Trust in Numbers

By T. Porter. 2020

642 citations recorded by the source corpus.

What accounts for the prestige of quantitative methods? The usual answer is that quantification is desirable in social investigation as a result of its successes in science. This book questions whether such success in the study of stars, molecules, or cells should be an attractive model for research on human societies, and examines why the natural sciences are highly quantitative in the first place. The book argues that a better understanding of the attractions of quantification in business, government, and social research brings a fresh perspective to its role in psychology, physics, and medicine. Quantitative rigor is not inherent in science but arises from political and social pressures, and objectivity derives its impetus from cultural contexts. A new preface sheds light on the current infatuation with quantitative methods, particularly at the intersection of science and bureaucracy.

Subfields: Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/6074b7b7db1408cc2cf733ded15c47fe2a7bcfd9

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In Management Science and Operations Research

Citations in the corpus

Thinking critically about and researching algorithms (2014, background): “…of mechanical objectivity – being distant, detached and impartial in how they work and thus acting independent of local customs, culture, knowledge and context (Porter, 1995) – in the process of translating a task or process or calculation into an algor-ithm they can never fully escape these.”
Classification situations: Life-chances in the neoliberal era (2013, neutral): “The ﬁrst distinguishes people who are ‘‘in’’ from those who are ‘‘out.’’”
Algorithms in practice: Comparing web journalism and criminal justice (2017, background): “Many expert fields that used to be protected from quantitative evaluation are now asked to comply with a growing number of metrics and standards (Espeland and Sauder, 2016; Porter, 1996; Strathern, 2000).”
Ethics of quantification: illumination, obfuscation and performative legitimation (2020, background): “Quanti ﬁ cation is performative; it serves a legitimating function (Porter, 1996) because it can in principle illuminate and make hitherto intangible things commensurable.”
Big Data in food and agriculture (2016, background): “Some possible research questions here are: Who has a role in deciding on the context for the data production, storage and use of particular data tools used in food and agriculture? Who decides which kinds of data are to be collected, given the functioning of current big digital collection and analyt”
Consumers and Artificial Intelligence: An Experiential Perspective (2020, positive): “…politics of algorithms (Seaver 2019) has also drawn on this popular imagination to theorize AI in the context of rationalization and quantification (Porter 1996), automated inequality (Dormehl 2014a), uneven information landscapes (Eubanks 2018), and the historical rise of “algorithms of…”