BREAKING NEWS: PEOPLE ARE DEBATING AUGMENTED REALITY/DIGITAL DUALISM!!!
This post, however, takes a break from The Great Dualism Debates of 2013 and reflects instead on some musings that have been whirring around in my brain since #TtW13 based on discussions surrounding the Quantified Self.
After returning from my favorite professional weekend of the year (AKA the Theorizing the Web annual conference), I sat enjoying a cup of coffee with a good friend. She asked about my presentation, and we got talking about Self Quantification and Identity. This particular friend is also an occasional running partner and a fellow nutrition enthusiast. We seamlessly moved into her personal tracking habits, and she shared with me that when she uses her calorie tracking app, she ends up omitting a good deal of information, and contextualizing other data. Specifically, she tells me that she “forgets” to track her food while spending weekends with her long-distance boyfriend (during which she tends to eat more), and made a point to write down that it was her birthday to explain why she was so high above her daily allotment one day last month. Interestingly, she does not have any followers on this app, which means her justifications and omissions are purely for own benefit. She is not keeping up appearances for others, but rather, maintaining meanings for herself.
My friend’s experiences resonated with a hovering notion that has lingered with me since the conference, a notion I want to further explore here. Specifically, it seems that self-quantification has a really important, prevalent, and somewhat ironic, qualitative component. This qualitative component is key in mediating between raw numbers and identity meanings. If self-quantifiers are seeking self-knowledge through numbers, then narratives and subjective interpretations are the mechanisms by which data morphs into selves. Self-quantifiers don’t just use data to learn about themselves, but rather, use data to construct the stories that they tell themselves about themselves.
My friend, for example, works to maintain a healthy body, and relatedly, maintains self-views as a healthy and health-conscious person. Caloric over-consumption threatens this view. The story that perceived caloric overconsumption tells her about herself is a troubling one. It disrupts her health-conscious narrative, which, because social actors work to maintain stable identity meanings, is social psychologically distressing. She knows, on an implicit level, that the data cannot speak for itself, and so she gives the data voice—her own voice—and guides the story in identity affirming ways. Of course, if the data strayed too far, if for instance, she went beyond her caloric allotment consistently, she would need to re-construe her story to make sense of the lapse and/or alter her identity meanings to create a new self-story altogether (e.g. from “I’m the kind of person who maintains a healthy body weight” to “I’m the kind of person who chooses not to prioritize body size”).
One example I gave during my presentation really speaks to the qualitative component of self-quantification. At a QS meetup, a woman named Nancy Daugherty talks about tracking her smiles via an EEG sensor with LED lights. She notices that she “lights up” when talking with co-workers in very instrumental ways (you can watch Daugherty’s full talk below). As a sociologist, my first inclination is to make sense of this in terms of gender training. Women are taught to smile during interaction, and so it is unsurprising that a woman would find herself with a smile upon her face without consciousness of it being there—and without a clear feeling of joy ostensibly signified by a smile. However, Daughtery interprets her smiles differently. She “realizes” that her interactions with co-workers are more meaningful than she has been giving them credit for, and reinterprets these relationships accordingly.
Which one of us is more “correct” is a moot point. Rather, the point of interest is that the same raw data can take on multiple meanings with quite different behavioral and perceptual outcomes. With my structurally based attribution, the data may reveal to a woman her own oppression and guide her away from compulsory accommodation and sweetness of demeanor. With Daugherty’s socio-emotional interpretation, the data reveal unrecognized warmth between this woman and her colleagues, and guides her to further appreciate these interactions on a personal level. Far from a precise tool with which the data prosumer reveals scientific Truth, the data act more like a word bank with which the data prosumer pieces together an artistic construction—a poem, a story, an arranged collage of the self.
This theme of self-qualification within self-quantification came up during the discussion portion of the Bodies & Bits panel of which I was a part (along with Christina Dunbar-Hester, Gina Neff & Brittany Fiore-Silfvast, and John Michalczyk). In the video linked here (~3:50:00), we see one audience member ask about the origins of more data as necessarily better. Whitney Erin Boesel (@phenatpyical), Gina Neff (@Ginasue), and I all trouble this question, noting the ways in which within self-quantification, usable data is far more important than large amounts of data.
I think Whitney says it best when she refers to a QS talk entitled “I Have all of This Data, Now What?” The Now What? requires subjective interpretation and qualitative story telling. The Quantified Self takes shape through qualification.
The qualification of the self is more than just a post-test tool. Self-qualification is present from the beginning, as decisions about what to measure and how to do so are highly subjective, and rest upon subject narratives. Tracking mood, for example, is rooted in a value for particular kinds of moods over others (typically, the preference for happiness over melancholy). Tracking physical activity is rooted in a value of a thin body over a large one. If the goal of a self-quantifier is to construct an improved future-self, one must determine who they want that self to be. What is the story that they hope to tell about themselves?
Self quantification is a process bookended by self qualification. Yes, the numbers are important. Self-quantification is, by definition, self-knowledge through numbers. Those numbers, however, take shape qualitatively. They become the code with which self-quantifiers prosume selves and identities into being. They are the bits with which self-quantifiers make sense of their atoms.
Headline Pic via: http://www.fuelyourwriting.com/files/1295468414_writing450.jpg
Jenny Davis is a weekly contributor on Cyborgology. Follow Jenny on Twitter @Jup83
Doug Hill — March 13, 2013
Thanks for this, an excellent rejoinder to the "objectivity" myth that Big Data encourages us to (yet again) buy into. I attended the Bits and Bodies panel at TTW13 and loved it; this is a helpful addition.
quiet riot girl — March 13, 2013
Interesting and clearly expressed piece thanks! It relates to longstanding debates about qualitative v quantitative research methodologies.
As I said on twitter I'd be interested to see some of the research on women smiling due to 'gender training'. I have found a few papers after a brief search. They are:
1.'The Power of a Smile to Move You: Complementary Submissiveness in Women’s Posture as a Function of Gender Salience and Facial Expression'
Soledad de Lemus,Russell Spears, Miguel Moya1
Personality and Social Psychology Bulletin November 2012 vol. 38 no. 11 1480-1494 http://psp.sagepub.com/content/38/11/1480
2.'The contingent smile: A meta-analysis of sex differences in smiling.'
LaFrance, Marianne, Hecht, Marvin A., Paluck, Elizabeth Levy,
Psychological Bulletin, Vol 129(2), Mar, 2003. pp. 305-334
3.'Smile Boycotts and Other Body Politics'
Feminism & Psychology 2002 12: 319
4.'Who may frown and who should smile? Dominance, affiliation, and the display of happiness and anger'
Ursula Hessa, Reginald Adams Jrb & Robert Kleckc
Cognition and Emotion Vol 19, issue 4 2005
I'm reserving judgement on the quality of this research/these articles until I have read them properly!
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