What Gets Counted

A Shifting Debate: Hope, Care, and Critical Possibilities

Questions about whether the humanities should be engaging and shaping computational methods have moved from if to how. Sites of debate and their intersections include the qualitative and quantitative, theory and numbers, empiricism and hermeneutics, credit and labor, to name just a few. Importantly, the conversation about collaboration, labor, and policy is being pushed in new directions. We are attuned to the broader community through online communication, yet we are also more aware of the importance of institutional and community support, which became even more important amid local and global challenges. There is, then, a need for a capacious computational humanities to remix, reimagine, and reconfigure the world of quantification and ways of knowing that can be produced.

Computational humanities (CH) is an open term, to be defined over time, in collaboration, and never fully cemented.1 It is a process that will extend far beyond the bounds of a book but which this project shapes. In this moment, the volume revolves around a formation of CH that focuses on the use and critique of computational methods to create and analyze data that animates inquiries in the humanities. This approach pays careful attention to the infrastructure and labor that shape what is possible. Employing computational methods that are informed by an awareness and interrogation of histories that both animate and haunt computation offers a way to reveal ghosts, forge connections, and conduct beautiful experiments guided by carework.2 The volume functions as a part of this definition and is open to expansion, agreement, critique, and debate itself.

Our entry into the debate comes from a particular angle. This volume emerged in the context of a series of debates, some ongoing, some new about what counts as CH and what does not, who is doing CH and who is not, and who is doing it right.3 In some cases, we wanted to move beyond questions that we felt had become stale and unproductive, while in other cases we wanted to dig in further and explore what we thought were the pressing issues that could lead to better work and better working. We chose to move past debates on what constitutes computing in the humanities and whether it is possible for computing to provide useful perspectives, particularly moving beyond writ-large accusations of neoliberalism.4 We offer no further discussion of definitions or boundaries of the digital in the humanities.5 Our own experience, especially these last few years, has taught us that there is no clear boundary between “distant” and “close” reading; between scholarship produced by the academy and labor happening beyond classroom walls; between quantitative and qualitative data; between learning to count humans and being one in relation, in the world.6

Rather, computation at its most generative demands an equal degree of interpretation. The scholarship we create is generated, designed, and cocreated by and with communities at all levels of our institutions and across international boundaries, and it is rooted in our experiences, desires, and visions of the world well beyond the ivory tower. We can turn to publications such as the Journal of Cultural Analytics as a model for bringing together method and interpretation, attention to collaboration and credit, and prioritizing access and transparency. Authors in this volume echo these positions with a consistent call for a better understanding of hermeneutics, of stakes, and of data itself. There is a long tradition of critical essays rejecting the notion of computation as having any contribution to the humanities.7 These are perspectives worth considering, but for this volume we start with the assumption that quantitative approaches have something to add; our sources can be seen from many perspectives, and one of those perspectives is as data.

A more recent debate, and one that we choose to consider more directly, is a product of the very interdisciplinarity that makes computational humanities thrilling. It amounts to asking: Who are we doing this work for, and who are we accountable to? Consider a researcher brought up in computer science, a researcher who builds their career on eight-page LaTeX pdfs published at competitively reviewed conferences, and a researcher brought up in a humanities discipline that prizes monographs above all else. Can they all relate? Can they recognize each other’s “rigor” and value it? Perhaps more important, can they fit alternative ways of working into the institutional structures that they must navigate to maintain their careers and their entire fields? Which new structures do we need to build and for whom?

Every one of these interactions, moreover, is embedded in a context of powerful social constructs such as race and gender, prestige, and geography. Describing how intersectionality (a Black feminist framework created by Kimberle Crenshaw) and identity politics (a Black feminist framework best attributed to Beverly Smith and the Combahee River Collective) shape what is possible and for whom, and how we can collectively remove barriers, undergirds these chapters.8 In this volume, we approach these questions in what we hope is a new way. What are the resources, in both expertise and infrastructure, that make computational humanities successful? How can different perspectives complement and strengthen each other? And how can we more intentionally open ourselves and our work to different experiences and different histories with data, computation, or the digital and allow it to reshape how we do our work?

A context that we did not anticipate but that saturated every aspect of this volume is the global pandemic. In obsessively tracking daily case counts and the horror of sickness and death, we were constantly reminded of both the inescapable necessity of quantification and also its fundamental inadequacy. The pandemic also highlighted the necessity of human infrastructure. Who was able to keep working uninterrupted, and who was not? We saw, inescapably, the value of support, whether it is high-performance computing or colleagues to bounce ideas off or childcare. We saw who has support, and who does not. We saw who died (Black and non-Black people of color at disproportionate rates) and who was at risk because of decades (centuries) of systemic lack of resources, dispossession, neglect, and other violence. We saw who was mourned and who was viewed as expendable. This perspective shaped the priorities of the volume: computational humanities is not just about the work but about how and where we work and for whom. What are the environments that allow us to apply computational methods in ways that broaden our knowledge? What do we need to make that work possible and, more important, accessible, fruitful, careful, caring, and transformative?

As we debate, we would like to consider setting at least one parameter. This involves a common rhetorical and sometimes theoretical move that we hope we might use more carefully. A trend in scholarly discourse is to place “critical” in front of a term to forge a new area of study. Yet, such a move risks becoming an appropriation, often by groups whose subjectivity and disciplinary background are quite powerful in the academy, and erasing the important work that actually undergirds the area of study. Our configuration of CH builds on the fundamental work of movements in the digital humanities (DH), such as #DHPOCO, FemTechNet, and #transformDH, and the reconfiguration of DH being forged by collectives like #USLDH, Digital Ethnic Futures, Data 4 Black Lives, Black beyond Data, and LifexCode: DH against Enclosure.9 Their work is not only critical but specific.

They offer theories and actions through particular frames that, in aggregate, offer a critical foundation for CH. They ask us to question the sterility of “hard” numbers, the lifelessness of a form of humanistic inquiry adverse to theory, and the building of thick boundaries. Instead, they ask us to build a more expansive, inclusive, and justice-oriented praxis that knits communities together by expressing the complexity of being people in relation to one another, animated by theories that expand our ways of knowing. We have much work to do to realize the critical and emancipatory aspirations of these movements, and we hope this volume joins the call.

Organizing Debates: Asking With and Asking About

We found it hard to settle on an organization. The chapters were proposed before a global pandemic and shifted as we all processed the tremendous change to our everyday lives and immense loss underway. The chapters transformed as a result of global and local conversations about critical topics such as data, quantification, and labor. The chapters informed each other as peer review further drew out connections, and we are grateful to the authors who worked to integrate and speak directly to each other. A prominent pattern emerged in the form of the connections across disciplinary lines as authors discussed and debated within subfields such as computational literary studies and digital history. These connections are further elucidated through links in the digital open access version of this volume.

While we anticipate many readers will pick their own pathway through the volume, we do offer a broad organization: “Asking With” and “Asking About.” The organization revolves around chapters that ask questions with computational methods and chapters that are asking about computational methods. Rather than using an organization based on the objects of study, we selected an order that comes from another angle and augments the links that authors draw in their chapters. The parts are infused with questions about access, labor, and power, which is shaped by the chapter’s debates alongside the subjectivity and positionality of the authors.

The first part circulates around computation and interpretation. How we know what we know is shaped by method, a constant site of debate within, between, and across disciplines, areas of study, and institutional structures. Moving beyond debates about whether computational methods can animate humanistic inquiry, part 1 engages into debates over what kind of interpretation furthers the humanities. Hannah Ringler calls for a focus on the how of interpretation, for developing a hermeneutics that distinguishes between interpreting artifacts through tools (“asking with”) and interpreting the output of tools (“asking about”) toward humanistic claims. Mark Algee-Hewitt builds on this call and asks us to shift our process from applying the interpretive methods of the humanities to the results of a digital analysis to rethink how the transformation itself becomes a new kind of evidence, which now requires that we understand what these analytics methods actually do. In other words, we need a new interpretative framework for understanding the computational and humanities models of the phenomenon that we seek to study.

Building on Ringler and Algee-Hewitt, who situate their chapters within rhetoric and computational literary studies, David Bamman calls for a shift in national language processing research that trades in models that generalize analysis, regardless of the specificities of the genre, for models that are built with and for humanistic insight. These literary-centric models, he argues, necessitate collaboration with humanities scholars, a process of building with and not just for. Bamman’s call could be understood as enacting Crystal Hall’s call to recover the humanistic definition of parallax provided during the late Italian Renaissance in text analysis. Hall argues that this older configuration of parallax provides a method for drawing on multiple perspectives and quantitative models to expand how one analyzes text through the humanities. Finally, Fabian Offert and Peter Bell shift perspective to the study of art history and explore how humanities scholars can harness generative AI tools. They demonstrate, like Algee-Hewitt, how “wrong” results from a particular method can actually offer powerful evidence as well as call for computational humanities to embrace discriminative and generative approaches.

Turning to debates in digital history and then Black studies, the next two chapters address pressing issues about what kinds of data we should be asking our questions with. Katherine McDonough reframes maps as data by tracing the historiography of spatial history and arguing why a more interdisciplinary computational approach can further the study of space and place. The chapter expands our understanding of data into this powerful multimodal form through the lens of computer vision, bringing together image and text analysis in powerful ways that situate CH as part of a new kind of spatial turn. Crystal Eddins discusses how reading with and against the grain of the archive is critical to working with the sources that we transform into data. By focusing on archives of slavery, the chapter demonstrates how this approach is particularly crucial when working with archives and their datafied forms, as there is a risk of reifying rather than resisting violence through data. Together, these two chapters offer ways of rethinking what counts as data, with implications for all of CH.

The second part, “Asking About,” turns to structural issues for CH with a focus on research practices. Roopika Risam addresses the barriers to building computational ethnic studies by drawing on her own experience forging the field. Not only does one have to contend with the institutional challenges of creating and building ethnic studies, but one must also overcome technical barriers—specifically, learning how to code. These double and triple binds offer a powerful framework for understanding the challenges faced by many areas of study, seeking to incorporate computational methods. Additionally, they offer another critical dimension to debates over coding and programming. Benjamin Schmidt presents the history of debates over computation in the disciplinary formation of history in the United States over the past fifty years as a cautionary tale. He warns that the construction of computational humanities need not (and should not) produce a humanities that becomes more scientific or primarily defined by quantification. Instead, it should make interpretive space for a more creative humanities. Barbara McGillivray, Federico Nanni, and Kaspar Beelen turn to the search process and advocate for an information retrieval architecture that is shaped by disciplinary commitments, specifically emphasizing how search affects the practice of history. These authors all demonstrate how different disciplines within the humanities contribute powerful perspectives and needs to CH—which, we argue, will benefit from a transdisciplinary framework.

Turning to collaboration and datafication, Vanessa Holden and Joshua Rothman draw on debates in digital history. Working with the archive of slavery, they argue that how evidence is created and collected is as important as the computational analysis that follows. They call for a collaborative, participatory model of engagement with the archive and data creation that facilitates thinking with, across, and against the archive, with attention to their historical legacies. While Holden and Rothman focus on the making of data, Julia Damerow, Abraham Gibson, and Manfred Laubichler turn to another key aspect of CH: coding. Because code facilitates the creation and analysis of data, the lines of structured characters become an important documentation of interpretation. The authors argue that the computational humanities have a problem: code of low quality limits our ability to interpret, reproduce, and understand its output. They offer suggestions for how to expand the field to produce clearer and higher-quality code. In aggregate, these chapters bring to the fore debates over what kinds of data we want to create, with calls to expand what counts as data and who is collaborating and with particular attention paid to how data and coding are shaped by praxis rather than just given.

Addressing the challenge of double and triple binds that Risam outlines, Marieke van Erp, Barbara McGillivray, and Tobias Blanke argue that collaboration across disciplines, particularly computer science and the humanities, will be key to the computational humanities. They identify key pressure points, including funding, publication models, and research evaluation, that will need to be grappled with. Returning to debates over collaboration and programming, Quinn Dombrowski, Tassie Gniady, David Kloster, Megan Meredith-Lobay, Jeffrey Tharsen, and Lee Zickel turn to another key aspect of computational processing: high-performance computing (HPC). They address how whoever provides support shapes which research is prioritized, and they advocate for humanities disciplinary experts as part of HPC staff to support access to these methods as we develop the computational humanities. Lisa Tagliaferri zooms out to argue that the broader “vernacular” world of startups and tech companies can both benefit from the humanities perspective and be a source for innovation relevant to humanists. These chapters speak to new and ongoing debates over the role of disciplinarity, interdisciplinarity, and transdisciplinarity and the barrier to entry to the computational humanities that, as editors, we are responsible for acknowledging, addressing, resisting, and removing. In aggregate, part 2 addresses larger debates over which ways of knowing have been erased or rendered illegitimate and which are considered prestigious, convincing, and worth investment. These chapters demonstrate how CH is less of a science and more of a situated knowledge, best when attuned to issues of field formation, history, labor, and power.

No categorization fully captures the complexity of the conversation. Like the data that we work with, there will be limits, omissions, and silences. At the same time, we hope that expected and unexpected connections and patterns will be illuminated. Listening to critiques of digital humanities from collectives such as #DHPOCO, FemTechNet, and #transformDH, we are attuned to the authors’ positionality as shaped by disciplinary, geographical, institutional, and related structures of power. While we worked to create a robust and expansive conversation, there are inevitably gaps, and we are excited to see where communities bring these debates.

Future Debates: Power, Pedagogy, and Reflexive Openness

As we look to next steps, we highlight several components that we want to amplify and flag with caution. First, the growth of CH is occurring in the context of the largest shift in the balance of academic power and prestige in recent memory. We are in a moment where data and computation enjoy great prominence, both positive and, increasingly, negative. At the same time, both the institutional support for and perceived cultural impact of humanities are crashing. The incredible expansion of computer science, which struggled in the academy in the early 2000s following the dotcom crash, and the rapid ascent of data science are a testament to this moment of quantification. There are many calls in this volume and in other spaces to think across boundaries, particularly involving these two fields.

Yet, the challenges of these fields, along with the digital humanities, are well documented, ranging from a destructive “move fast and break things” ethos to long-standing cultural dynamics that reject and marginalize the contributions of all but a narrow “codebro” profile. The humanities face similar challenges of elitism and inaccessibility, compounded by isolation, glacial publication cycles, and training that is mismatched with the realities of the market. As we build out this area, it is crucial to pay attention to the structures we are creating and those we are replicating.

We are researchers, and the essays in this volume reflect that, but we are also educators. CH asks us to address our pedagogy. On the one hand, CH offers ways to think about how the humanities can expand their methodological and evidential repertoire. On the other hand, CH offers an expanded idea of why and how we pursue fields such as computer science and data science. In our own courses, we have seen significant interest in humanities-oriented computational courses from non-humanities students. When we have this kind of narrow window of opportunity, what should we teach to leave students with a long-term excitement about reaching past purely technical work?

Expanding beyond disciplinary boundaries in our classes at the secondary, undergraduate, and graduate levels will be key. Achieving balance has consistently been difficult: Can we train students in both Sanskrit and Python? What the current volume suggests is that we should not so much attempt to train generalists or unicorns as learn how to become generous collaborators. We can turn to exciting models, such as hiring scholars trained in computer science, English, and history at information science programs in the United States and interdisciplinary PhDs in Europe. But turning a pattern of work and a research agenda into a structured, measurable curriculum will take dedication.10

Perhaps one of the largest social and structural shifts that is underway because of CH is how digital humanities will configure itself in relation to the computational social sciences.11 While much of the discussion in DH has involved discussing collaboration between computer science and the humanities, CH requires engagement with data science, information science, and statistics, among other disciplines. This is bringing DH in closer collaboration with the social sciences. The rapid growth of the computational social sciences in higher education, perhaps counterintuitively, is also shifting the sciences closer to the social sciences. By embracing these opportunities to create, collaborate, and debate across the humanities, social sciences, and sciences, CH makes space for the humanities to be at the center of debates over our computational world and how to computationally understand the world.

The realization of an expansive CH will require experimentation, openness, collaboration, and critical reflexivity.12 We need to create an environment where researchers from many backgrounds can feel comfortable, productive, and valued, through a “vernacular” culture that radically expands accessibility. CH will have a sturdier foundation if we are open to listening and collaborating across boundaries, rather than trying to build a space apart. By knitting in tandem, we can create together: try new patterns, rip out, and try again.

A Final Note

When we began discussing an edited volume within the Debates in the Digital Humanities book series, compiled under the umbrella of “computational humanities,” we could not have predicted how the world would change on our watch. Since the winter of 2019, when the call for papers was first issued, the world has undergone successive waves of mobilization and calamity, political and viral reverberations of an unprecedented nature, all mediated by charts and tables and graphs. We are living through history, rocked by personal grief and bathed in quantification. The larger reality risked being obscured in scale, diffusion, and complexity. Abstraction and numbers could render invisible as much as they could illuminate. We have struggled to reconcile our feelings that our subject is insignificant in the face of so much calamity, and simultaneously that the subject of this volume has never been more pressing. We are grateful to the authors, peer reviewers, series editors Matt Gold and Lauren Klein, and the team at the University of Minnesota for being a part of this critical and timely debate.

Notes

1. 1. The term and definitions are in formation. There is the Computational Humanities Forum that “serves as an asynchronous platform to discuss all ideas and questions related to computational humanities research.” Another formation is a lab, such as the Computational Humanities Lab (2012–2014) at the University of Wisconsin–Madison and centers such as the Rhoads Computational Humanities Center at Duke University. There are also efforts to define computational humanities in relation to specific fields; see Tilton, “American Studies + Computational Humanities.” Another area of development is coursework, such as Info 190: Computational Humanities at Berkeley–School of Information, taught by David Bamman, who is an author in this volume.

2. 2. We explicitly seek to situate this version of computational humanities in Black studies and cultural theory such as described by Saidiya Hartman and Avery Gordon. We also see this volume joining the calls of other volumes in the Debates in the Digital Humanities series, such as Bodies of Information edited by Elizabeth Losh and Jacqueline Wernimont and People, Practice, Power: Digital Humanities outside the Center edited by Anna McGrail, Angel David Nieves, and Siobhan Senier.

3. 3. For example, a group came together around 2019 to build the computational humanities research community. Primarily among scholars in Western Europe, the emerging conversation elicited critique because of initial framings that positioned CH as finally a return to more scientific engagement. This was understood as an effort to split off from the rest of DH to not have to grapple with the hard work of DH scholars to bring important questions about intersectionality and power to the field through the guise of making the field more of a science. Much of the debate ensued on X (formerly known as Twitter). For a lovely call to find an intersection between the humanities and sciences in DH, see the introduction to Miguel Escobar Varela’s Theatre as Data. The version of the computational humanities research community that we are seeing today has taken this into account and worked to shed the problematic frames that led to a storm cloud over the initial launch of the community.

4. 4. See, for example, Allington, Brouillette, and Golumbia, “Neoliberal Tools (and Archives): A Political History of Digital Humanities,” and Digital Humanities Now’s “Editors’ Choice: Round-up of Responses to ‘The LA Neoliberal Tools (and Archives)’” at https://digitalhumanitiesnow.org/2016/05/editors-choice-round-up-of-responses-to-the-la-neoliberal-tools-and-archives/. For more about past debates on the topic in the debates series, see Greenspan, “The Scandal of Digital Humanities.”

5. 5. For more on this debate, see Terras, “Peering Inside the Big Tent: Digital Humanities and the Crisis of Inclusion”; Svensson, “Beyond the Big Tent”; and Weingart and Eichmann-Kalwara, “What’s under the Big Tent?: A Study of ADHO Conference Abstracts.”

6. 6. For example, see Antoniak, Mimno, and Levy, “Narrative Paths and Negotiation of Power in Birth Stories”; Rhody, “Topic Modeling and Figurative Language”; and So, Redlining Culture: A Data History of Racial Inequality and Postwar Fiction.

7. 7. For example, Nan Z. Da’s “The Computational Case against Computational Literary Studies” ignited a debate about the interpretative limits of computational literary studies, including a 2019 forum in Critical Inquiry, which is available at https://critinq.wordpress.com/2019/03/31/computational-literary-studies-a-critical-inquiry-online-forum/ and https://critinq.wordpress.com/2019/04/12/more-responses-to-the-computational-case-against-computational-literary-studies/.

8. 8. For more on the origins of intersectionality, see Crenshaw, “Mapping the Margins,” and the Combahee River Collective, “A Black Feminist Statement.” For a breakdown of intersectionality and other terms relevant to data and digital humanities work broadly, see D’Ignazio and Klein, Data Feminism.

9. 9. For more about these movements, see the following websites: https://dhpoco.org/, http://digitalethnicfutures.org/, https://www.femtechnet.org/, https://www.lifexcode.org/, and https://transformdh.org/. For articles about these movements, see Gajjala, Risam, and Gairol, “What Is Postcolonial Digital Humanities (#DHpoco)?”; Johnson, “4DH + 1 Black Code/Black Femme Forms of Knowledge and Practice”; and Lothian, “From Transformative Works to #transformDH: Digital Humanities as (Critical) Fandom.”

10. 10. Recent examples of textbooks include Arnold and Tilton’s Humanities Data in R; Walsh’s Introduction to Cultural Analytics & Python; Jockers and Thalken’s Text Analysis with R for Students of Literature; and Karsdorp, Kestemont, and Riddell’s Humanities Data Analysis: Case Studies with Python.

11. 11. We want to thank the anonymous external reviewer for bringing this important point to our attention. Along with your generous feedback and witty writing style, we are greatly appreciative of the time that you took to review the entire volume.

12. 12. One area of emphasis in DH is building it around values. We draw on Spiro, “‘This Is Why We Fight’: Defining the Values of the Digital Humanities.”

Bibliography

1. Allington, Daniel, Sarah Brouillette, and David Golumbia. “Neoliberal Tools (and Archives): A Political History of Digital Humanities.” Los Angeles Review of Books, May 1, 2016. https://lareviewofbooks.org/article/neoliberal-tools-archives-political-history-digital-humanities.
2. Antoniak, Maria, David Mimno, and Karen Levy. “Narrative Paths and Negotiation of Power in Birth Stories.” Proceedings of the ACM on Human-Computer Interaction 3, no. CSCW, art. 88 (November 2019): 1–27. https://doi.org/10.1145/3359190.
3. Arnold, Taylor, and Lauren Tilton. Humanities Data in R: Exploring Networks, Geospatial Data, Images and Text. New York: Springer, 2015.
4. Combahee River Collective. “A Black Feminist Statement.” Women’s Studies Quarterly 42, no. 3/4 (2014): 271–80.
5. “Computational Literary Studies: A Critical Inquiry Online Forum.” Critical Inquiry—In the Moment, March 31, 2019. https://critinq.wordpress.com/2019/03/31/computational-literary-studies-a-critical-inquiry-online-forum/.
6. “Computational Humanities.” Duke University, n.d. https://bigdata.duke.edu/computational-humanities.
7. Crenshaw, Kimberle. “Mapping the Margins: Intersectionality, Identity Politics, and Violence against Women of Color.” Stanford Law Review 43, no. 6 (1991): 1241–99.
8. Da, Nan Z. “The Computational Case against Computational Literary Studies.” Critical Inquiry 45, no. 3 (2019): 601–39. https://doi.org/10.1086/702594.
9. D’Ignazio, Catherine, and Lauren F. Klein. Data Feminism. Cambridge, Mass.: MIT Press, 2020.
10. Gajjala, Radhika, Roopika Risam, and Rahul K. Gairol. “What Is Postcolonial Digital Humanities (#DHpoco)?” Salaam: The Newsletter of the South Asian Literary Association 38, no. 1 (2014): 7–11.
11. Gordon, Avery. Ghostly Matters: Haunting and Sociological Imagination. Minneapolis: University of Minnesota Press, 1997.
12. Greenspan, Brian. “The Scandal of the Digital Humanities.” In Debates in the Digital Humanities 2019, edited by Matthew K. Gold and Lauren F. Klein, chap. 9. Minneapolis: University of Minnesota Press, 2019.
13. Hartman, Saidiya. Wayward Lives, Beautiful Experiments: Intimate Histories of Social Upheaval. New York: W.W. Norton, 2019.
14. Henrickson, Leah. “Humanities Computing, Digital Humanities, and Computational Humanities: What’s in a Name?” 3:AM Magazine, October 24, 2019. https://www.3ammagazine.com/3am/humanities-computing-digital-humanities-and-computational-humanities-whats-in-a-name/.
15. “Info 190: Computational Humanities.” Berkeley School of Information. Accessed January 13, 2024. https://www.ischool.berkeley.edu/courses/info/190/ch.
16. Jockers, Matthew L., and Rosamond Thalken. Text Analysis with R for Students of Literature. 2nd ed. New York: Springer, 2020.
17. “Join the Community.” Computational Humanities Research, 2021. https://2021.computational-humanities-research.org/contact/.
18. Johnson, Jessica Marie. “4DH + 1 Black Code/Black Femme Forms of Knowledge and Practice.” American Quarterly 70, no. 3 (2018): 665–70. https://doi.org/10.1353/aq.2018.0050.
19. Karsdorp, Folgert, Mike Kestemont, and Allen Riddell. Humanities Data Analysis: Case Studies with Python. Princeton, N.J.: Princeton University Press, 2021.
20. Losh, Elizabeth, and Jacqueline Wernimont, eds. Bodies of Information: Intersectional Feminism and Digital Humanities. Minneapolis: University of Minnesota Press, 2018. https://dhdebates.gc.cuny.edu/projects/bodies-of-information.
21. Lothian, Alexis. “From Transformative Works to #transformDH: Digital Humanities as (Critical) Fandom.” American Quarterly 70, no. 3 (2018): 371–93. https://doi.org/10.1353/aq.2018.0027.
22. McGrail, Anne, Angel David Nieves, and Siobhan Senier, eds. People, Practice, Power: Digital Humanities outside the Center. Minneapolis: University of Minnesota Press, 2021. https://www.jstor.org/stable/10.5749/j.ctv2782dmw.
23. “More Responses to ‘The Computational Case against Computational Literary Studies.’” Critical Inquiry—In the Moment, April 12, 2019. https://critinq.wordpress.com/2019/04/12/more-responses-to-the-computational-case-against-computational-literary-studies/.
24. Rhody, Lisa M. “Topic Modeling and Figurative Language.” Journal of Digital Humanities 2, no. 1 (2012). http://journalofdigitalhumanities.org/2-1/topic-modeling-and-figurative-language-by-lisa-m-rhody/.
25. So, Richard Jean. Redlining Culture: A Data History of Racial Inequality and Postwar Fiction. New York: Columbia Press University, 2021.
26. Spiro, Lisa. “‘This Is Why We Fight’: Defining the Values of the Digital Humanities.” In Debates in Digital Humanities, edited by Matthew K. Gold, chap. 3. Minneapolis: University of Minnesota Press, 2012.
27. Svensson, Patrik. “Beyond the Big Tent.” In Debates in Digital Humanities, edited by Matthew K. Gold, chap. 4. Minneapolis: University of Minnesota Press, 2012.
28. Terras, Melissa. “Peering Inside the Big Tent: Digital Humanities and the Crisis of Inclusion.” Melissa Terras’s Blog, July 26, 2011. https://melissaterras.org/2011/07/26/peering-inside-the-big-tent-digital-humanities-and-the-crisis-of-inclusion/.
29. Tilton, Lauren. “American Studies + Computational Humanities.” American Quarterly 70, no. 3 (2018): 633–39. https://doi.org/10.1353/aq.2018.0046.
30. Varela, Miguel Escobar. Theatre as Data. Ann Arbor: University of Michigan, 2021.
31. Walsh, Melanie. Introduction to Cultural Analytics & Python. Version 1, 2021. https://melaniewalsh.github.io/Intro-Cultural-Analytics/welcome.html.
32. Weingart, S. B., and N. Eichmann-Kalwara. “What’s under the Big Tent?: A Study of ADHO Conference Abstracts.” Digital Studies/le Champ Numérique 7, no. 1, art. 6 (2017). https://doi.org/10.16995/dscn.284.