Hostname: page-component-586b7cd67f-g8jcs Total loading time: 0 Render date: 2024-12-03T19:17:45.107Z Has data issue: false hasContentIssue false

Data Aggregation as Social Relations: Making Datasets from Self-Tracking Data

Published online by Cambridge University Press:  12 July 2019

Dawn Nafus*
Affiliation:
Intel Labs, 2111 NE 25th Ave, Hillsboro, OR 97124, USA. Email: [email protected]

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.

Type
Articles
Copyright
© Academia Europaea 2019 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

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. 129.Google Scholar
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. 662679.CrossRefGoogle Scholar
Andrejevic, M. (2014) The big data divide. International Journal of Communication, 8, pp. 16731689.Google Scholar
Pasquale, F. (2015) The Black Box Society: The Secret Algorithms that Control Money and Information (Cambridge, MA: Harvard University Press).CrossRefGoogle Scholar
Ruppert, E. (2011) Population objects: interpassive subjects. Sociology, 45(2), pp. 218233.CrossRefGoogle Scholar
Cheney-Lippold, J. (2011) A new algorithmic identity: soft biopolitics and the modulation of control. Theory, Culture & Society, 26(6), pp. 164181.CrossRefGoogle Scholar
Barocas, S. and Selbst, A. (2016) Big data’s disparate impact. California Law Review, 104(3), pp. 671732.Google Scholar
Van Dijck, J. (2014) Datafication, dataism and dataveillance: big data between scientific paradigm and ideology. Surveillance and Society, 12(2), pp. 197208.CrossRefGoogle Scholar
Lupton, D. (2013) The digitally engaged patient: self-monitoring and self-care in the digital health era. Social Theory and Health, 11(3), pp. 256270.CrossRefGoogle Scholar
Lupton, D. (2013) Quantifying the body: monitoring and measuring health in the age of mHealth technologies. Critical Public Health, 23(4), pp. 393403.CrossRefGoogle Scholar
Rabinow, P. and Rose, N. (2006) Biopower today. BioSocieties, 1(2), pp. 195217.CrossRefGoogle Scholar
Mort, M., Roberts, C. and Callén, B. (2013) Ageing with telecare: care or coercion in austerity? Sociology of Health & Illness, 35(6), pp. 799812.CrossRefGoogle ScholarPubMed
Clarke, A.E. (2010) Biomedicalization (New York: Wiley).Google Scholar
McCarthy, M.T. (2016) The big data divide and its consequences. Sociology Compass, 10, pp. 11311140.CrossRefGoogle Scholar
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. 174188.CrossRefGoogle Scholar
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. 111.CrossRefGoogle Scholar
Couldry, N. and Powell, A. (2014) Big data from the bottom up. Big Data and Society, 1(1), pp. 15.CrossRefGoogle Scholar
Ruckenstein, M. (2014) Visualized and interacted life: personal analytics and engagements with data doubles. Societies, 4(1), pp. 6884.CrossRefGoogle Scholar
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. 92109.CrossRefGoogle Scholar
Schüll, N.D. (2016) Data for life: wearable technology and the design of self-care. BioSocieties, 11(3), pp. 317333.CrossRefGoogle Scholar
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. 115.Google Scholar
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. 17841794.Google Scholar
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. 123146.CrossRefGoogle Scholar
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. 189210.CrossRefGoogle Scholar
Gitelman, L. (2013) Raw Data is an Oxymoron (Cambridge, MA: MIT Press).CrossRefGoogle Scholar
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. 258277.Google Scholar
Verran, H. (2010) Number as an inventive frontier in knowing and working Australia’s water resources. Anthropological Theory, 10(1-2), pp. 171178.CrossRefGoogle Scholar
Ballestero, A. (2015) The ethics of a formula: calculating a financial–humanitarian price for water. American Ethnologist, 42(2), pp. 262278.CrossRefGoogle Scholar
Guyer, J.I. (2014) Percentages and perchance: archaic forms in the twenty-first century. Distinktion: Scandinavian Journal of Social Theory, 15(2), pp. 155173.CrossRefGoogle Scholar
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
Strathern, M. (2000) Audit Cultures: Anthropological Studies in Accountability, Ethics, and the Academy (London: Psychology Press).Google Scholar
Epstein, S. (1996) Impure Science: AIDS, Activism, and the Politics of Knowledge (Berkeley, CA: University of California Press).Google ScholarPubMed
Kitchin, R. (2014) Big Data, new epistemologies and paradigm shifts. Big Data & Society, 1(1), pp. 112.CrossRefGoogle Scholar
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
Topol, E.J. (2013) The Creative Destruction of Medicine: How the Digital Revolution will Create Better Health Care (New York: Basic Books).Google Scholar
Dumit, J. (2006) Illnesses you have to fight to get: facts as forces in uncertain, emergent illnesses. Social Science & Medicine, 62(3), pp. 577590.CrossRefGoogle ScholarPubMed
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