I am an Assistant Professor in the Department of English at Rutgers University, New Brunswick. I study and teach twentieth-century literature in English. My research interests include modernism, the sociology of literature, genre fiction, South Asian literature in English, and the digital humanities. My book, Fictions of Autonomy: Modernism from Wilde to de Man (2013), is published by Oxford University Press.
Upcoming conferences and talks
- November 17–20, 2016. MSA 18.
I’m attending the Society for Novel Studies conference in Pittsburgh this week. I’m on a panel organized by my colleague Rebecca Walkowitz on “Genre Fiction and World Literature,” with papers by Sarah Chihaya (Princeton), Jessica FitzPatrick (Pitt), and Philip Joseph (University of Colorado–Denver). The panel is in the D session, Saturday at 2:30 p.m. in Carnegie Room I-II.
My paper is called “Cosmopolitanism before and after the Omnivore,” and I’m going to argue that the rise of omnivorousness as a high-status disposition enables one kind of cosmopolitan science fiction—exemplified by Ghosh’s Calcutta Chromosome—while inviting us to forget the worldliness of lowlier forms dating back to the earliest pulps. Visualization:
Every year, I teach Early Twentieth-Century Fiction. In the first lecture, I tell my students that the baseline fact about fiction in this period is expansion: more books, more readers, more writers. It is this expansion which makes possible the diversification and hierarchization that characterize the literary field in the twentieth century. And every year, I want to illustrate these claims quantitatively. It seems straightforward enough: surely, somewhere in my small pile of book-history books, there would be a table of figures of fiction production over time that will let me substantiate this straightforward point. But the best I have managed so far has been disappointingly vague and broad-brush, cobbled together from a table here and an offhand summary there in work by book historians.
For my research on popular fiction genres, I have been working through Publishers’ Weekly in the decades on either side of 1900. It has the additional convenience of being digitally available, since scans of the yearly volumes are in HathiTrust (and, mirabile dictu, actually organized under a single catalogue entry). PW is one of the sources for the figures I have seen, since it kept a running bibliography of new books, and every January printed an annual summary of the past year’s production. Since I was reading those annual summaries anyway, I decided to transcribe PW’s tallies of yearly fiction production. Perhaps it would be possible to automate the transcription, but trying to get, say, Tabula going here was more work than just writing down the numbers in a CSV file myself.
It occurred to me that this transcription might be useful to others, and that it wouldn’t hurt to make it possible to build on it. So I have put it on github. I didn’t have the time to transcribe the full tables, so there’s work to be done to get PW’s other categories transcribed. And other fundamental time series for the literary field would be equally useful: statistics from other countries, of course (I am also going through the Publishers’ Circular myself); but also anything about readership or the book market.
Descriptions of the courses I’m teaching next semester are now up on the teaching page. I am offering Early Twentieth-Century Fiction, as I have in past years, as well as a new course, a seminar on “Science Fiction in Print from Pulp to the Present.” I’m very excited about both courses. I’m still working on the syllabuses, which will be linked from those pages as soon as they’re ready. In the meanwhile, my Early 20th-Century Fiction syllabus from 2014 is online; this year’s course will not be identical, but it will be broadly similar. I taught science fiction as a 300-level course in 2013; the 400-level seminar will have different readings and requirements but quite a bit of overlap in terms of themes. I’m always happy to hear from students who are interested in either course.
I’ve updated this site with a minor redesign. I switched to using Hugo to generate the site. Hugo’s speed advantage over Jekyll was a big draw, despite its tragically disorganized documentation and some frustrating minor limitations. I’ve tried to preserve all the old links, except for some reorganizing in the blog-post categories; all the posts are still there, but I eliminated some redundant categories, and category listings themselves have moved. I have also updated my page on LaTeX and digital documents with extra remarks on markdown and R markdown. The page is no longer headed “Typography or death!” though I continue to stand by that position.
This post is a discussion and partial replication of Ted Underwood and Jordan Sellers’s fascinating essay “How Quickly Do Literary Standards Change?” (available in preprint and discussed on Ted’s blog). Hats off to Ted and Jordan, who have contributed something really remarkable here, not just in their provocative arguments but in the data they have made usable by others. It’s one thing to circulate data and code so that someone can in principle re-run your scripts—though that is already a lot—and quite another to make the data accessible enough for fresh analyses. The latter is a very demanding standard, too demanding for everyone to meet, I think. But it is what is required to let others build directly on your results. Even more importantly, it’s what’s needed to make research results pedagogically available. As I argue in an essay I’m working on now, any quantitative methods pedagogy should—must—lean heavily on the results of research. In the immortal words of Miriam Posner, “It’s just awful trying to find a humanities dataset”: one of the best ways to address this challenge would be to make good research data available in recirculable, easily accessible form.
So consider this post partly pedagogical in intent: I want to show that Ted and Jordan’s replication repository is already an excellent dataset and could be the basis for a lesson in automatic classification. What I want to emphasize here is that their work allows us to breeze right past the data-wrangling and straight into the analytical substance. This may not be entirely obvious from their python code, so I’m going to try to make it clearer by doing the whole thing in R instead.
Rather than give all the technical detail, I’ll only show the R code where it makes a point about the analysis. The full R markdown source for this post is in this repository. Feel free to skim right past the R listings anyway. This is a literary theory and data analysis post that happens to have some software details in it, not a programming post. Here we go…