For the last week or so I have been collaging together small pieces readying for a guest presentation and workshop in Virginia Tech’s ENGL6364: Research Design in Rhetoric and Writing tomorrow afternoon, a presentation and workshop I’m calling “A Feeling for the Data Set.” I taught the class in 2020 and 2022, so I have a feel for it, and now—the 2024 & 2026 rounds—it is being taught by my friend and (former, in the sense that I no longer work at VT) colleague, Dr. Shakil Rabbi. The class is reading an old friend, Network Sense; my prompt for the visit is to talk about how to create a data visualization from a large, unwieldy data set.
As moving would have it most of my books and materials are in boxes in Ann Arbor at the moment, so I had to approach this a little bit differently and work only with what I had on these few small shelves at home. Thus, the setup sent me back to Evelyn Fox Keller’s book, A Feeling for the Organism (1983), a biography of 1983 Nobel prize winning geneticist Barbara McClintock, whose research on maize led her to insights about “jumping genes,” or gene transposition—the abruptive events that cause genetic mutations. With the purpose of contextualizing the lag in the reception of McClintock’s work, Keller wrote that her prose was alleged by some to be “difficult to follow” (10). Keller continues, “When she made these ideas public in 1951, in 1953, and again in 1956, in spite of the fact that she had long since established her reputation as an impeccable investigator, few listened, and fewer understood. She was described as ‘obscure,’ even ‘mad'” (10).
I won’t have time on Tuesday evening to go into McClintock’s ingenious research nor into Keller’s brilliant and accessible account on McClintock’s life and career. I will mention it briefly at the beginning, nevertheless, because it has provided me with what I consider an engaging, intuitive analog in the “A Feeling for” precept. Later in the book, in Chapter 12, “A Feeling for the Organism,” Keller wrote, “Over and over again, [McClintock] tells us one must have the time to look, the patience to ‘hear what the material has to say to you,’ the openness to ‘let it come to you.’ Above all, one must have a ‘feeling for the organism'” (198). The time, the patience, the openness, a feeling. Tuck this mantra away on a sticky note and you’d be surprised, as I have been, that it maps as research wisdom again and again, for me, in my own work, and for offering guidance and support to graduate students.
The way I remember it, A Feeling for the Organism first came up in CCR720: Interdisciplinary Influences on Composition and Rhetoric: The Making of Meaning, a class I took with Louise Phelps at Syracuse in Spring 2005. The class compiled with an endless, robust reading list, some of which was focused on “the making of meaning,” and much of which was peripheral, circulated in weekly optional sets of PDFs and in in-class discussions about cognitive psychology, distributed cognition, models of mind, comprehension, memory and forgetting, and more. I must have picked up Keller’s book and read it leafingly-flittingly. And then more recently, a couple of years ago, I picked it up again and read it more carefully after I heard the story that A.’s mom was at Cold Spring Harbor on the day McClintock, who was also working there, learned of the Nobel prize, about how they walked together to the building’s entrance that morning, congratulatory small talk, about how McClintock said it was occasion to have cupcakes at the cafeteria later in the day. “A Feeling for” has become more sticky, a more appealing premise, as I have aged into a career and with each successive research experience.
My picking up and extending “A Feeling for” is also meant to respond to what I have noticed at times an implicit argument that data analysis, data visualization, and big data fascination are appealing because they are, with the heave-ho of computing might, expeditious. Dromological determinism, we might call it, owing to Virilio. This attitude supposes that if you just feed enough interview transcripts or whatever into the software, it will do the “feeling for” part in a jiff and the researcher will have assertion-worthy baseline evidence upon which to build a dissertation, a book, a career, etc. This is akin to a party popper enthusiasm for generative AI, implying that you can dump the data in, wiggle your nose, and ta-da! To contrast this counterpart to “a feeling for” would amount to “a shortcut for,” and its byproduct is more of the mythology that your life will improve because of all of the time you have gained back.
Not everything has held on much less held together through the collaging. For instance, I spent some time looking at the Nobel Prize website and at the photo gallery, McClintock’s prize lecture, and the short banquet speech. The banquet speech is especially badass for McClintock’s suggestion that being so long ignored might be construed by some as painful and disappointing, it was, for her, rewarding to be left alone with her work and to have so much time to get to know (and name) each corn plant. She said,
“Subsequently, several maize geneticists did recognize and explore the nature of this phenomenon, and they must have felt the same exclusions. New techniques made it possible to realize that the phenomenon was universal, but this was many years later. In the interim I was not invited to give lectures or seminars, except on rare occasions, or to serve on committees or panels, or to perform other scientists’ duties. Instead of causing personal difficulties, this long interval proved to be a delight. It allowed complete freedom to continue investigations without interruption, and for the pure joy they provided.”
Following this framing gesture, which amounts to a few minutes on a book recommendation underscored with why “A Feeling for,” I plan to say a little bit about immersing with the data, which for me in the context of Network Sense and before that the dissertation was stacks and stacks of CCC PDFs, followed by the importance of making visuals, being invested in the imagetext problem, and learning from the dataviz examples modeled by others. Next, a few remarks on CCC Online Archive, on those early graphs, on fretting much later about extending the dataset from 20 to 25 years, and then on word watching and turn-spotting, which will segue to the applied part of the session, where everyone will in effect make a simple cluster map of keywords in their research, then attempt to resolve which of the terms are rising, falling, or flat, and which corpora would be appropriate for corroborating the guesses.
