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, the sociology of quantification is an expanding field which includes the literature on the quantified self, on algorithms, and on various forms of metrics and indicators.[2] A prior review in 2016 names a similar range of topics: "quantification processes in the sciences, quantification in society driven by the sciences, quantification processes driven by other social processes, including for example implementations of numeric technologies, standardization procedures, bureaucratic management, political decision-taking and newer trends as self-quantification."[3] Older works which can be classified under the heading of the sociology of quantification are Theodore Porter’s Trust in Numbers,[4] the works of French sociologists Pierre Bourdieu[5][6] and Alain Desrosières,[7] and the classic works on probability by Ian Hacking[8] and Lorraine Daston.[9] The discipline gained traction due to the increasing importance and scope of quantification,[2] its relation to the economics of conventions,[10] and the perception of its dangers as a weapon of oppression[11][12][13] or as means to undesirable ends.[12][14]

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.[15] 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.[16]

The French movement of ‘statactivisme’ suggests fighting numbers with numbers under the slogan “a new number is possible".[5] On the other extreme, algorithmic automation is seen as an instrument of liberation by Aaron Bastani,[17] spurring a debate on digital socialism.[18][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. 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".[20] 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,[21] 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[22] and artificial intelligence posing a threat to democracy and to democratic agency.[23][24]

Mathematical modelling is a field of interest for sociology of quantification,[25] 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'[26] 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.[27] Models as mediators between 'theories' and 'the world' are discussed in a multi-author book edited by Mary S. Morgan and Margaret Morrison[25] that offers several examples from physics and economics. The volume provides a historical and philosophical discussion of what models are and of what models do, with contributions from the authors as well as from scholars such as Ursula Klein, Marcel Boumans, R.I.G. Hughes, Mauricio Suárez, Geert Reuten, Nancy Cartwright, Adrienne van den Boogard, and Stephan Hartmann.[28] A later work by Morgan offers elements of history, sociology and epistemology of modelling in economics and econometrics.[29] Relevant material for a sociology of mathematical models can be found in the works of Ian Scoones and Andy Stirling,[30][31] in Mirowski’s Machine Dreams, in Evelyn Fox Keller Making Sense of Life, Jean Baudrillard's Simulacra and Simulation, in Bruno Latour and Steve Woolgar's Laboratory Life.

The role of quantification in historiography and macrohistory is the subject of The Measure of Reality: Quantification in Western Europe, 1250-1600, a 1997 nonfiction book by Alfred W. Crosby. The book examines the origins and effects of quantitative thinking in post-medieval European history, suggesting it as a major factor in the ensuing development of European arts and techniques.[32]

## Links

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* Algorithmic Justice League.
* Cardiff University: “Data Justice Lab”, School of Journalism, Media and Culture.
* French National Research Institute for Sustainable Development: “Project SSSQ - Society for the Social Studies of Quantification.

## References

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1. ^ a b Espeland, Wendy N.; Stevens, Mitchell L. (2008). "A Sociology of Quantification". European Journal of Sociology. 49 (3): 401–436\. JSTOR 23998802.
2. ^ a b Berman, Elizabeth Popp; Hirschman, Daniel (2018). "The Sociology of Quantification: Where Are We Now?". Contemporary Sociology. 47 (3): 257–266\. doi:10.1177/0094306118767649.
3. ^ Diaz-Bone, Rainer; Didier, Emmanuel (2016). "The Sociology of Quantification - Perspectives on an Emerging Field in the Social Sciences". Historical Social Research. 41 (2): 7–26\. JSTOR 43798480.
4. ^ Porter, Theodore (2020) [1995]. Trust in Numbers: The Pursuit of Objectivity in Science and Public Life (paperback ed.). Princeton University Press. ISBN 9780691208411.
5. ^ a b Bruno, Isabelle; Didier, Emmanuel; Prévieux, Julien (2014). Statactivisme: Comment Lutter avec des Nombres. Zones (in French). La Découverte. ISBN 9782355220548.
6. ^ Robson, Karen; Sanders, Chris, eds. (2009). Quantifying Theory: Pierre Bourdieu. Springer. ISBN 978-1-4020-9449-1.
7. ^ Desrosières, Alain (2002). The Politics of Large Numbers: A History of Statistical Reasoning. Translated by Naish, Camille. Harvard University Press. ISBN 9780674009691.
8. ^ Hacking, Ian (1990). The Taming of Chance. Cambridge University Press. ISBN 9780521388849.
9. ^ Daston, Lorraine (1995) [1988]. Classical Probability in the Enlightenment (paperback ed.). Princeton University Press. ISBN 9780691006444.
10. ^ Salais, Robert (2012). "Quantification and the Economics of Convention". Historical Social Research. 37 (4): 55–63\. JSTOR 41756473.
11. ^ Espeland, Wendy Nelson; Sauder, Michael (2016). Engines of Anxiety: Academic Rankings, Reputation, and Accountability. Russell Sage Foundation. JSTOR 10.7758/9781610448567.
12. ^ a b Muller, Jerry Z. (2018). The Tyranny of Metrics. Princeton University Press. ISBN 9780691174952.
13. ^ O'Neil, Cathy (2016). Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. Random House Publishing Group. ISBN 978-0-553-41881-1.
14. ^ Porter, Theodore M. (2013). "Funny Numbers". Culture Unbound. 4 (4): 585–598\. doi:10.3384/cu.2000.1525.124585.
15. ^ Merry, Sally Engle (2016). The Seductions of Quantification: Measuring Human Rights, Gender Violence, and Sex Trafficking. Chicago University Press. ISBN 9780226261287.
16. ^ Supiot, Alain (2017). Governance by Numbers: The Making of a Legal Model of Allegiance. Translated by Brown, Saskia. Bloomsbury Publishing. ISBN 9781509907748.
17. ^ Bastani, Aaron (2019). Fully Automated Luxury Capitalism: A Manifesto. Verso. ISBN 9781786632623.
18. ^ Mostafa, Joshua (July 23, 2019). "The Revolution Will Not Be Automated: Joshua Mostafa on Shoshana Zuboff". Sydney Review of Books. Retrieved December 9, 2024.
19. ^ Morozov, Evgeny (March–June 2019). "Digital Socialism? The Calculation Debate in the Age of Big Data". New Left Review (116/117): 33–67.
20. ^ Mennicken, Andrea; Espeland, Wendy Nelson (2019). "What's New with Numbers? Sociological Approaches to the Study of Quantification". Annual Review of Sociology. 45 (1): 223–245\. doi:10.1146/annurev-soc-073117-041343.
21. ^ Mennicken, Andrea; Salais, Robert, eds. (2022). The New Politics of Numbers: Utopia, Evidence and Democracy. Executive Politics and Governance. Palgrave Macmillan. doi:10.1007/978-3-030-78201-6. ISBN 978-3-030-78201-6.
22. ^ Supiot, Alain (March 18, 2015). La Gouvernance par les nombres: Cours au Collège de France 2012-2013 (in French). Fayard. ISBN 9782213681092.
23. ^ Salais, Robert (2022). "La Donnée n'est Pas Un Donné: Statistics, Quantification and Democratic Choice". In Mennicken, Andrea; Salais, Robert (eds.). The New Politics of Numbers: Utopia, Evidence and Democracy. Executive Politics and Governance. Palgrave Macmillan. pp. 379–415\. doi:10.1007/978-3-030-78201-6. ISBN 978-3-030-78201-6.
24. ^ McQuillan, Dan (2022). Resisting AI: An Anti-fascist Approach to Artificial Intelligence. Bristol University Press. ISBN 978-1529213508.
25. ^ a b Morgan, Mary S.; Morrison, Margaret, eds. (2010) [1999]. Models as Mediators: Perspectives on Natural and Social Science (ebook ed.). Cambridge University Press. doi:10.1017/CBO9780511660108. ISBN 9780511660108.
26. ^ Rhodes, Tim; Lancaster, Kari (May 13, 2020). "Mathematical models as public troubles in COVID-19 infection control: following the numbers". Health Sociology Review. 29 (2): 177–194\. doi:10.1080/14461242.2020.1764376. hdl:1959.4/unsworks_67427.
27. ^ Saltelli, Andrea; Bammer, Gabriele; Bruno, Isabelle; Charters, Erica; Di Fiore, Monica; Didier, Emmanuel; Espeland, Wendy Nelson; Kay, John; Piano, Samuele Lo; Mayo, Deborah; Pielke Jr., Roger; Portaluri, Tommaso; Porter, Theodore M.; Puy, Arnald; Rafols, Ismael; Ravetz, Jerome R.; Reinert, Erik; Sarewitz, Daniel; Stark, Philip B.; Stirling, Andrew; van der Sluijs, Jeroen; Vineis, Paolo (June 24, 2020). "Five ways to ensure that models serve society: a manifesto". Nature. 582: 482–484\. doi:10.1038/d41586-020-01812-9. hdl:1885/219031.
28. ^ Guala, Francesco; Psillos, Stathis (2001). "Models as Mediators. Perspectives on Natural and Social Science, Mary S. Morgan and Margaret Morrison (eds.). Cambridge University Press, 1999, xi + 401 pages". Economics and Philosophy. 17 (2): 275–294\. doi:10.1017/S0266267101230272.
29. ^ Morgan, Mary S. (September 17, 2012). The World in the Model: How Economists Work and Think. Cambridge University Press. doi:10.1017/CBO9781139026185. ISBN 978-1-107-00297-5.
30. ^ Scoones, Ian (2024). Navigating Uncertainty: Radical Rethinking for a Turbulent World. Polity Press. ISBN 978-1-5095-6008-0.
31. ^ Scoones, Ian; Stirling, Andy, eds. (2020). The Politics of Uncertainty: The Challenges of Transformation. Routledge. doi:10.4324/9781003023845. ISBN 978-1-00-302384-5.
32. ^ Crosby, Alfred W. (1996). The Measure of Reality: Quantification in Western Europe, 1250–1600. Cambridge University Press. doi:10.1017/CBO9781107050518. ISBN 978-0-521-55427-5.