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Sociology of quantification

The sociology of quantification is the investigation of quantification as a sociological phenomenon in its own right.[1]

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According to a review published in 2018[2] sociology of quantification is an expanding field which includes the literature on the quantified self, on algorithms,[3] and on various forms of metrics and indicators.[4][5] Older works which can be classified under the same heading are Theodore Porter’s Trust in Numbers,[6] the works of French sociologists Pierre Bourdieu[7][8] and Alain Desrosières,[9] and the classic works on probability by Ian Hacking[10] and Lorraine Daston.[11] The discipline gained traction due to the increasing importance and scope of quantification,[2] its relation to the economics of conventions,[12] and by the perception of its dangers as a weapon of oppression,[3][5] or as means to undesirable ends.[5][13]

For Sally Engle Merry quantification is a technology of control, but whether it is reformist or authoritarian depends on who harnessed it and for what purpose.[14] The ‘governance by numbers’ is seen by jurist Alain Supiot as repudiating the goal of governing by just laws, advocating in its stead the attainment of measurable objectives. For Supiot the normative use of economic quantification leaves no option for countries and economic actors than to ride roughshod over social legislation, and pledge allegiance to stronger powers.[15]

The French movement of ‘statactivisme’ suggests fighting numbers with numbers under the slogan “a new number is possible".[7] On the other extreme, algorithmic automation is seen as an instrument of liberation by Aaron Bastani,[16] spurring a debate on digital socialism.[17][18] According to Espeland and Stevens[1] an ethics of quantification would naturally descend from a sociology of quantification, especially at an age where democracy, merit, participation, accountability and even "fairness" are assumed to be best discovered and appreciated via numbers. Andrea Mennicken and Wendy Espeland provide a review (2019) of the main concerns about the "increasing expansion of quantification into all realms, including into people’s personal lives".[19] These authors discuss the new patterns of visibility and obscurity created by quantitative technologies, how these influence relations of power, and how neoliberal regimes of quantification favour 'economization', where "individuals, activities, and organizations are constituted or framed as economic actors and entities." Mennicken and Robert Salais have curated in 2022 a multi-author volume titled The New Politics of Numbers: Utopia, Evidence and Democracy,[20] with contributions encompassing Foucauldian studies of governmentality, which first flourished in the English-speaking world, and studies of state statistics known as ‘economics of convention’, developed mostly at INSEE in France. A theme treated by several authors is the relationship between quantification and democracy, with regimes of algorithmic governmentality[21] and artificial intelligence posing a threat to democracy and to democratic agency.[22][23]

Mathematical modelling is a field of interest for sociology of quantification,[24] and the intensified use of mathematical models in relation to the COVID-19 pandemic has spurred a debate on how society uses models. Rhodes and Lancaster speak of 'model as public troubles'[25] and starting from models as boundary objects call for a better relation between models and society. Other authors propose five principles for making models serve society, on the premise that modelling is a social activity.[26] Models as mediators between 'theories' and 'the world' are discussed in a multi-author book edited by Mary S. Morgan and Margaret Morrison[27] that offer several examples from physics and economics. An earlier work by Morgan offers elements of history and sociology of modelling in economics and econometrics.[28]

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  1. ^ a b W. N. Espeland and M. L. Stevens, “A sociology of quantification,” Eur. J. Sociol., vol. 49, no. 3, pp. 401–436, 2008.
  2. ^ a b E. Popp Berman and D. Hirschman, “The Sociology of Quantification: Where Are We Now?,” Contemp. Sociol., vol. 47, no. 3, pp. 257–266, 2018.
  3. ^ a b C. O’Neil, Weapons of math destruction : how big data increases inequality and threatens democracy. Random House Publishing Group, 2016.
  4. ^ W. N. Espeland and M. Sauder, Engines of anxiety : academic rankings, reputation, and accountability. Russell Sage Foundation, 2016.
  5. ^ a b c J. Z. Muller, The tyranny of metrics. Princeton University Press, 2018.
  6. ^ Theodore Porter, Trust in Numbers: The Pursuit of Objectivity in Science and Public Life. Princeton University Press, 1996.
  7. ^ a b I. Bruno, E. Didier, and J. Prévieux, Statactivisme. Comment lutter avec des nombres. Paris: Zones, La Découverte, 2014.
  8. ^ Robson, K., Sanders, C. (Eds.), 2009. Quantifying Theory: Pierre Bourdieu. Springer.
  9. ^ Desrosières, A., 1998. The Politics of Large Numbers: a history of statistical reasoning . Harvard University Press.
  10. ^ Hacking, I., 1990. The taming of chance. Cambridge University Press
  11. ^ Daston, L., 1995. Classical Probability in the Enlightenment. Princeton University Press.
  12. ^ Robert Salais, 2012. Quantification and the Economics of Convention. Hist. Soc. Res. 37, 55–63.
  13. ^ Theodore Porter, “Funny Numbers,” Cult. Unbound, vol. 4, pp. 585–598, 2012.
  14. ^ Sally Engle Merry, 2016, The Seductions of Quantification: Measuring Human Rights, Gender Violence, and Sex Trafficking, Chicago University press.
  15. ^ Alain Supiot, Governance by Numbers: The Making of a Legal Model of Allegiance. Oxford University Press, 2007.
  16. ^ A. Bastani, Fully Automated Luxury Capitalism: A Manifesto. New York: Verso, 2019.
  17. ^ J. Mostafa, “The Revolution Will Not Be Automated,” Sydney Review of Books, Jul-2019.
  18. ^ E. Morozov, “Digital Socialism? The Calculation Debate in the Age of Big Data,” new left Rev., no. 116/117, pp. 33–68, 2019.
  19. ^ Mennicken, Andrea, and Wendy Nelson Espeland. 2019. “What’s New with Numbers? Sociological Approaches to the Study of Quantification.” Annual Review of Sociology 45 (1): 223–45. https://doi.org/10.1146/annurev-soc-073117-041343.
  20. ^ Mennicken, Andrea, and Robert Salais. 2022. The New Politics of Numbers: Utopia, Evidence and Democracy. Palgrave Macmillan.
  21. ^ Supiot, Alain. 2015. La Gouvernance par les nombres. Paris: Fayard. https://www.fayard.fr/sciences-humaines/la-gouvernance-par-les-nombres-9782213681092.
  22. ^ Salais, Robert. 2022. “‘La Donnée n’est Pas Un Donné’: Statistics, Quantification and Democratic Choice.” In The New Politics of Numbers: Utopia, Evidence and Democracy, Andrea Mennicken and Rober Salais, 379–415. : : Utopia, Evidence and Democracy. Palgrave Macmillan.
  23. ^ McQuillan, Dan. 2022. Resisting AI: An Anti-fascist Approach to Artificial Intelligence. Bristol University Press. https://www.amazon.es/Resisting-AI-Anti-fascist-Artificial-Intelligence/dp/1529213495.
  24. ^ Morgan, Mary S., and Margaret Morrison, eds. 1999. Models as Mediators: Perspectives on Natural and Social Science. Cambridge ; New York: Cambridge University Press.
  25. ^ T. Rhodes and K. Lancaster, “Mathematical models as public troubles in COVID-19 infection control: following the numbers,” Heal. Sociol. Rev., pp. 1–18, May 2020.
  26. ^ A. Saltelli, G. Bammer, I. Bruno, E. Charters, M. Di Fiore, E. Didier, W. Nelson Espeland, J. Kay, S. Lo Piano, D. Mayo, R.J. Pielke, T. Portaluri, T.M. Porter, A. Puy, I. Rafols, J.R. Ravetz, E. Reinert, D. Sarewitz, P.B. Stark, A. Stirling, P. van der Sluijs, Jeroen P. Vineis, Five ways to ensure that models serve society: a manifesto, Nature 582 (2020) 482–484.
  27. ^ Morgan, M.S., Morrison, M. (Eds.), 1999. Models as Mediators: Perspectives on Natural and Social Science. Cambridge University Press, Cambridge ; New York.
  28. ^ Morgan, Mary S. 2012. The World in the Model: How Economists Work and Think. New edition. Cambridge ; New York: Cambridge University Press.
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