannot be borne by Black, brown, and Indigenous scholars alone. Rather, it requires collaborative efforts by all digital humanists across institutions, particularly those in positions of privilege, such as tenured jobs or access to institutional resources. For those at institutions undertaking digitization projects, consider what you are digitizing and how it reflects the influence of dominant white scholarly knowledge production; the more material that is made available through digitization in fields like African diaspora, Indigenous, Latinx, Asian, and postcolonial studies, as appropriate to cultural protocols on digitization and access to knowledge, the more content we have to work with for computational ethnic studies.

Consider how you are developing metadata schemas and the effects your practices have on inclusion, exclusion, and discovery for materials that are underrepresented in the digital cultural record. Consider the ontologies and controlled vocabularies, the metadata in digitized collections, and the metadata in library catalogs, and work toward changing subject headings. If you are developing tools, examine the biases or presumptions embedded in the tools you build and how your design practices can shift to address them. Consult with scholars and community members who have the expertise to assist with development of better metadata schema and tools—and compensate them.

If you work in a digital humanities or data science center, hold yourself and your colleagues accountable for how they are supporting computational ethnic studies. Ask how you can redistribute resources to assist scholars who do not have access to them. When you pursue grants, consider how to build in funding to support the growth of computational ethnic studies and the capacity of computational ethnic studies scholars at your campus and at other campuses. Resist models for grant funding that anoint individual scholars or projects in favor of ones that bring others in.

If you are active in your scholarly organizations, work with them to ensure that their guidelines for evaluating digital scholarship include attention to systemic racism and colonization and that they are actively working to resist these forces. In the case of publicly engaged scholarship (digital or otherwise), for example, I have been collaborating with the Modern Language Association to ensure that the practices we build into evaluation are explicitly antiracist and anticolonial. Putting these practices at the heart of guidelines and evaluation are essential to seeing them realized.

If you are working in digital humanities and committed to improving the conditions of knowledge production for your colleagues in computational ethnic studies, support the work of journals like Reviews in Digital Humanities and archipelagos, which are working to provide evaluations of digital scholarship with a strong emphasis on fields related to ethnic studies. If you are a faculty member, advocate for hiring and for the value of scholarship in computational ethnic studies. Put aside your presumptions about what scholarship—digital or otherwise—should look like, and encourage your colleagues to do the same.

The road ahead to undo the double and triple binds of computational ethnic studies is long. But at this moment, when institutions are committing to fighting systemic racism, let us hold them accountable to it and do our part to ensure that this battle extends to our own scholarly practices. The future of computational ethnic studies depends on it.

Notes

1. 1. See Sara Harrison, “Five Years of Tech Diversity Reports—and Little Progress,” Wired, October 1, 2019, https://www.wired.com/story/five-years-tech-diversity-reports-little-progress/; Galen Gruman, “The State of Ethnic Minorities in U.S. Tech: 2020,” ComputerWorld, September 21, 2020, https://www.computerworld.com/article/3574917/the-state-of-ethnic-minorities-in-us-tech-2020.html; Chandra Steele, “Women of Color Face Extra Barriers to Entering Tech Fields,” PCMag, July 8, 2020, https://www.pcmag.com/news/women-of-color-face-extra-barriers-to-entering-tech-fields.

2. 2. See Allison Scott and Alexis Martin, “Perceived Barriers to Higher Education in Science, Technology, Engineering, and Mathematics,” Journal of Women and Minorities in Science and Engineering 20, no. 3 (2014): 235–56; Kimberly McGee, “The Influence of Gender and Race/Ethnicity on Advancement in Information Technology (IT),” Information and Organization 28, no. 1 (2018): 1–36; J. A. Muñoz and I. Villanueva, “Latino STEM Scholars, Barriers, and Mental Health: A Review of the Literature,” Journal of Hispanic Higher Education (2019), https://doi.org/10.1177/1538192719892148; Maya Corneille, “Developing Culturally and Structurally Responsive Approaches to STEM Education to Advance Education Equity,” Journal of Negro Education 89, no. 1 (2020): 48–57; Maria N. Miriti, “The Elephant in the Room: Race and STEM Diversity,” BioScience 70, no. 3 (March 2020): 237–42, https://doi.org/10.1093/biosci/biz167; Sandy Marie Bonny, “Effective STEM Outreach for Indigenous Community Contexts: Getting It Right One Community at a Time,” International Journal of Innovation in Science and Mathematics Education 26, no. 2 (2018), https://openjournals.library.sydney.edu.au/index.php/CAL/article/view/12656; Fikile Nxumalo and Wanja Gitari, “Introduction to the Special Theme on Responding to Anti-Blackness in Science, Mathematics, Technology, and STEM Education,” Canadian Journal of Science, Mathematics and Technology Education 21 (2021): 226–31.

3. 3. The Torn Apart/Separados team consisted of untenured faculty, librarians, contingent faculty, and graduate students. At the time, I was an untenured assistant professor, which meant that while I was negotiating the research demands of tenure, I also had the privilege of stable, gainful employment where research was part of my job duties, which was not the case for the whole team.

4. 4. For further resources on this topic, see Black Girls Code, https://lnk.bio/blackgirlscode, accessed September 15, 2021; The Hidden Genius Project, https://www.hiddengeniusproject.org/, accessed September 15, 2021; All Star Code, https://www.allstarcode.org/, accessed September 15, 2021; Black Tech Unplugged, https://blacktechunplugged.com/, accessed September 15, 2021; digitalundivided, https://www.digitalundivided.com/, accessed September 15, 2021; People of Color in Tech, https://peopleofcolorintech.com/, accessed September 15, 2021.

5. 5. Implicitly underlying these barriers is the fraught state of labor in higher education, where 75 percent of the labor force is contingent.

6. 6. See The Javascripting English Major (https://the-javascripting-english-major.org/v1/contents); University of Toronto’s “Learn to Program: The Fundamentals” (https://www.coursera.org/learn/learn-to-program); and the University of Michigan’s “Python for Everybody” (https://www.coursera.org/specializations/python).

7. 7. See the Programming4Humanists course (https://liberalarts.tamu.edu/codhr/programs/p4h/); Digital Mitford Coding School (https://digitalmitford.wordpress.com/); DHSI (https://dhsi.org/); and DREAMLab (https://web.sas.upenn.edu/dream-lab/).

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