This week, Lisa Gitelman’s “Raw Data is an Oxymoron” and Lev Manovich’s “The Science of Culture?” introduce the data studies as a contested and multifarious field of study. Gitelman’s piece seems to address an imagined scholarly community that presumably treats data like a fresh pair of glasses: a clarifying, objective, and unquestionably useful tool to see the world. The thrust of Gitelman’s argument is to implore scholars to pay attention to the “pre-cooked” hermeneutics of data-vision and to point out the hegomonic ways in which data and other empirical tools have evolved over time. Additionally, she draws a throughline between science studies and data studies by pointing to how objectivity in both cases is mythical.
On this last point, Gitelman and Manovich both treat data studies as a vibrational nexus between science and the humanistic study—an idea that has really strongly resonated with me in all of my own research. As I think about the possibility of using data visualization as a critical lens from which to consider either climate discourse or the circulation of fetal images, it becomes imperative for me not to pick a scientific view over a humanistic one and vice versa. I really want to hold these two tensions together: the long, globalizing view that data can offer (which will possibly be generalizing but maybe also generative) and the potentialities of data to speak to human actants, or what Manovich refers to as the “individual and particular” (Manovich 2016, 9).
Gitelman says that the imagination of data is always “an act of classification,” (Gitelman 172) and I believe that to be mostly true. But what I sensed after reading Manovich’s Cultural Analytics manifesto is that classification doesn’t have to constrict our study. Manovich’s call for a “wide data” argues against the categorization of data into discrete dimensions or variables. And while he doesn’t state it explicitly in this essay, I think what he means is that there is a use for views of data that are continuous rather than discrete. It reminds me of something Alan Liu once said about his own work in the digital humanities, where rather than trying to classify and plot the sentiments we encounter from large data sets, we can construct heat maps and look for hot spots.
I can think of ways that a wide cooker could be useful in both my projects. Maybe I can mine a lot of instagram images under a hashtag like #globalwarming and allow clusters of affiliation to form in ways that they could not if I just plotted them temporally or by geolocation. Or maybe I cull information from several pro-life websites and try to simulate my own anti-abortion page using textual and visual data. I also think Professor Sakr’s mosaics would actually be a great example of “wide data.” What other kinds of “wide cooking” could we come up with?