Sociology of quantification

Sociology of quantification can be defined as the investigation of quantification as a sociological phenomenon in its own right.[1]

Content

According to a review published in 2018[2] Sociology of quantification is an expanding fields which includes the literature on the quantified self, that 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 of Ian Hacking[10] and Lorraine Daston.[11] The interest in this field is driven by the increasing importance and scope of quantification,[2] its relation to the economics of conventions, [12] and by the perception of its dangers as weapons 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 has harnessed its power and for what purpose.[14] The ‘governance by numbers’ is seen by jurist Alain Supiot[15] 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 open to 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] To the opposite extreme, algorithmic-based automation is seen as an instrument of liberation by Aaron Bastani,[16] spurring a debate on 'digital socialism'.[17][18] An ethics of quantification including algorithms, metrics, statistical and mathematical modelling is suggested in.[19] 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.

Mathematical modelling can also be seen as a field of interest for sociology of quantification, and the recent intensified use of mathematical modelling 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'[20] and starting from models as boundary objects suggest that a better relation between models and society is needed. The authors in [21] propose five principles for making models serve society, by moving from the premise that modelling is a social activity.

References
  1. W. N. Espeland and M. L. Stevens, “A sociology of quantification,” Eur. J. Sociol., vol. 49, no. 3, pp. 401–436, 2008.
  2. 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. 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. J. Z. Muller, The tyranny of metrics. Princeton University Press , 2018.
  6. T. M. Porter, Trust in Numbers: The Pursuit of Objectivity in Science and Public Life. Princeton University Press, 1996.
  7. 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. T. M. 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. A. 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. A. Saltelli, “Ethics of quantification or quantification of ethics?,” Futures, vol. 116, 2020.
  20. 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.
  21. 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.
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