Although most of Olga Scrivner’s talk on “Language Variation Suite and Interactive Text Mining Suite” was somewhat over my head, it offered an interesting contrast to Lauren Klein’s talk on “Speculative Designs.” Scriver’s program is of course more in line with Playfair’s preference for clarity and accuracy than Peabody’s preference for experience. Scrivner’s goal in creating LSV was to make codes she had written more user friendly, and LSV is, in fact, fairly user-friendly for folks with some statistical knowledge.
LSV allows users to generate various visualizations and tables based on a csv import. For example, users can create a “conditional inference tree” which I understand as a more complex cluster diagram. Running the Labov data allows the user to see that R-use was most similar at Macy’s and Saks as well as the frequency of R retention and R deletion at Macy’s/Saks and Klein’s. Users can also generate a “random forest” graph, which makes the most significant variables visible.
Most people in the room seemed to know about Labov’s data. When Scrivner initially mentioned Labov, someone responded by saying “fourth floor” in a cartoonish New York accent, a joke that to me seemed so out of context as to be almost surreal. I googled Labov and quickly found out what his experiment was and why “fourth floor” was relevant, but the scene was a striking example of how a common vocabulary and canon of knowledge is often a necessary foundation for a field-specific discussion. Since literary scholars worry a lot, and rightly so, about the literary canon, I forget that other fields also have shared narratives that define the group and allow for common ground.
A tangential story: BC required its MA in English students to pass a historical placement exam that required students to place ten excerpts within a specific time period based on the form and content of the piece. The exam required extensive general knowledge, but little deep knowledge. The day before the exam I was with a group of fellow students stressing about whether we had studied enough, and one student said dryly, “Well, we’re going to host a 2-hour study group on ‘Goblin Market’ tomorrow—so we should be ready after that.” We all erupted in laughter. Field knowledge leads to very strange jokes.
I tell that story mostly because it still makes me laugh. However, it does lead me to recognize the fact that as a humanities student becoming more acquainted with digital tools, there is a certain amount of laughing along while secretly googling that I have to do, and I assume others do as well. There were years of study that led up to the immediate understanding of a “Goblin Market” joke. I don’t have years of study in computing or statistics—not many of us humanities folks do—but there is value in full-immersion language learning, in laughing along and asking questions later.
