Having an unpleasant reaction to Lisa Gitelman’s bad shrimp analogy

Digesting Lisa Gitelman’s admonition about data and rawness, most of her argument went down fine: after all, disciplinary cuisines aside (171), what she most stridently calls for is the disclosure of epistemological and methodological concerns, or recipes, and for us to discard the notion of any particular palate—whether one makes purportedly subjective or objective claims for a living—being truer than another. However, the jumbo shrimp analogy (168) stuck like a bone in my throat, in spite of how cheerfully Gitelmen provides and then discards it. I found it ill-conceived, and here’s why: “raw” is an absolute term, and while it may be an inaccurate and blinkered way to refer to data, it doesn’t really bear comparison to “jumbo,” which is a relative term for shrimp. These shrimp found here are more hefty than the already-large shrimp found near, which are in turn more generously-proportioned than the pedestrian, moderately-sized shrimp found over there—thus, “jumbo” in the first case. 

Mark Twain famously attributed to Benjamin Disraeli the quip fond to many a wag: “there are three types of lies: lies, damned lies, and statistics.” To cite either as the author, while often done, seems superfluous—it is such a truism that it doesn’t matter on whose authority we have it. And Gitelman and Manovich agree; both argue that the essential problem with data analysis, no matter how clever, is that sampling facts is a ticklish business. Carelessness and perfidiousness in the case of data collection and analysis end the same, and so whether by accident or design, prejudiced samples produce less generalizable results. The operative principle, then, is nuance, and not only acknowledging that data is always “cooked” but learning how to select it, season it, how to render most effectively the particular characteristics of interest. Thus, I would argue that in his call for “wide data” and in hers for frank disclosure of disciplinary predilections, Manovich and Gitelman in fact do apply the wisdom of fishmongers and think about scales of data as useful metrics. The jumbo shrimp, after all, is only oxymoronic in one iteration—elsewhere the langoustine and the tiger prawn produce none of the consternation apparently plaguing the American shopper.

I think it bears pointing out that the issues Gitelman has with jumbo shrimp and raw data seem to come down to the notion that somehow we’re being tricked into thinking the thing at hand it something other than it is: so, the shrimp is not a shrimp, it’s a jumbo shrimp, and data isn’t contingent observation about the material world, it is unbracketed, unadulterated truth. And while conflating industrial aquaculture and the empirical or positivist bent may work on many levels—after all, industrial aquaculture and many of its devastating consequences originate in notions that the world is entirely knowable and always improvable—there are some close to the surface in which that shorthand just doesn’t.

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Having an unpleasant reaction to Lisa Gitelman’s bad shrimp analogy

2 thoughts on “Having an unpleasant reaction to Lisa Gitelman’s bad shrimp analogy

  1. lisayhan says:

    Hi Naomi,

    I very much enjoyed this meditation on shrimp, both for your apt extension of the analogy and for the hypocrisy you’re pointing out in Gitelman’s piece. I was also bothered by the lack nuance in “raw vs. cooked,” particularly the way that creates a simplistic binary and inherently suggests a good vs. bad qualification. The idea that all mediation of information (including data collection) is biased is easy enough to agree with, but the real meat in both of these essays was their recipes for, as you put it, the selecting, seasoning, and rendering of data. I think what frustrates me about a lot of the discourse on empirical methods in the humanities is this continued harping on making sure people aren’t being “tricked” into using data uncritically. Obviously it’s an important point, but I think it sometimes stunts our ability to ask more interesting questions about data in the humanities. This is why I liked Manovich’s piece a bit more than Gitelman’s; there’s no pretense about needing to rehash debates about whether or not data is good or bad—rather, it takes the use of data as its starting point and offers something more about where to go from there.

    Really, I think there are two different trajectories at play here. On the one hand, there are those who see data as a new tool that needs to be judiciously applied to help answer old questions. To me, it seems like this essentially amounts to self-reflexively using data as rhetoric while acknowledging the pitfalls of using data as rhetoric (experimenting with different seasonings on a tried and true dish, writing down what was good about it and what was bad about it). The second trajectory seems to be about using data to change the very questions themselves (changing the dish altogether). What is the end goal of creating a simulation, for instance, if it isn’t to prove a point or support a logical argument? What is the point of glitch, if it doesn’t clarify information for us? Is it a form of art? Is it an appeal to affect? How can data help us adjust our goal posts and come up with new types of questions?

    Liked by 1 person

  2. Ali says:

    Interesting to read your view of her choice of analogies, but I’m not sure I quite understand what your main issue is with her issue with the concept of “raw” data. Lisa mentions the hypocrisy of Gitelman in this piece, but again, I’m having trouble pinpointing exactly what this hypocrisy is— perhaps the term jumbo shrimp doesn’t quite provide the perfect analogy with raw data. No analogy ever is perfect. But Gitelman’s main point about raw data (at least it seems to me) is not merely that we’re being tricked into thinking data is something it isn’t, it’s instead that acting like data can ever be raw (which is to say that there is some absolute and objective truth) is a foolish assumption that will lead to far too much being taken as a given when consuming any sort of study, statistic, etc. So yes, “raw” is absolute when talking about food and “jumbo” is not, but isn’t Gitelman that even though we generally come to the consensus that data can never totally be “raw” we still more or less accept data to be objective.

    I like the way she describes the way data is talked about: it’s “collected” and “mined” and “stored” and “processed” and “entered” and “compiled” and of course, it’s “interpreted” but as Gitelman points out it’s this last step that is often taken for granted— and she cites Manovich as saying that data does not merely exist but rather it is “generated.” Later on in the piece she makes the connection to the scientific knowledge, which is not discovered, it is produced. When we look at something like the census Tyler posted last week, that information was not discovered in this raw form, it was in fact produced, much like information in any type of scientific endeavor. There are so many considerations that go along with the census, there is an element of manipulation (whether intentional or not) when questions are worded a certain way, or where the sample size is taken, who are the people behind it, etc…

    But you go on:

    “Thus, I would argue that in his call for “wide data” and in hers for frank disclosure of disciplinary predilections, Manovich and Gitelman in fact do apply the wisdom of fishmongers and think about scales of data as useful metrics.”

    I suppose you’re right here, about the jumbo shrimp metaphor, and the tactics of these two scholars falling in line with fishmongers. But I don’t think it’s hypocritical, I find their tactics to be more about transparency, to take the absolute quality away from data.

    Liked by 1 person

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