Categories
DH Pedagogy UVA Collaboration

Why To Teach Students to Not-Read Novels

[Enjoy this guest post by James Ascher, doctoral candidate in the English Department at University Virginia. He came to W&L to give a workshop in Prof. Taylor Walle’s ENGL 335 course through a Mellon-funded collaboration with the Scholars’ Lab at UVA. More information about this initiative can be found here. His post is cross-listed on the Scholars’ Lab blog.]

This post has a simple argument: if you teach novels, you should teach students to not-read novels. Now, before you get concerned, I’m not arguing against teaching literature or avoiding novels altogether; the hyphen in “not-read” means a method, not a rejection of reading. Indeed, my whole argument is based on the idea that students need help returning careful attention to texts, but faced with a deluge of texts, we teachers ought to show them how some professional literary critics not-read, or as others call it “distant read.”

What is a novel anyhow? A reasonable question to ask your students as a semester goes along, since it seems to be a long form of prose that comes to dominate what we now consider literature. If one is to believe Amazon Rankings, then the most read forms of electronic books and the most purchased books remain novels (at least when I wrote this, but I’d be surprised if it changes). This wasn’t always the case and part of the history of the novel is its commercial success. Franco Moretti makes the case clear in his Graphs, Maps, and Trees where he argues that the rise of the novel can be studied using charts, as he does in his previous work Atlas of the European Novel (Moretti’s response, which is more useful.) Building on the idea of charting large-scale phenomena over time, he notices that novels rise (fig. 1).

Fig. 1 rises of novels from Graphs, Maps, and Trees

Fig. 1 rises of novels from Graphs, Maps, and Trees

Something about the form, the markets, the people, or something else means that this particular literary form comes to dominate, so whatever time period you consider, it’s worth considering what was going on.

Its well established that the English novel rose first—the form seems to have been invented there—so even in a non-English classroom, it’s worth considering how those novels were imported into a broader literary discourse. Luckily, the text of most eighteenth-century English novels are freely available online. We have a fortuitous confluence of the intellectually important materials that have become technologically available (a careful reader will note that this is one possible explanation of the novel’s rise as well). But, how can you study the features of a whole genre? An easy way is to read them by putting them on your syllabus, which I encourage, but after reading them you can look back at the whole syllabus and chart the topics that come up.

Fig. 2 Novel topics

Fig. 2 Novel topics

To test this idea, I presented for Taylor Walle in her English 335 “Radical Jane: the Politics of Class, Gender, and Race in Austen’s ‘Polite’ Fiction.” Her course asked students to think about how English novels formed identities and related to the growing issues of British society. It seemed like a great chance to try some topic-modeling across her entire syllabus, the chart was produced with ten topics across the whole class (fig. 2). You can see the lesson here if you want to reproduce the work.

After demonstrating how the method works, we turned to this chart and looked for topics that crossed the texts. The works read for the class and on the chart—from left to right—are Emma, Northanger Abbey, Pride and Prejudice, Sense and Sensibility, A Sentimental Journey, A Sicilian Romance, and A Vindication of the Rights of Woman. You can see, and the class immediately saw, that the topics broke at the boundaries of books. You can see that many of the topics to detect specific books, but a few cross boundaries. Now, the topic used in topic modeling isn’t quite the normal sense of the word “topic.” It means a list of words with probabilities that, when they occur, signal the topic that is that list of words. The topics by their top words are,

time made heart letter moment feelings mind spirits happiness
present long felt thought affection left hope return day love
situation ...

elizabeth darcy bennet jane bingley wickham collins mrs sister
lydia catherine lady lizzy longbourn gardiner father family
netherfield kitty charlotte ...

miss mrs good great dear young make time room house give day
thought friend heard man home replied pleasure hear ...

julia marquis door ferdinand madame hippolitus castle heart duke
heard marchioness length appeared light night discovered time part
count scene ...

man life love woman character mind world society sense opinion
great beauty good present taste nature understanding husband
degree subject ...

emma harriet weston mrs knightley elton thing jane woodhouse miss
fairfax frank churchill body hartfield bates highbury father sort
harriet's ...

fleur paris monsieur poor hand count thou man set told madame good
french thy heart lady tis put made nature ...

elinor marianne mrs dashwood edward jennings sister willoughby
colonel lucy john mother thing brandon ferrars barton middleton
marianne's lady town ...

catherine tilney isabella thorpe morland allen general henry bath
eleanor catherine's brother james father street hour northanger
abbey john captain ...

women men reason virtue sex respect mind duties affection make
heart children power render human virtues true allowed till duty
...

As far as the course goes, the first topic seems to cover what all these texts have in common, but notice everything isn’t perfectly lined up by novel. The topic beginning “julia marquis door” clearly comes from Sicilian Romance, but also hits on some later chapters of Northanger Abbey. Why would that be? Well, if you know the texts, you realize that some of the same Gothic themes occur in both texts and they use the same words.—“Dear students, can anyone bring us to where in the text this happens?” And, we enter the realm of the normal literature classroom.

By presenting a broad view of the texts, built by a computer algorithm, but out of the words of the text, we invite students to re-read works. Not-reading becomes re-reading and presenting words across the entire corpus puts students into partnership with technology to ask what it is about the form of novelistic prose that makes it popular and speak to social issues. Furthermore, we also encourage students to be critical of the results of computerized analysis. Several students noted that these topics were obvious, having read the works, and that they could have come up with them by hand, which is—of course—true. It’s easy to scale these up beyond what you could do by hand, but seeing how they reflect what is accurately in the text shows that they provide some purchase on truth and also suggests what might be going on with other computerized analyses. One way we imagined it was that the computer applied an obvious rule at a fine level of detail. If we follow the same method, but only for Emma by paragraph, we get a much messier chart (fig. 3), but seeing that chart, students can begin to engage with both literary texts and computers that help them to not read—to ask what it means and what can be done with it.

Emma by chapter

Fig. 3 Emma by paragraph

Categories
Announcement DH

Announcing Our New Mellon DH Fellow: Sydney Bufkin

Sydney Bufkin Headshot
We are happy to announce our new Mellon DH Fellow, Sydney Bufkin. Sydney is a familiar face for many of us at W&L as she’s been teaching in the English Department for the last few years. Sydney received her PhD in English from the University of Texas at Austin with a specialization in nineteenth-century American literature and reception studies. At W&L, Sydney has taught a range of courses in the English Department and Writing Program. She specializes in digital approaches to pedagogy and has received a DH incentive grant for her multi-modal writing assignments. Her interest in computational approaches to literature manifests itself in her own research on a corpus of reviews of nineteenth-century purpose fiction and hopefully in future DH courses!

Sydney will being on June 12. Please welcome her to her new role in Leyburn Library.

Categories
DH Trip Report

Days of Networks & Social Capital

Enabling faculty and students to attend high-quality workshops is one of the most valued opportunities supported by our Mellon DH grant. I recently returned from a 4-day trip to Philadelphia with Jon Eastwood (Laurent Boetsch Term Professor of Sociology) and three brilliant undergrads at W&L: Elena Diller, C’17 (Sociology major with minors in Poverty Studies and Women, Gender, and Sexuality Studies ), Dani Leon, C’18 (Politics/Sociology double major with a minor in Poverty Studies), and Kassie Scott (English/Sociology double major with a minor in Poverty Studies). Jon attended a workshop on Casual Inference with Directed Graphs taught by Felix Elwert; Elena, Dani, Kassie, and I attended a workshop on Social Network Analysis (SNA) taught by Steve Borgatti. The latter workshop was recommended to us by Professor Megan Hess (Accounting), who had taken a workshop with Borgatti before. Both short courses were hosted by Statistical Horizons.

This post will focus on the SNA workshop, though we all learned a fascinating amount about Directed Acyclic Graphs from Jon Eastwood over meals and during the long drive back from Philadelphia. Jon reports that he will be bringing what he learned into the classroom this Fall Term as part of the course he will be teaching on Bayesian data analysis.

The workshop was two intensive days, 9-5, that provided an in-depth introduction to SNA, particularly with the use of the UCINET software package developed by Borgatti. The W&L students were the only undergrads in attendance, which is an indicator of the quality of opportunities that this university provides students. The class size was 28, consisting mostly of faculty, professionals in fields such as public health, government, and social work, as well as a few grad students. Medical school faculty were prominently represented. The W&L undergrads, not shy about asking questions, easily kept up with their more educated classmates.

The instructor, Steve Borgatti, is one of the leading scholars in the field. Borgatti is currently president of the International Network for Social Network Analysis and has published a wealth of literature that has received over 40,000 citations. Borgatti is a humble man whose jovial and bearded presence in blue jeans never conveys any hint of his standing within academia. The pace of the workshop was intense, thought provoking, and exhausting but well worth the effort. Borgatti structured the 2-day workshop as a mixture of lecture (with over 200 slides) and hands-on practice with UCINET. Attendees came away with an understanding of how to import different data structures into UCINET and the many network measures provided by the software.

As a Windows program, UCINET works well on Macs via the Windows emulator Wine. Step-by-step instructions for installing on a Mac via WineBottler work really well. My one bit of advice on installation for Macs is to make a symbolic link to the UCINET data folder, which is installed by WineBottler at “/Users/[username]/Library/Application Support/com.yourcompany.yourapp[numeric string]/drive_c/users/[username]/My Documents/UCINET data”. A symbolic link from your Desktop or elsewhere will make it easy to access the data files generated by UCINET.

UCINET is a good alternative for people who prefer a menu-driven interface over the command-driven approach of R. I would definitely recommend UCINET over Gephi. For my own SNA research, I suspect I will stick with R’s igraph package but I’m going to explore the possibilities of UCINET. As far as software goes, UCINET is simple to learn. It mostly works the same way for any feature or network measure: find the desired option under the menus, load the data set, set any additional options, and run. UCINET also comes with a command-line interface. The extensive HELP file provides references to scholarly articles for a large number of functions provided by the software.

Due to the computational nature of SNA, people new to the topic often approach it as a methodology or a tool. Borgatti emphasizes a distinction between the “theory of networks” as a way of explaining why networks exist and “network theory” as a way of understanding the consequences of networks. Borgatti stated that most SNA studies are about the consequences of networks.

A significant part of the second day of the workshop focused on egonets, which is a network focused on one person (the “ego”) and the connections that person has with others (the “alters”). Building on that discussion was an examination of social resource theory, associated with the scholar Nan Lin. Another conception of social capital is articulated by scholar Ron Burt via network analysis in the form of structural holes.

A variety of measures exist for SNA but I’m finding in my own research into literary networks that absorbing the network theory before embarking on the actual data analysis is having a positive impact on my understanding of the patterns and connections within literary publishing. Previously I have thrown my data set into network visualization tools as a means of exploring the data but I now feel better equipped to think about exactly which measures I should examine for better understanding the interchange among editors and authors in producing the outcome that is a literary magazine.

Borgatti covered a great number of topics in the two days, including a very mathematical discussion of the types of regression that can be done on network matrices. He also gave an overview of exponential random graph models (ERGMs) as a computational model for predicting the absence or presence of connections within a network.

SNA is not just about contemporary issues. Borgatti used the data set from a well-known study on the relationship of marriage and business ties among 15th century Florentine families that led to the rise of the Medici. Another historical topic covered was a study of 12th century Russia and the emergence of Moscow as a dominant location due to trade routes. In the latter study, we see that SNA can be applied to areas other than just interpersonal relationships. As Borgatti says, “Networks are everywhere” and network studies are found in the natural sciences, the social sciences, and the humanities.

I already mentioned the professional organization INSNA, which tends to focus on the social sciences. Another organization, the Network Science Society, focuses more on physics, computer science, biology, etc. While there is overlap among the two professional organizations, Borgatti describes the two as still “having a different feel”.

I’ll wrap up this report with a suggested reading. Just about anything by Borgatti is worthwhile, and a good starting point is his 2009 article in Science on Network Analysis in the Social Sciences. I highly recommend reading about the theories behind social network analysis and how those apply to your own research questions. Grasping those concepts before jumping into the tools of SNA will be very beneficial. Borgatti and his colleagues at the University of Kentucky’s LINKS Center for Social Network Analysis also have published an insightful article on Social Network Research: Confusions, Criticisms, and Controversies [PDF preprint].