I watched a TED talk by Aaron Koblin about “artfully visualizing our humanity” but I think his talk could also serve as a “how to” guide for the best way of thinking about the digital humanities. In it, Koblin speaks mainly about accessing large data sets and crowd-sourcing, both of which are topics that might not seem applicable to the humanities. But, one of the things I remember about Professor Michael Witmore’s work is one of our conversations regarding statistics. Professor Witmore was relaying to me a conversation he had with a professor of statistics about trying to find the best method for “artfully visualizing” Shakespeare’s corpus and literature in general. The statistics the other professor recommended for Witmore’s project were similar in kind to the methods used on the human genome project but Witmore’s study of literature was infinitely more complex than DNA. That literature could contain so many variables is, at first glance, hard to conceive of but becomes easier as one considers that individual words, even common “filler” words, are counted as well as phrases, clauses, etc. In this respect, even a contained data set like Shakespeare’s corpus proved to be much hard to visualize clearly and meaningfully than scientific studies. The “largeness” of the humanities then, is a question of scope; a question that has had trouble being answered as scholarship moves into the digital realm.
Tag Archives: EEBO
I think this is the last old post I had to write. This is focused on my final project for Prof. Witmore’s class in May:
Over the course of a semester, Professor Witmore introduced our class to writings about relational patterns and networks, then subsequently applied them to the study of literature. We read books such as Graham Harman’s “Prince of Networks: Bruno Latour and Metaphysics”, Franco Moretti’s “Graphs, Maps, Trees: Abstract Models for a Literary History”, and Alexander, Ishikawa, and Silverstein’s “A Pattern Language: Towns, Buildings, Construction” which slowly coalesced in my mind and led into my final project; a Java program designed to help render Docuscope quality text from a plain or formatted transcription.
Whilst going over older materials I have stored, I came across an article by Witmore and Hope dealing with the Romances or Late Plays of Shakespeare. It was a journal in Early Modern Tragicomedy (2007), the twenty-second installment of the “Studies in Renaissance Literature” series. In it, Witmore and Hope write that John Fletcher’s “definition of genre not only specifies what must be in a play to qualify it for membership in a genre, but also what it must lack”. Fletcher’s postulation of what must be present, but also not present, to belong to a genre is similar to what I tested in my last post by adding and removing the characters that plays were named after. In review, it was a mixed bag of results leading towards both the idiolects of the play’s main characters and the texture of plays themselves as the primary reasons for clustering. However Witmore and Hope’s article sparked a new thought process in my head. Since readers and critics as far back in time as Fletcher have noted the peculiar differences between the Romances and the rest of Shakespeare’s corpus, does that mean by following Fletcher’s formula that adding or subtracting characters will affect a play’s genre classification? One of Witmore’s earliest views from Docuscope was a simplified dendrogram noting a genre specific clustering result from an unbiased word tagging program. I have since noticed particular genre related movement in the Romeo and Juliet post, but I am now trying to combine a three hundred year-old literary critic’s mind with a modern machine’s processes. In sum, I wish to determine if the idiolects of the main characters assist the Romances in clustering differently from the rest of Shakespeare’s corpus and whether or not the isolation of these particular characters’ lines from the whole play reacts with the genre specific clustering already present. Continue reading