As a follow up to our recent post highlighting Luke Stark’s Slate piece on “The Long History of Computer Science and Psychology Comes Into View,” we point you to Stark’s forthcoming piece in the April issue of Social Studies of Science. Now available online as a preprint, the article explores what Stark terms the “scalable subject” in relation to the history of the psy-disciplines and the ongoing big data controversies around Facebook and Cambridge Analytica. Full details below.
“Algorithmic Psychometrics and the Scalable Subject,” by Luke Stark. Abstract:
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.
The 2017 issue of Osiris is dedicated to Data Histories and includes a piece on big data in mid-twentieth century social science that may be of interest to AHP readers.
“Anthropology’s Most Documented Man, Ca. 1947: A Prefiguration of Big Data from the Big Social Science Era,” by Rebecca Lemov. Abstract:
“Big Data,” a descriptive term of relatively recent origin, has as one of its key effects the radically increased harnessing of ever-more-personal information accrued in the course of pedestrian life. This essay takes a historical view of the amassing and sharing of personal data, examining the genealogy of the “personal” and psychological elements inherent in Big Data through the case of an American Indian man who (the reigning experts claimed) gained the status of the most documented single individual in the history of modern anthropology. Although raised a traditional Hopi Indian in Oraibi, Arizona, Don Talayesva (1890–1985) gave over his life materials to scientists at prominent universities and constituted in and of himself a “vast data set” long before such practices were common. This essay uses this pioneering data set (partially preserved in the Human Relations Area Files and its web-based full-text database, eHRAF) to examine the distinctiveness of Big Data in relation to the personal, psychological realm; finally, a comparison is made with twenty-first-century data-collection practices of quantifying the self.
This article presents a historical analysis of the origins, rise, and demise of theories of stratification (Schichtentheorien). Following their roots in the ancient metaphysical idea of the “great chain of being,” Aristotle’s scala naturae, the medieval “Jacob’s ladder,” and Leibniz’s concept of the lex continua, I argue that theories of stratification represent the modern heir to the ancient cosmological idea of a harmonious, hierarchical, and unified universe. Theories of stratification reached their heyday during the interwar period within German academia, proliferating over a vast number of disciplines and rising to special prominence within personality psychology, feeding the hope for a unitary image of the world and of human beings, their biological and mental development, their social organization and cultural creations. This article focuses on the role of visuality as a distinct mode of scientific knowledge within theories of stratification as well as the cultural context that provided the fertile ground for their flowering in the Weimar Republic. Finally, the rapid demise of theories of stratification during the 1950s is discussed, and some reasons for their downfall during the second half of the 20th century are explored.
“Scientometric trend analyses of publications on the history of psychology: Is psychology becoming an unhistorical science?,” by Günter Krampen. The abstract reads,
Examines scientometrically the trends in and the recent situation of research on and the teaching of the history of psychology in the German-speaking countries and compares the findings with the situation in other countries (mainly the United States) by means of the psychology databases PSYNDEX and PsycINFO. Declines of publications on the history of psychology are described scientometrically for both research communities since the 1990s. Some impulses are suggested for the future of research on and the teaching of the history of psychology. These include (1) the necessity and significance of an intensified use of quantitative, unobtrusive scientometric methods in historiography in times of digital “big data”, (2) the necessity and possibilities to integrate qualitative and quantitative methodologies in historical research and teaching, (3) the reasonableness of interdisciplinary cooperation of specialist historians, scientometricians, and psychologists, (4) the meaningfulness and necessity to explore, investigate, and teach more intensively the past and the problem history of psychology as well as the understanding of the subject matter of psychology in its historical development in cultural contexts. The outlook on the future of such a more up-to-date research on and teaching of the history of psychology is—with some caution—positive.
In her book, Borgman locates data as only meaningful within infrastructures or ecologies of knowledge, and discusses the management and exploitation of data as particular kinds of investments in the future of scholarship. Her take on the history of big data and the growing enthusiasm for data sharing, which she asserts often obscures the challenges and complexities of data stewardship, is relevant to historians of the social sciences. An excerpt:
Data practices are local, varying from field to field, individual to individual, and country to country. Studying data is a means to observe how rapidly the landscape of scholarly work in the sciences, social sciences, and the humanities is changing. Inside the black box of data is a plethora of research, technology, and policy issues. Data are best understood as representations of observations, objects, or other entities used as evidence of phenomena for the purposes of research or scholarship. Rarely do they stand alone, separable from software, protocols, lab and field conditions, and other context. The lack of agreement on what constitutes data underlies the difficulties in sharing, releasing, or reusing research data. Continue reading Issues in Open Scholarship: ‘If Data Sharing is the Answer, What is the Question?’→