Algorithmic Psychometrics and the Scalable Subject
Stark, Luke (2018). "Algorithmic Psychometrics and the Scalable Subject," Social Studies of Science (48)2, 204-231
46 Pages Posted: 19 Apr 2018 Last revised: 10 May 2018
Date Written: April 2, 2018
Recent public controversies, ranging from the 2014 Facebook ‘emotional contagion’ study to psychographic data profiling by Cambridge Analytica in the 2016 American presidential election, Brexit referendum and elsewhere, signal watershed moments in which the intersecting trajectories of psychology and computer science have become matters of public concern. The entangled history of these two fields grounds the application of applied psychological techniques to digital technologies, and an investment in applying calculability to human subjectivity. Today, a quantifiable psychological subject position has been translated, via ‘big data’ sets and algorithmic analysis, into a model subject amenable to classification through digital media platforms. I term this position the ‘scalable subject’, arguing it has been shaped and made legible by algorithmic psychometrics – a broad set of affordances in digital platforms shaped by psychology and the behavioral sciences. In describing the contours of this ‘scalable subject’, this paper highlights the urgent need for renewed attention from STS scholars on the psy sciences, and on a computational politics attentive to psychology, emotional expression, and sociality via digital media.
Keywords: Facebook, social media, platforms, psychology, psychometrics, big data, affect, emotion, subjectivity, scale
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