Author(s):
- Dawn Nafus
Abstract:
Data aggregations are an under-acknowledged site of social relations. The social and technical specifics of how data aggregate are arenas for rich debates about how knowledge ought to be produced, and who should produce it. Consumer goods such as fitness trackers create conditions where data scientists or professional researchers are no longer the only ones making decisions about how to aggregate data. Users of these products also rework their data to discover something medically significant to them. These practices call attention to a modality of ‘scaling up’ datasets about a single person that is different from, and until recently largely invisible to, clinical approaches to big data, which privilege the creation of a ‘bird’s eye’ view across as many people. Both technical questions how to build these aggregations, and social questions of who should be involved, betray broader epistemological issues about how new knowledge is created from electronic devices.
Documentation:
https://doi.org/10.1017/S106279871900005X
References:
1 Sharon, T. (2016) Self-tracking for health and the quantified self: re-articulating autonomy, solidarity, and authenticity in an age of personalized healthcare. Philosophy & Technology, 30(1), pp. 1–29.Google Scholar
2 Boyd, D. and Crawford, K. (2012) Critical questions for big data: provocations for a cultural, technological and scholarly phenomenon. Information, Communication and Society, 15(5), pp. 662–679.CrossRefGoogle Scholar
3 Andrejevic, M. (2014) The big data divide. International Journal of Communication, 8, pp. 1673–1689.Google Scholar
4 Pasquale, F. (2015) The Black Box Society: The Secret Algorithms that Control Money and Information (Cambridge, MA: Harvard University Press).CrossRefGoogle Scholar
5 Ruppert, E. (2011) Population objects: interpassive subjects. Sociology, 45(2), pp. 218–233.CrossRefGoogle Scholar
6 Cheney-Lippold, J. (2011) A new algorithmic identity: soft biopolitics and the modulation of control. Theory, Culture & Society, 26(6), pp. 164–181.CrossRefGoogle Scholar
7 Barocas, S. and Selbst, A. (2016) Big data’s disparate impact. California Law Review, 104(3), pp. 671–732.Google Scholar
8 Van Dijck, J. (2014) Datafication, dataism and dataveillance: big data between scientific paradigm and ideology. Surveillance and Society, 12(2), pp. 197–208.CrossRefGoogle Scholar
9 Lupton, D. (2013) The digitally engaged patient: self-monitoring and self-care in the digital health era. Social Theory and Health, 11(3), pp. 256–270.CrossRefGoogle Scholar
10 Lupton, D. (2013) Quantifying the body: monitoring and measuring health in the age of mHealth technologies. Critical Public Health, 23(4), pp. 393–403.CrossRefGoogle Scholar
11 Rabinow, P. and Rose, N. (2006) Biopower today. BioSocieties, 1(2), pp. 195–217.CrossRefGoogle Scholar
12 Mort, M., Roberts, C. and Callén, B. (2013) Ageing with telecare: care or coercion in austerity? Sociology of Health & Illness, 35(6), pp. 799–812.CrossRefGoogle ScholarPubMed
13 Clarke, A.E. (2010) Biomedicalization (New York: Wiley).Google Scholar
14 McCarthy, M.T. (2016) The big data divide and its consequences. Sociology Compass, 10, pp. 1131–1140.CrossRefGoogle Scholar
15 Gerlitz, C. and Lury, C. (2014) Social media and self-evaluating assemblages: on numbers, orderings and values. Distinktion: Scandinavian Journal of Social Theory, 15(2), pp. 174–188.CrossRefGoogle Scholar
16 Kennedy, H. and Moss, G. (2015) Known or knowing publics? Social media data mining and the question of public agency. Big Data & Society, 2(2), pp. 1–11.CrossRefGoogle Scholar
17 Couldry, N. and Powell, A. (2014) Big data from the bottom up. Big Data and Society, 1(1), pp. 1–5.CrossRefGoogle Scholar
18 Ruckenstein, M. (2014) Visualized and interacted life: personal analytics and engagements with data doubles. Societies, 4(1), pp. 68–84.CrossRefGoogle Scholar
19 Pantzar, M. and Ruckenstein, M. (2015) The heart of everyday analytics: emotional, material and practical extensions in self-tracking market. Consumption Markets & Culture, 18(1), pp. 92–109.CrossRefGoogle Scholar
20 Schüll, N.D. (2016) Data for life: wearable technology and the design of self-care. BioSocieties, 11(3), pp. 317–333.CrossRefGoogle Scholar
21 Sharon, T. and Zandbergen, D. (2016) From data fetishism to quantifying selves: self-tracking practices and the other values of data. New Media & Society, 19(11), pp. 1–15.Google Scholar
22 Nafus, D. and Sherman, J. (2014) This one does not go up to 11: the quantified self movement as an alternative big data practice. International Journal of Communication, 8, pp. 1784–1794.Google Scholar
23 Greenfield, D. (2016) Deep data: notes on the N of 1. In: Nafus, D. (Ed.), Quantified: Biosensing Technologies in Everyday Life (Cambridge, MA: MIT Press), pp. 123–146.CrossRefGoogle Scholar
24 Taylor, A. (2016) 11 data, (bio)sensing and (other-)worldly stories from the cycle routes of London. In: Nafus, D. (Ed.), Quantified: Biosensing Technologies in Everyday Life (Cambridge, MA: MIT Press), pp. 189–210.CrossRefGoogle Scholar
25 Gitelman, L. (2013) Raw Data is an Oxymoron (Cambridge, MA: MIT Press).CrossRefGoogle Scholar
26 Lury, C. and Gross, A. (2014) The downs and ups of the consumer price index in Argentina: from national statistics to big data. PARTECIPAZIONE E CONFLITTO, 7(2), pp. 258–277.Google Scholar
27 Verran, H. (2010) Number as an inventive frontier in knowing and working Australia’s water resources. Anthropological Theory, 10(1-2), pp. 171–178.CrossRefGoogle Scholar
28 Ballestero, A. (2015) The ethics of a formula: calculating a financial–humanitarian price for water. American Ethnologist, 42(2), pp. 262–278.CrossRefGoogle Scholar
29 Guyer, J.I. (2014) Percentages and perchance: archaic forms in the twenty-first century. Distinktion: Scandinavian Journal of Social Theory, 15(2), pp. 155–173.CrossRefGoogle Scholar
30 Scott, J.C. (1998) Seeing like a State: How Certain Schemes to Improve the Human Condition have Failed (New haven, CT: Yale University Press).Google Scholar
31 Strathern, M. (2000) Audit Cultures: Anthropological Studies in Accountability, Ethics, and the Academy (London: Psychology Press).Google Scholar
32 Epstein, S. (1996) Impure Science: AIDS, Activism, and the Politics of Knowledge (Berkeley, CA: University of California Press).Google ScholarPubMed
33 Kitchin, R. (2014) Big Data, new epistemologies and paradigm shifts. Big Data & Society, 1(1), pp. 1–12.CrossRefGoogle Scholar
34 Timmermans, S. and Berg, M. (2010) The Gold Standard: The Challenge of Evidence-based Medicine and Standardization in Health Care (Philadelphia, PA: Temple University Press.)Google Scholar
35 Topol, E.J. (2013) The Creative Destruction of Medicine: How the Digital Revolution will Create Better Health Care (New York: Basic Books).Google Scholar
36 Dumit, J. (2006) Illnesses you have to fight to get: facts as forces in uncertain, emergent illnesses. Social Science & Medicine, 62(3), pp. 577–590.CrossRefGoogle ScholarPubMed
37 Bietz, M., Gregory, J., Calvert, C. and Rao, R. (2014) Personal data for the public good: new opportunities to enrich understanding of individual and population health. http://hdexplore.calit2.net/wp-content/uploads/2015/08/hdx_final_report_small.pdf (retrieved 27 January 2017).Google Scholar