“Digitizing the Ephemeral: Reconstructing Museum Exhibitions Attended by Jane Austen”

Professor Janine Barchas, University of Texas





Wednesday, April 19, 2017

4:30 PM



Professor Barchas will take us through the making of a major digital humanities project, the digital gallery What Jane Saw, which offers a room-by-room reconstruction of two public art spectacles witnessed by Jane Austen.  The site allows a modern visitor to walk through the Shakespeare Gallery as it looked in 1796 or tour the Sir Joshua Reynolds retrospective held in 1813.  Professor Barchas will give a curator’s tour of both digital exhibitions and explain the method and research behind the gallery and its historical reconstructions.  She will also discuss her team’s current work with Virtual Reality.



Refreshments served

“Novel Analytics from James Joyce to the Bestseller Code”

Professor Matthew L. Jockers, University of Nebraska

Thursday, March 2 4:30 PM

Russell House

Refreshments provided


In a New Yorker article in 2014, Joshua Rothman asks, provocatively and rhetorically: “If you’re an English professor, how should you spend your time: producing [close] ‘readings’ of the literary works that you care about (art), or looking for the [distant] patterns that shape whole literary forms or periods (science)?” Rothman’s parentheticals, “art” and “science,” make for a good editorial hook, but they frame a misleading and false dichotomy.  The emerging debate in literary studies pitting traditional scholarly practices of close reading against digitally oriented methods of “distant” reading is a nonstarter. What gets disguised as an argument over method (close vs. distant) and discipline (art vs. science) is, in fact, an argument about interpretation and the ways that literary scholars collect and prioritize evidence. In this talk Jockers proposes a methodological reconciliation that understands large scale computational approaches to literature as entirely consistent with traditional practices of close reading.

Data, Computing and Journalism

Andrew B. Tran

Monday 2/13, 4:30 PM, Allbritton  103
Snacks and refreshments will be available

Increasingly, journalists are turning to tools that were once solely the domain of data analysts and computer scientists to create compelling visualizations and enhance their storytelling. Newsrooms are using accessible technology to process big and open data to assist in investigations, keep citizens informed, and help make institutions accountable— and they’re often following the tenets of data science, like making their work transparent and reproducible. It’s important, now more than ever, that data not be hidden by government agencies from the public so that it instead might be used to illuminate the truth. Andrew B. Tran

Andrew,  currently a Koeppel Journalism Fellow at the Center for the Study of Public Life is the senior data editor of Trend ct (http://trendct.org/about/ a CT Mirror affiliate).  He was a founding producer of The Boston Globe’s Data Desk where he used a variety of methods to visualize or tell stories with data. He also was an online producer at The Virginian-Pilot and a staff writer at the South Florida Sun-Sentinel. He’s a Metpro Fellow, a Chips Quinn Scholar, and a graduate of the University of Texas.

Gender and Power Dynamics in Elite Discourse: Evidence from the U.S. Supreme Court



Assistant Professor of Political Science and Sociology
University of Iowa

Do men talk over women even in elite settings? Whether it is the corporate board room or the kitchen table, gender dynamics affect the way men and women talk to one another. One would hope women who hold public office do not face similar biases, but this study shows, even on the Supreme Court women are spoken too differently than men. Using R, Python and a high-performance computing cluster, we obtained the text and audio from over 500,000 utterances during oral arguments from 1982-2014.  Ultimately, we show male Justices and attorneys display verbal and non-verbal dominance towards female Justices.
This study is part of a larger research agenda that emphasizes the importance of elite non-verbal behavior, such as changes in vocal pitch. Unlike other time series, vocal pitch is incredibly stable at short intervals, meaning it can be extracted using a windowed autocorrelation function. While there are a number of other variables one can use, vocal pitch has been shown to be associated with dominance, even in institutional settings, making it particular useful for this application,  and one of the first to highlight “Panel effects” long noted by judicial scholars. We show vocal pitch is not only indicative of underlying gender dynamics, but it also influences voting behavior. With that said, there are a number of other ways audio can be used for research, ranging from speaker segmentation to supervised classification. Part of the presentation will lay an important foundation and provide some guidelines for those interested in using these techniques for future work.