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Articles

Engendering assemblages: the constitution of digital health data as an epistemic consumption object

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Pages 599-616 | Received 14 Oct 2020, Accepted 25 Apr 2022, Published online: 19 May 2022

References

  • Airoldi, M., 2022. Machine habitus: towards a sociology of algorithms. Cambridge: Polity.
  • Andrejevic, M., 2012. Exploitation in the data mine. In: C. Fuchs et al., eds. Internet and surveillance: the challenges of web 2.0 and social media. London: Routledge, 71–88.
  • Bachle, T. and Wernick, A., eds., 2019. The futures of health: social, ethical and legal challenges. Berlin: Alexander Von Humboldt Institute for Internet and Society.
  • Balicer, R. and Afek, A., 2017. Digital health nation: Israel’s global big data innovation hub. The Lancet, 389 (10088), 2451–2453.
  • Bates, J., Lin, Y.-W., and Goodale, P., 2016. Data journeys: capturing the socio-material constitution of data objects and flows. Big Data & Society, 3 (2), 1–12.
  • Beer, D., 2018. The data gaze: capitalism, power and perception. London: Sage.
  • Berry, D. and Fagerjord, A., 2017. Digital humanities: knowledge and critique in a digital age. Cambridge: Polity.
  • Boyd, D. and Crawford, K, 2012. Critical questions for big data. Information, Communication and Society, 15 (5), 662–679.
  • Callon, M., 1984. Some elements of a sociology of translation: domestication of the scallops and the fishermen of St. Brieuc Bay. The Sociological Review, 32 (1_suppl), 196–233.
  • Callon, M., 1986. The sociology of an actor-network: the case of the electric vehicle. In: M. Callon, J. Law, and A. Rip, eds. Mapping the dynamics of science and technology: sociology of science in the real world. New York: Macmillan Press, 19–34.
  • Callon, M., 2016. Revisiting marketization: from interface markets to market-agencements. Consumption Markets & Culture, 19 (1), 17–37.
  • Deleuze, G. and Parnet, C., 2002. Dialogues II. New York: Columbia University Press.
  • Ezrachi, A. and Stucke, M., 2016. Virtual competition: the promise and perils of the algorithm-driven economy. Cambridge: Harvard University Press.
  • Fisher, E., 2022. Algorithms and subjectivity: the subversion of critical knowledge. London: Routledge.
  • Fisher, E. and Mehozay, Y., 2019. How algorithms see their audience: media epistems and the changing conception of the individual. Media, Culture & Society, 41 (8), 1176–1191.
  • Flichy, P., 2007. The internet imaginaire. Cambridge: MIT Press.
  • Getzoff, J., 2020. Start-up nationalism: the rationalities of neoliberal Zionism. Environment and Planning D: Society and Space. doi:10.1177/0263775820911949.
  • Gitelman, L., ed., 2013. ‘Raw data’ is an oxymoron. Cambridge: MIT Press.
  • Government of Israel, 2018a. The national plan for digital health as a growth engine (in Hebrew).
  • Government of Israel, 2018b. Government resolution no. 3709, March 25, 2018: a national plan for the advancement of digital health as a means to improve health and as a growth engine (in Hebrew).
  • Hannah-Moffat, K., 2019. Algorithmic risk governance: big data analytics, race and information activism in criminal justice debates. Theoretical Criminology, 23 (4), 453–470.
  • Hennedy, H. and Moss, G., 2015. Known or knowing publics? Social media data mining and the question of public agency. Big Data & Society, 2 (2), 1–11.
  • Hoeyer, K., 2019. Data as promise: reconfiguring Danish public health through personalized medicine. Social Studies of Science, 49 (4), 531–555.
  • Hoeyer, K., Bauer, S., and Pickersgill, M., 2019. Datafication and accountability in public health: introduction to a special issue. Social Studies of Science, 49 (4), 459–475.
  • Hogle, L., 2016. Data-intensive resourcing in healthcare. BioSocieties, 11 (3), 372–393.
  • Jasanoff, S. and Kim, S.-H., 2009. Containing the atom: sociotechnical imaginaries and nuclear power in the United States and South Korea. Minerva, 47 (2), 119–146.
  • Karakayali, N., Kostem, B., and Galip, I., 2018. Recommendation systems as technologies of the self: algorithmic control and the formation of music taste. Theory, Culture & Society, 35 (2), 3–24.
  • Kehl, D., Guo, P., and Kessler, S., 2017. Algorithms in the criminal justice system: assessing the use of risk assessments in sentencing. Responsive Communities Initiative, Berkman Klein Center for Internet & Society, Harvard Law School. Available at: http://nrs.harvard.edu/urn-3:HUL.InstRepos:33746041 [Accessed 7 May 2020].
  • Kim, G.-H., Trimi, S., and Chung, J.-H., 2014. Big-data applications in the government sector. Communications of the ACM, 57 (3), 78–85.
  • Kitchin, R. and Lauriault, T., 2014. Towards critical data studies: charting and unpacking data assemblages and their work. The Programmable City Working Paper 2.
  • Knorr Cetina, K., 2010. The epistemics of information: a consumption model. Journal of Consumer Culture, 10 (2), 171–201.
  • Latour, B., 2005. Reassembling the social: an introduction to actor-network theory. Oxford: Oxford University Press.
  • Lupton, D., 2014a. Digital sociology. London: Routledge.
  • Lupton, D., 2014b. Critical perspectives on digital health technologies. Sociology Compass, 8 (12), 1344–1359.
  • Lupton, D., 2016. The quantified self. Cambridge: Polity.
  • Lupton, D., 2018. Digital health: critical and cross-disciplinary perspectives. New York: Routledge.
  • Mackenzie, A., 2017. Machine learners: archaeology of a data practice. Cambridge, MA: MIT Press.
  • Maggor, E., 2020. The politics of innovation policy: building Israel’s ‘neo-developmental’ state. Politics & Society. doi:10.1177/0032329220945527.
  • Marcus, G. and Saka, E., 2016. Assemblage. Theory, Culture & Society, 23 (2-3), 101–106.
  • Marres, N., 2017. Digital sociology: the reinvention of social research. Cambridge: Polity.
  • Mayer-Schönber, V. and Cukier, K., 2013. Big data: a revolution that will transform how we live, work, and think. Boston, MA: Houghton Mifflin Harcourt.
  • Mazzucato, M., 2013. The entrepreneurial state: debunking public vs. private sector myths. London: Anthem Press.
  • Mehozay, Y. and Fisher, E., 2018. The epistemology of algorithmic risk assessment and the path towards a non-penology penology. Punishment & Society, 21 (5), 523–541.
  • Mittelstadt, B. and Floridi, L., eds., 2016. The ethics of biomedical big data. Basel: Springer.
  • Mosco, V., 2004. The digital sublime: myth, power, and cyberspace. Cambridge: MIT Press.
  • Nye, D., 1994. American technological sublime. Cambridge: MIT Press.
  • O’Riain, S., 2004. The politics of high tech growth: developmental network states in the global economy. Cambridge: Cambridge University Press.
  • Pink, S., et al., 2018. Broken data: conceptualising data in an emerging world. Big Data & Society, 5 (1), 1–13.
  • Roski, J., Bo-Linn, G., and Andrews, T., 2014. Creating value in health care through big data: opportunities and policy implications. Health Affairs, 33 (7), 1115–1122.
  • Segal, H., 1985. Technological utopianism in American culture. Chicago: University of Chicago Press.
  • Sharon, T., 2018. When digital health meets digital capitalism, how many common goods are at stake? Big Data & Society, 5 (2), 1–12.
  • Star, S. L. and Griesemer, J. R., 1989. Institutional ecology, ‘translations’ and boundary objects: amateurs and professionals in Berkeley’s museum of vertebrate zoology, 1907–39. Social Studies of Science, 19 (3), 387–420.
  • The Economist, 2017. The world’s most valuable resource (cover), May 6.
  • Tupasela, A., Snell, K., and Tarkkala, H, 2020. The Nordic data imaginary. Big Data & Society. doi:10.1177/2053951720907107.
  • Turow, J., 2011. The daily you: how the new advertising industry is defining your identity and your worth. New Haven, CT: Yale University Press.
  • Turow, J. and Draper, N., 2014. Industry conceptions of audience in the digital space. Cultural Studies, 28 (4), 643–656.
  • van Dijck, J., 2014. Datafication, dataism and dataveillance: big data between scientific paradigm and ideology. Surveillance and Society, 12 (2), 197–208.
  • Vezyridis, P. and Timmons, S., 2017. Understanding the care.data conundrum: new information flows for economic growth. Big Data & Society, 4 (1), 1–12.
  • Zuboff, S., 2020. The age of surveillance capitalism: the fight for a human future at the new frontier of power. New York: Public Affairs.
  • Zwick, D. and Dholakia, N., 2006. The epistemic consumption object and postsocial consumption: expanding consumer-object theory in consumer research. Consumption Markets & Culture, 9 (1), 17–43.

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