I am an Associate Professor in the Department of English at Rutgers University, New Brunswick. I study and teach twentieth-century literature in English. My research interests include genre fiction, the sociology of literature, modernism, South Asian literature in English, and the digital humanities. I am the author of Fictions of Autonomy: Modernism from Wilde to de Man (Oxford University Press, 2013).
I have two undergraduate courses this fall. I thought I’d post the syllabuses and also give away my hidden agenda.
English 358:358 Early Twentieth-Century Fiction
What do James Joyce, Dashiell Hammett, Mulk Raj Anand, and Zora Neale Hurston have in common? All significant writers of English-language fiction, all active in the first half of the twentieth century, these writers lived through an epoch of global social upheaval—world wars, revolutions, mass migrations, the rise and decline of empire—and their work registers and responds to a world of crisis. Yet Joyce, the Irish experimentalist, writes nothing like Hammett, the pioneer of hard-boiled detective fiction; Anand, the committed Indian leftist, adopts very different perspectives from Hurston, the supreme Harlem Renaissance novelist. This course is a study in what is and is not shared in the fiction of these four writers and others of their era. Students will learn to analyze the forms and themes of exemplary fictions of the early twentieth century and to understand the variety of these fictions as a result of social contestation and collaboration. Readings include case studies in literary modernism (Joyce, Virginia Woolf, William Faulkner), detective fiction (Dorothy Sayers, Hammett), Harlem Renaissance fiction (Jean Toomer, Hurston), and Indian writing in English (Rabindranath Tagore, Anand).
English 359:207 Data and Culture
The digitization of wide swaths of the print record has opened up new challenges and opportunities for researchers in the humanities. This course introduces students to some of the key techniques used by humanities scholars to organize, manipulate, and analyze digital sources—attending both to longstanding scholarly institutions and practices that shape our understanding of digital texts (critical editions, brick-and-mortar archives, and quantitative methods within social, political, and cultural history) and to new methods for studying texts, cultural geography, and relations between and among producers and consumers of culture.
Students who complete this course will develop facility in the use of digital tools for the representation, curation, and analysis of digital text. In each case, however, we will place these relatively new tools within a longer history of humanistic inquiry and will ask: what insights can these tools provide, and what questions (and texts) do they marginalize or occlude? Our aim throughout is to examine how digitization and data science have changed the questions that humanists can ask of their sources. What does it mean to think of culture as data? What new histories do these tools and methods help us uncover? In what ways has digitization helped and hindered the ability of humanities disciplines such as history, literary studies, and art history to provide an understanding of the past that can speak to urgent questions in the present moment?
Hidden agenda after the jump (as they used to say in the Elder Days of Blogging).
The other day I was talking with an innocent bystander about some of my past work in the digital humanities. It occurred to me to wonder what a person who went looking for that work would find. The abyss also looks into you. Anyhoo, once upon a time I spent a lot of time working with data from JSTOR’s Data for Research service, a thing that no longer exists, and I produced two fairly elaborate programming projects related to topic models of text: my dfrtopics R package and my dfr-browser topic-model visualization. I am writing this post to announce that those things are still available and continue to shamble on, zombie-like, into the coming apocalypse. But I don’t plan to develop them further.
In my last post on casualization at Rutgers, written November 2021, I discussed statistics on the rise of full-time, non-tenure-track faculty, arguing that this was an increasingly significant yet under-discussed aspect of the broader erosion of the tenure track. I promised then that I’d follow up on some of the details about different categories of faculty and of institutions. Would my dire picture of “twilight for tenure” change if I separated non-medical from medical faculty, or if I paid attention to faculty with non-instructional roles? Well, I’m pleased to report the picture is dire no matter how you paint it. I’ve been looking at the more granular information on higher-education staffing found in the Human Resources data from the Department of Education (specifically, the “Employees by Assigned Position” or EAP data files from IPEDS). Here are some tentative explorations, vacillating between being tediously technical and speculatively broad-brush. Skip to the end for my regular “workers of the world” conclusion followed by faculty-casualization league tables for research universities.
The basic question is, what are the terms of employment for people doing academic work in higher education? The EAP data answer that question by classifying workers at each institution as tenured, tenure-track, non-tenure-track, or “without faculty status,” dividing each category into full-time and part-time categories and according to whether they are in medical schools or not. The EAP survey also subdivides academic workers’ duties into instruction, research, and public service—as well as further categories like librarianship, archiving, and “Student and Academic Affairs.” Graduate workers (“graduate assistants”) are treated as another employee category, assigned either to teaching or research (and medical or non-). That leaves us with many possible ways of cross-cutting or subsetting the data about tenured and contingent academic work. (Obviously such categories do not exhaust the interesting variables; for example, the EAP data does not include any demographic information about each category. These are found in other IPEDS components, which however do not subdivide job categories with the same granularity.) What I want to do here is explore—without being exhaustive about it—whether the divisions matter to our understanding of contingency in the academy. I’m hoping that even if my analysis is wanting, the annotated R code on github, as “tidy” as I can make it, might help others get a leg up on working with this data.
Yesterday I had the privilege of responding to a wonderful talk by Matt Rubery at the Rutgers Initiative for the Book on “Podcasts, Audiobooks, and Podiobooks.” Feeling out of my depth as a non-podcast-listener, I went to my happy place instead, using my response as an occasion for digging around in a period and a medium I know better. Thinking about entanglements of print and audio fiction, I was moved to learn a little bit more about…
(Image from Galactic Central.) I’m not sure bringing this up was particularly illuminating on the subject of podcasts and books, but it was fun for me and I thought I’d set down a couple of things it made me think about.
I caught wind of some of the examples of GPT-3 answering PhD-exam-style questions plausibly. It seems to me an elegant if indirect proof that Wikipedia entries on many topics are written by current or former graduate students, or people with an excellent ability to imitate them. But it also called to mind the famous arithmetic scene in Eugène Ionesco’s La leçon, which I am sure I am not the first to think of in connection with today’s debates over “stochastic parrots.”