 John Mohr University of California, Santa Barbara | The Duality of Theory and Practice in Seattle As I set about organizing the Theory Section’s program for next summer’s ASA meetings in Seattle, I decided to highlight some of the new theoretical work that is emerging in sites where innovative empirical programs are finding their footing. I think these are places where it is especially easy to see the duality of theory and practice at work. This is the idea that I would like to focus our collective attention on this year, and it is with that theme in mind I tried to create a set of panels that would explore some of the interesting edges between what we know and what we don’t know—to reflect upon some research areas where we need theory to help us understand what we are seeing at the same time that we need new empirical research to help us advance theoretically. |
In some ways, the balancing of theory and practice is always needed, but that need is also, I think, more pronounced in some places and at some times than others. My sense is that the embrace of edgy theory-infused empirical work is on the upswing in sociology, and I think this bodes well for sociological theory. I say this in part because I have been watching a number of new research programs coming into formation where scholars are finding the headroom to bring broad, smart, and interesting theorizing to bear on problems that are deeply embedded in, and indeed constitutive of, the design and conceptualization of the empirical work itself. Often, this research is more exploratory than confirmatory, and, as I will try to explain here with regard to the case of the new field of computational sociology, I think we can identify some of the reasons why this is happening more frequently now than before.
Three of the panels in Seattle will be Open Submission; all take on some part of this challenge. One is on theorizing perception (organized by Joseph Klett and Terence McDonnell), one is on theorizing relational sociology (Emily Erikson), and one is on abductive theorizing (Iddo Tavory). If it fits the topic, please consider submitting your paper to one of these panels. (For descriptions of the sessions as well as more details on how to submit your paper, see page 28). In addition, we will have two panels with invited speakers. One of these, organized by Marion Fourcade and Raka Ray, will be on Visualization and Social Theory. The goal of the panel is to ask the provocative question, “How can the vibrancy and resonance of sociological concepts be reflected in visual form?”
The fifth panel is also an invited panel, which I will organize in collaboration with Ronald Breiger and Robin Wagner-Pacifici. This last session has the title “Big Data/Big Theory,” and that is also the title of this edition of the Note from the Chair. In the remainder of this note, I will explain what I mean by this juxtaposition of terms and, in the spirit of offering up a more concrete example of my broader optimism, I will propose three reasons why I think the era of Big Data is likely to be good for theory.
Big Data
By Big Data, I especially mean to refer to the world ahead of us (and, indeed, increasingly all around us) in which important components of social life become digital in their essence. I am referring to the kind of world where many if not most of our texts never actually achieve materiality, but instead begin and end their existence as information organized into bits of data, stored, transferred, and occasionally projected on a screen. Or, as I see in the case of my seventeen-year-old daughter and her friends, we begin living some significant segments of our social lives in digitally mediated systems of exchange where the defining features of the interaction are precisely the affordances of digitization itself. Consider the humble selfie—a self-portrait, appropriately silly, tongue out, eyes crossed, captured at some moment, at some place, during some event, with some person, or in some group, and then liked online by some number of people from some group. Then gone. The image itself is never anything but data in electronics and it comes into existence, however temporarily, only because of and within this digitally mediated exchange experience. And it is not just the young. I confess I am scarcely better, though my digital worlds are more defined by citation counts, links, tweets, reads, comments, and downloads. For me, what archetypically defines this dimension of the digital world is that it is a style of social life that creates a digital footprint at the very moment and in the very expression of its occurrence. In short, I am especially interested in that nexus space in Big Data where social life itself exists primarily as data (and vice versa).
Of course, this type of digitization is always only partial. Material beings still exist on the sending and receiving ends of these digital systems and, in any case, only some components of social experience can operate through digital media. No argument there. Beyond this, there is the highly problematic matter of the actual accessibility, not to mention the ethicality, of analyzing all this data—and this opens up numerous other complications. But, setting those concerns aside for a moment, what fascinates me is that for the first time ever, we as social and humanistic scientists may gain access to what is essentially an overwhelming amount of high-quality data about the social and cultural world. For the first time, we may begin to approach the kind of relationship to data that a discipline like physics or engineering has with their terabytes and petabytes of highly precise information. I’m not saying, by the way, that this means the laws of physics will now explain society. In fact, I am saying just the opposite. Because we now have data to describe social life in such enormous detail, social scientists rather than physicists should try to figure out what this data means.
And I will say, appreciatively, from the standpoint of a sociologist who likes using formal data analysis, I think Big Data does have the potential to produce digitally accessible information that is far richer than anything social scientists have ever had or known before, and that some part of that richness will come from the fact that much of that data is produced within the very flow and practice of daily life itself. Instead of gathering answers retrospectively from standardized survey questions, Big Data can provide texts from spontaneous tweets, posts, or messages that are wound into dynamic conversations between friends or communities, thus allowing social scientists to capture social life in its natural richness as it unfolds in real time. High quality data could mean data that was created authentically, with complete textual (and visual or audio?) content recorded, all types of relational signatures captured, and precise temporal and geo-stamping included.
Moreover—and this is actually the most interesting thing to me—Big Data sources can provide us with articulated access to complex levels and systems of meanings. Data is not limited to attitudes or opinions registered retrospectively in surveys; instead, Big Data can allow us to strategically examine different types and forms of meanings, from simple sentiments to complex thoughts, from immediate reactions to deliberative reflections. And, in contrast to the era of survey research, rather than focusing our attention on designing sampling strategies and systems for retrieving statistically reliable extrapolations of data, we can now have access to nearly entire populations of participants or complete universes of events, which means that we can select particular (theoretically meaningful) components of social/cultural systems for our analysis.
Of course there is a whole lot more that could be said about the nature and character of this emergent digital transformation of our social world and about its impact on the social sciences (Anderson 2008; Jockers 2013; Kitchin 2014; Lazer et al. 2009; Lee and Martin 2015; Liu, 2013; Mayer-Schonberger and Cukier 2013; Moretti 2013; and the responses to Lee and Martin in the October 2015 issue of the American Journal of Cultural Sociology). And there are many critically important implications of these changes for sociology, for basic sociality (Turkle 2012), social class and inequality (DiMaggio et al. 2001.), civil liberties (Scheer 2015), and so much more. But these matters are not my focus here. My focus is on how Big Data is going to have an impact on the intellectual subfield of sociological theory over the next generation or so (and vice-versa). In the remainder of this short essay, I will describe three reasons why I think the shift toward Big Data will demand more and better theory.
Big Data/Big Theory
My thesis is simple. In the not too distant future, I think that Big Data is going to have a very Big Impact on sociology. And I think that the more that sociologists (as well as other social and cultural scientists) embrace the analysis of Big Data (which I think is, after all, inevitable), the more they are going to need to (and want to) call upon good sociological theory--lots of it—which is what I mean when I talk about an emerging era of Big Theory. In it simplest form, my argument is that those who analyze Big Data with a goal of studying the social or the cultural will be much advantaged by drawing on well informed sociological and other social-scientific and humanistic theories. The flip-side of that would be that sociological theory (et al.) will be much advantaged by becoming more engaged with efforts to think about and analyze Big Data. That duality of theory and practice is the subject and, I suppose, the thesis of this essay. But let me step back for a minute. Just why do I believe that the study of Big Data is going to require an era of Big Theory, and just what do I mean by that phrase?
I am sure there are lots of other reasons that we can find, but at the moment I want to consider three things about the move to Big Data that will require a greater and more ambitious effort at theorization. I will call these (1) the Paradigm Effect, (2) the Data Effect, and (3) the Culture Effect. I will explain each of these in turn. I should also note that many of the papers that I cite in the remainder of this essay have just been published as part of a special collection of 18 essays entitled, “Conceiving the Social with Big Data: A Colloquium of Social and Cultural Scientists,” in the online journal Big Data and Society. I co-edited the colloquium along with Robin Wagner-Pacifici and Ronald Breiger. All of these papers can be accessed via the journal’s website: http://bds.sagepub.com.
To be continued: See Part 2 in the Spring Edition (2016) of Perspectives.
References
Anderson C. 2008. “The End of Theory: The Data Deluge Makes the Scientific Method Obsolete.” Wired, 23 June.
DiMaggio, Paul, Eszter Hargittai, W. Russell Neuman, and John P. Robinson. 2001. “Social Implications of the Internet.” Annual Review of Sociology 27: 307-336.
Jockers, M.L., 2013. Macroanalysis: Digital Methods and Literary History. Urbana: University of Illinois Press.
Kitchin, Rob. 2014. “Big Data, New Epistemologies and Paradigm Shifts.” Big Data and Society. DOI: 10.1177/2053951714528481, June.
Lazega, Emmanuel, Marie-Thérèse Jourda, and LiseMounier. 2013. "Catching Up with Big Fish in the Big Pond? Multi-Level Network Analysis through Linked Design." Social Networks, 30 (2):159-176.
Lazer, D., Pentland, A., Adamic, L., Aral, S., Barabasi, A.-L., Brewer, D., Christakis, N., Contractor, N., Fowler, J., Gutmann, M., Jebara, T., King, G., Macy, M., Roy, D., Van Alstyne, M., 2009. “Computational Social Science.” Science (6 February), 721-723.
Lee, Monica, and John Levi Martin. 2015. “Coding, Counting and Cultural Cartography.” American Journal of Cultural Sociology 3(1): 1-33.
Liu, A. 2013. “The Meaning of the Digital Humanities.” PMLA 128: 409-23.
Mayer-Schonberger V and Cukier K. 2013. Big Data: A Revolution that Will Change How we Live, Work and Think. London: John Murray.
Moretti, Franco. 2013. Distant Reading. London: Verso.
Scheer, Robert. 2015. They Know Everything About You: How Data-Collecting Corporations and Snooping Government Agencies Are Destroying Democracy. New York: The Nation Books.
Turkle, Sherry. 2012. Alone together: Why we expect more from technology and less from each other. New York: Basic Books.
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