I am writing this post in response to Bethany Nowviskie’s excellent storify collection that asks some important questions about the gender imbalance visible at the Digging into Data conference. Be sure to click “load more” and read the comments as well.
As a first year assistant professor, I have to admit that I am more concerned with learning the ropes at my new university and keeping my head above the waters of teaching and service than with applying for a grant of this scale.
However, I do a bit of data analysis in my dissertation/book project so it isn’t inconceivable that some day I could find myself wanting to apply for this grant, or another like it. I admit to feeling intimidated by the thought due to A) a lack of statistics training and B) (possibly arising out of the former) a reluctance to commit to this type of engagement with data. I find myself less interested in “digging into” data than playing with it, poking at it, batting it around, deforming it (in the Jerome McGann / Lisa Samuels sense), and perhaps even breaking it. I suspect this is the response to framing and rhetoric noted by Nowviskie.
At MLA 2009, I heard a Digital Humanities talk in which one of the speakers encouraged the audience to move away from “doing science badly.” The phrase has really stuck with me, though likely not for the reason the speaker intended. I am fascinated with what we might learn from doing science badly. It’s not that I want to be irresponsible to the data. I don’t. But I take inspiration from this quote in Marshall McLuhan’s The Medium is the Massage,
Professionalism is environmental. Amateurism is anti-environmental. Professionalism merges the individual into patterns of total environment. Amateurism seeks the development of total awareness of the individual and the critical awareness of the groundrules of society. The amateur can afford to lose. (93)
So perhaps I should say that it’s less that I want to do science badly, than that I am reluctant to do it “professionally.” I like being a data amateur. Again, this may be related to a lack of statistical training. Though truth be told, this is not an insurmountable issue. I could easily pick up some books and even audit some classes at my university were I interested in overcoming this particular obstacle. So far, I’m not.
I am hesitant to label this an effect of gender, however. I do recall that as a child my well-meaning mother, perhaps in an effort to celebrate my natural affinity for words and reading, set up a dichotomy in which those who were good at words, were not good at numbers. Wanting to emulate my mother, who introduced me to the Bronte sisters when I was in fifth grade, I embraced my love of words. I took the minimal amount of math required and I suspect that I subconsciously wrote off any challenges with numbers as the unfortunate by-product of my linguistic prowess. It wasn’t until many years later, when I thrived at a job in which I was responsible for large financial forecasts and budgets, that I realized how deeply ingrained that dichotomy had been and as with many dichotomies, just how false it was. Again, this is not necessarily a gender issue. Though one might argue for it as the legacy of attitudes toward women in math and science from when my mother was in school in the 1960’s. However, I now recognize that I am not bad at numbers and I could develop my skills further in this area. But for whatever reason, I don’t.
Perhaps it is a matter of the right research question coming along. Perhaps someday the siren call of a large-scale data set will prompt me to step outside the realm of the amateur. Perhaps after I’ve found my footing as an assistant professor, I might have the intellectual resources to point in this direction. But for now, I find a certain liberty in the position of the amateur and am content to watch from the sidelines as the professionals do the digging.