The Wide Cooker

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. Additionallyshe 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?

The Wide Cooker

2 thoughts on “The Wide Cooker

  1. Hi Lisa,

    I really appreciate your thoughts on Manovitch’s notion of “wide data,” as this was for me one of the more striking concepts offered in the Cultural Analytics manifesto, as you describe it. I think it really does offer a kind of “vibrational nexus” (wonderful term!) between scientific and and humanistic inquiry, insofar as it seems to describe what we would otherwise call textual analysis – attending to the specific specific features or attributes of a single text, or of small set of texts – by other means. Rather than relying on the subjectivity of the individual scholar or critic, a wide data approach seems to disperse the analytic activity, routing it not through an individual sensorium, but rather through a large dataset, threading together the particular and the general, the humanistic and the scientific. But even as I find this a compelling approach, I have to wonder, whither perception? Perhaps this is my own attachment to humanistic inquiry betraying itself, but I have to wonder what is gained and what is lost in the effort to transform textual analysis into a data-driven methodology.

    Does a wide-data assessment of a given text trump an individual critic’s affective or perceptual response to the same text? Might we lose something of a text’s affective freight in the rush to conceptualize it as a set of ‘attributes’ to be mapped, parsed, algorithmically compared? Do we not find ourselves once again in league with those late 19th and early 20th Century champions of mechanical objectivity who sought to outsource the work of perception to the camera in an attempt to eliminate human error and subjectivity (an elimination that was, in the end, more of an elision)? How are we to account for the social, cultural, and historical conditioning of the sensorium, and the different ways in which it touches and is touched by particular texts (that is, how do we account for the ways in which a given text impresses upon some bodies more than others), in the turn to analytics? If the goal is to retain the humanistic concern with the particular, then it seems important that we find ways of preserving the particularities of perception, affect, and sensation, keeping close at hand the question of how certain bodies are touched and moved by particular images, and indeed datasets. The immense datasets generated through contemporary state surveillance practices, for instance, surely touch some bodies more than others, setting in motion a lopsided economy of contact that implicates racialized, migratory, and displaced populations most of all. There are particularities of the flesh and skin that, for me, remain salient in the contemporary moment, and there is something in Manovitch’s turn to Cultural Analytics – something in its expansiveness, its preference for the language of ‘features’ and ‘attributes’ – that to my mind risks precisely these particularities. As we develop new methods that aspire hold the universal and the particular together, we ought still to ask what (or whose) universals, what (or whose) particulars?

    Liked by 1 person

  2. danbyd1 says:

    Hi Lisa,

    I really like your understanding of the articles, especially when it seems to relate to your research topic. While Manovich’s notion of wide data may provide a way out of data classification boundaries, I believe that this very method may constitute a boundary in and of itself. Not only this method has the potential to ignore data specificity, but it also optimistically ignores its own limitations by claiming to incorporate humanities’ concerns with the particular.

    In other words, I do believe that wide data collection is still an act of classification, and even an act of classification of itself. Having said that, I still believe that this type of data collection can be of interest since it does classify information into “clusters” and can be successfully used as such.

    Liked by 1 person

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