In 2010, as a new postdoctoral fellow, I presented a paper on James Thomson’s 1730 poem The Seasons to a group of senior scholars. The argument was modest: I used close readings to suggest that in each section of the poem Thomson simulated an aesthetic experience for his readers before teaching them how to interpret it. The response was mild and mostly positive. Six months later, having gained slightly more confidence, I presented the same project with a twist: I included a graph that revealed my readings to be based on a pattern of repeated discourse throughout the poem. The response was swift and polarizing: while some in the room thought that the quantitative methods deepened the argument, others argued strongly that I was undermining the whole field. For me, the experience was formative: the simple presence of numbers was enough to enrage scholars many years my senior, long before Digital Humanities gained any prestige, funding, or institutional support.
My experience suggests that this project passed what Da calls the “smell test”: the critical results remained valid, even without the supporting apparatus of the quantitative analysis. And while Da might argue that this proves that the quantitative aspect of the project was unnecessary in the first place, I would respectfully disagree. The pattern I found was the basis for my reading and to present it as if I had discovered it through reading alone was, at best, disingenuous. The quantitative aspect to my argument also allowed me to connect the poem to a larger pattern of poetics throughout the eighteenth century. And I would go further to contend that just as introduction of quantification into a field changes the field, so too does the field change the method to suit its own ends; and that confirming a statistical result through its agreement with conclusions derived from literary historical methods is just as powerful as a null hypothesis test. In other words, Da’s “smell test” suggests a potential way forward in synthesizing these methods.
But the lesson I learned remains as powerful as ever: regardless of how they are embedded in research, regardless of who uses them, computational methods provoke an immediate, often negative, response in many humanities scholars. And it is worth asking why. Just as it is always worth reexamining the institutional, political, and gendered history of methods such as new history, formalism, and even close reading, so too is it important, as Katherine Bode suggests, to think through these same issues in Digital Humanities as a whole. And it is crucial that we do so without erasing the work of the new, emerging, and often structurally vulnerable members of the field that Lauren Klein highlights. These methods have a powerful appeal among emerging groups of students and young scholars. And to seek to shut down scholarship by asserting a blanket incompatibility between method and object is to do a disservice to the fascinating work of emerging scholars that is reshaping our critical practices and our understanding of literature.
MARK ALGEE-HEWITT is an assistant professor of English and Digital Humanities at Stanford University where he directs the Stanford Literary Lab. His current work combines computational methods with literary criticism to explore large scale changes in aesthetic concepts during the eighteenth and nineteenth centuries. The projects that he leads at the Literary Lab include a study of racialized language in nineteenth-century American literature and a computational analysis of differences in disciplinary style. Mark’s work has appeared in New Literary History, Digital Scholarship in the Humanities, as well as in edited volumes on the Enlightenment and the Digital Humanities.