This is a totally half baked idea but it keeps popping up in my little head and maybe you all can help me deal. I’m basically thinking about the challenge of applying my humanities-trained mind to data driven projects.
The challenge isn’t that I am skeptical of quantitative stuff but that I know how easy it is to make mistakes with it if you don’t know what you’re doing or get easily confused by big numbers (I am guilty of both). I also know how easy it is to be dazzled by big numbers and beautiful visualizations (no offense Hans Rosling).
I started thinking about this while reading the first of the New York Times stories about digital humanities. Patricia Cohen wrote:
Members of a new generation of digitally savvy humanists argue it is time to stop looking for inspiration in the next political or philosophical “ism” and start exploring how technology is changing our understanding of the liberal arts. This latest frontier is about method, they say, using powerful technologies and vast stores of digitized materials that previous humanities scholars did not have.
The idea of a “data turn” in the humanities has been tossed around but it is not as if data are somehow a-political or outside of ideology, right? In this session I want to hear people’s ideas about strategies for approaching Big Data. On the one hand, the idea of being able to search for patterns across vast sets of historical and cultural data is very exciting. On the other hand, I can’t unread Foucault (or Marx for that matter). I know all of this data exists in a context but I don’t know how to keep that in perspective when I’m dealing with Big Data.
Anyway, this is just a thought on the table. I would love it if someone wants to pick it up and build an actually coherent session proposal out of it.
Stewart
3 comments
Amanda French
May 26, 2011 at 12:23 am (UTC -5) Link to this comment
I wrote a little bit about being uneasy with Big Data recently, Stewart, so I’m in. In in in. For a conversation if nothing else. I do think the concept of “zoom” is important: all Big Data analyses have to have the “equal eye” that sees a million heroes perish and a single sparrow fall. Suggested reading (though not with your interesting Foucauldian approach): Steve Ramsay’s “The Hermeneutics of Screwing Around.” www.playingwithhistory.com/wp-content/uploads/2010/04/hermeneutics.pdf
Sarah Werner
May 29, 2011 at 8:55 pm (UTC -5) Link to this comment
Hmm. I’m interested in this as well. I’m not sure what more coherent I can add, but Big Data does make me uneasy, both in terms of what it gets applied to and in terms of who applies it. Do the skills and the large amounts of data required need a level of infrastructure that precludes, say, scholars at less-funded institutions or in positions don’t provide institutional support? Does Big Data work as well for studies that pursue the more distant past? I worry, too, that a desire for measuring data–for examining things that can be thought of in terms of data–overshadows the hardwork that many have done to illuminate the understudied, such as minority communities, or the immaterial, or the non-textual.
Brian Croxall
May 31, 2011 at 4:59 pm (UTC -5) Link to this comment
In some ways, I wonder if the risk is that humanities people end up subsuming data to their particular “ism.” By this I mean that since humanists aren’t used to working with big data sets or to interpreting from them, people might be inclined to simply put all of the data stuff into a small chart that helps prove the point that they were already trying to make. Just as we can too easily abstract the data’s origins, so too can we shoe horn it into what we want it to be.
To build on what Sarah says, I do think that a risk of Big Data is that it does preclude people at less funded institutions. For example, the Digging Into Data program requires an international team across multiple universities. Is that something that is really accessible for people that are at non-flagship or R1 schools? And do we care? (Maybe this is getting us back to James Neal’s session idea about diversity.