Books in Conversation / Heather Houser and Richard Jean So

A conversation between Heather Houser, author of Infowhelm: Environmental Art and Literature in an Age of Data (Columbia UP, 2020), and Richard Jean So, author of Redlining Culture: A Data History of Racial Inequality and Postwar Fiction (Columbia UP 2020)

Richard Jean So (RJS): Thanks, Heather, for offering to chat with me today about our books. Nominally, our books take on very different topics—environmental art versus racial inequality in literature—but as I read your book, with a great deal of interest and admiration, I was delighted to find that our books converge on several substantial topics. One of them—and is a word that appears in both of our books’ titles—is “data.” Could you say a bit about how you came to realize how central data is to contemporary culture and what constitutes its relationship to representations of and engagements with the environmental crisis?

Heather Houser (HH): It’s a pleasure to meet over our new books! I first noticed the data explosion in contemporary culture through data visualizations that were taking over journalism and social media alike. Stories about environmental crises—especially climate change—were prominent in these new media. I’d studied environmental literature for my dissertation and first book, Ecosickness, and visual culture became an important place to grasp how environmental crises and response were unfolding. But I also came to wonder how data appeared in literature; I didn’t have to look hard or long. Specifically, I came to wonder if literary description was a locus for relaying eco-scientific information and might in fact constitute an “environmental text.” I don’t dwell on this specific point in Infowhelm, but it’s at its origins. As I was working on the book, climate denialism took over the White House and mis/disinformation, alternative facts, etc. all made questions surrounding information even more urgent.

To your second question, engagements with scientific information—which, for me, encompasses quantitative data as well as scientific methods and explanations—is everywhere in literature and visual culture today. I was surprised there wasn’t more thinking about how info becomes aesthetic material, an argument of Infowhelm. But in addition to being there, information is a way artists think through how we arrived at planetary environmental crises. How certain ways of approaching knowledge in the wealthy Eurowest led to mastery and domination and how those epistemologies could be dismantled when art repurposed scientific information. This repurposing involves taking up information but then deforming it through other ways of knowing: emotion, uncertainty, speculation, etc. It’s this entangling of epistemologies that allows art to glean knowledge from the sciences without reproducing domination and mastery.

I use pretty traditional methods of literary and visual culture analysis and one of the things that really impressed me about Redlining Culture is how you deftly merge methodologies. Your conclusions about the persistent whiteness of publishing, prestige, and scholarship are urgent and kill some sacred cows about the multiculti 1980s–90s that have blocked attention to racial inequality. How did you navigate your relationship to data as a literary scholar in this project? Specifically, was the book based in computational methods from the outset? Were there particular struggles or frictions between close reading, historical analysis, and/or the data methods?

RJS: Thanks for your response and question—the way your book thinks about and parses “data” in terms of the aesthetic objects it studies, and the way my book thinks about “data” as a methodological challenge and affordance—is precisely one major thread I thought we could mutually explore in talking about our books.

A lot of the early work I did with computational methods was more exploratory but I didn’t want to write an entire book in that vein. I didn’t think that kind of work, in the end, would ever convince non-quantitative scholars in the discipline. I thought these methods would only convince such scholars, and become mainstream, if they helped to articulate and answer a central and important question to literary studies. I stumbled upon this question back in 2015 or so when some colleagues and I were gathering Worldcat data regarding the most widely held American novels in libraries published between 1950 and 2000. The list turned out to be 97% white. We were all stunned by this statistic and I instantly intuited that this simple number pointed to a much bigger problem in literary history as well as the way scholars have understood and written about that history.

So yes, the project was always imagined from its inception to be quantitative in some way. I start with those numbers—publishing is 97% white, book reviews are 90% white, bestsellers are 98% white, prizewinners are 91% white—to unfold a much larger argument about inequality in American literature, but the idea of measurement, whether measuring demographic inequality or inequality in terms of literary representation—is central to the book’s conceptual DNA. The cost of not including quantitative method or evidence, I think, is that this argument about “inequality” would really lose a lot of its persuasive force.

That said, much of the pleasure and analytical energy of the book comes precisely through the tension between quantitative method/evidence and close reading/historicism. This is because both are obviously valid ways of looking at a text or literary history but each really bears a very different ontology of the text. Bringing these methods together and dealing with that friction, I think, was the hardest part of the book but it was also the most rewarding. Through their interaction, we gain a genuinely expanded view of this history.

Speaking of methodological friction and tension, one thing I think your book does quite masterfully is navigate an analytical position between being critical/reflexive of environmental science, particularly its popular data visualization aspects, while not invalidating the broader scientific enterprise that undergirds this work. Can you say a bit more about how you worked through this methodological challenge and perhaps, also how colleagues both in the sciences and the humanities have responded to your arguments?

HH: I’ve heard versions of “computational analysis is a starting point, not the whole point,” but your way of putting it—“this simple number pointed to a much bigger problem in literary history”—and performing it is so convincing and powerful in Redlining.

To your question, the first bits of Infowhelm I wrote led to the charge of fueling denialism. It stung, especially since it came from an eminent person in the environmental humanities, but it pushed me to thread the needle between deference to Eurowestern science (to paraphrase Sheila Jasanoff) and critique of it. I worked through this friction rhetorically—with caveats, the words I chose, etc.—and through attention to what artists were doing. They were threading this needle, too, and I saw that in how they repurposed and remediated scientific data and methods, or, as I write, how they took those up “in an interrogative mode.” To address climate crises or Covid-19 without reinforcing racism and other inequities, we need both—the data and the interrogation—in a constant dialectic or oscillation. The arts of the infowhelm do that.

Colleagues in the humanities more than those in the sciences have contested my portrayal of 21st-century Eurowestern science as positivist. I agree and disagree. I’ve collaborated with scientists and engineers who’ve insisted their work is objective, that data speaks truths and for itself. Perhaps they’re in the minority, but it seems the “epistemic virtues” (from Daston and Galison’s amazing book Objectivity) of objectivity, quantifiability, universality, etc. are still dominant.

I’d like to return to the origin story of Redlining Culture with something that’s both personal and intellectual: did/how did the affects of data shape your project and/or its reception? I’m curious about emotions that your computational findings about racial inequality raised in you and in your readers but also how your stance toward a text or an author does or doesn’t shift depending on your methods. (This Q is inspired by your brilliant reading of counting in Beloved: how “whitefolks” appear as “something so large it cannot be counted,” whereas Black folks and the violence inflicted on them are counted but in order to show that they are “tiny compared to the infinite white” [175]).

RJS: Thanks, this is a really interesting question and one, I am ashamed to admit, is something I hadn’t much thought about before. One of the great points that your book makes is that environmental information is deeply entangled or intertwined with belief and emotion.

In response to your question, I have two thoughts. First, I think any data scientist who works on material that encodes power will in some way be affected by that data. But I do find that literary data to be especially affecting because it’s unlike other quantitative social data like unemployment statistics. It’s different because literary data itself is a form of narrative—it’s not a proxy or measurement of human suffering. So I found this to be the challenging part of working with literature-as-data, to ensure that whatever I did with it as data drew out, rather than erased, its innate expressive power.

But then this gets to my second point, and I think the thrust of your question. Done well, data analysis of culture can tell powerful stories that enhance that expressive force. For me, this method is similar to archival recovery work. Distant reading, like finding some illuminating fragment of history in the archive, which can reveal otherwise hidden or latent aspects of a text, can shed light on some broader pattern of language that deepens what you see on the page.

What we are talking about here is “epistemology,” a keyword from your book, and at the center of one of my favorite lines from Infowhelm: “epistemology is everywhere.” Your book makes a powerful case for the importance of widening our understanding of how we understand the environment; how it’s going to take a lot of different forms of epistemology, and all of those ways of knowing are entangled. In your book, you make a bold proposal for how this might look practically as an institutional practice. Could you say a bit about how your book’s research got you to this point, this call for a“trans-epistemology” form of scholarship?

HH: Thank you for thinking through that question. Emotion can spur data questions and produce data stories, yet the presentation of that data sometimes mutes emotion in the interest of “objectivity.” I love the comparison to archival dis- and re-covery, too!

I have a bitch of a time writing conclusions so in one respect those thoughts arose under duress and distress. As you said, Infowhelm makes the case that epistemological overhauls are fundamental to radical environmental thought and action. I worked on the book within academic institutions—my department of English but also humanities centers and, starting in 2017, a climate research “grand challenge” at UT Austin, Planet Texas 2050. I was halfway through the first draft of the book MS when co-founding and running that program took over my life. I was spending a lot of time saying how transdisciplinary our efforts were, how essential that is to equitable climate strategies, how revolutionary it is for universities, etc. And I believed all that, in their ideal forms, but I was also experiencing disturbing tensions among the people and disciplines involved in the program. Part of this arose from us remaining so disciplined. (Things have changed a lot in the ten years since I got my PhD and a newer generation might not be so stuck in its ways.) But I suspect those tensions arose because talking in terms of disciplines—whether inter-, multi-, trans-, ambi- (I made that up… I think)—doesn’t get at the radical upheavals in thought and feeling that are required to make social and environmental change. “Disciplines” also don’t always acknowledge the expressions of identity and positionality that are playing out in transdisciplinary academic programs. “Trans-epistemology” was a way, an admittedly clunky way, for me to start thinking through this. The coda’s a half-formed thought, and I’m starting to fill it out through a special forum of ASAP/Journal on “becoming undisciplined” I’m co-editing with Stephanie LeMenager.

This makes me want to ask you two things, and, since this is my last Q, I’ll pose them both and you can choose. The first is about classification and categorization. Infowhelm addresses the problems and affordances of classification through a trend I call “the new natural history” as well as other modes of eco-representation. I wonder how you approached those acts in Redlining Culture. You meticulously detail your methodology for designating the race of an author, for making formal distinctions, etc., all of which are classificatory acts. Was classifying or categorizing fraught for you? If so, how did you overcome or reconcile that?

The second Q is how you think we can use research as the basis for effecting social change and what role partnerships with those outside the university play in that.

RJS: I found your chapter on “the new natural history” really brilliant and indeed I was struck by its engagement with the question of “categories” in terms of scientific practice and natural history, and how one important thing that environmental writers and artists are doing is creating new types of categories of the natural world, as well as scrambling existing categories imposed from above, whether scientific or industrial.

This really spoke to me because dealing with categories was probably the hardest part of writing Redlining Culture. Very simply, especially within the critical race theory tradition, scholars have generally resisted using racial categories because of their co-implication with scientific racism and also, as Stuart Hall points out, because they erase the particularity of racial experience. But I’ve found that racial categorization can have a rich affordance for combating racial inequality. Research in the social sciences, in particular, has done a good job of working with racial categories without reifying race. Chicago sociologists were able to identify patterns of racial discrimination with bank loans (financial “redlining”) based on survey research where they had to reductively categorize people based on racial identity.

In Redlining Culture, which borrows this metaphor and applies it to the cultural field, I take a similar approach, while still developing methodologies, in dialogue with the inherent flexibility of machine learning, that can still recover meaningful outliers that in the end problematize the categories I start with. I need to work with racial categories and label writers as “white” or “black” to do the work necessary to uncover patterns of racial inequality, but I also focus on examples of writers who, again, reveal the inherent instability of those categories.

I think we are near the end so let me briefly respond to your final question: I don’t think all humanities research needs to aspire to having explicit public impact but I wrote Redlining Culture to address a social problem, and I’ve found it gratifying to see it has had an impact on publishers and the broader literary world, especially regarding my recent New York Times op-ed. This has led to some opportunities to partner with some literary studios and start-ups focused on creating greater opportunities for racial minorities to get involved with creative writing and publishing. Culture, of course, is a huge part of the puzzle of understanding major social problems, like inequality and climate, and if we can in part articulate and frame our work as oriented towards problem solving, there are a lot of opportunities for us to get into the larger game of social impact and change.

But yes, to give you the last word—how do you think about this question?

HH: Your answer again leads me to champion the methodological contributions of Redlining. I don’t think you can do any computational analysis, modeling, even data collection without categories. You probably can’t do literary analysis without them either, even if the work of the analysis is to challenge those categories.

Humanistic research definitely can and does—though doesn’t have to—move into the public sphere for social change. You see that happening in environmental work, but, frankly, not all that often, at least in the US. Even within the university, it’s rare to have humanities people working alongside artists and STEM folks on projects with partners outside the university. Not impossible or nonexistent but rare in areas like climate action, medicine, or AI where STEM, economics, and public policy are the go-tos. Humanists and artists need to be at those tables for a million reasons I won’t rehearse here, and it can be rewarding, but it can also be full of friction and compromise. You have to go in expecting to make a difference: to use humanist methods and perspectives to bring new thinking, feeling, and response to societal issues and inequities. But you also have to go in expecting friction, and that friction can sometimes be productive and sometimes be destabilizing. I know that’s elliptical, but I wanted to emphasize the two sides to the “humanities in the world” coin: the outside, where we want to exercise and impart the value of the humanities, and the inside, where we have to remember what we value and want to preserve in ourselves as the people doing the work.

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