Science, Technology & Society Studies in Action

Science, Technology & Society Studies in Action

STSS 591, Prof. David Ribes, Autumn 2019 – See Syllabus

This course introduced graduate students from diverse disciplinary backgrounds (Social science, humanities, and STEM Programs) to Science, Technology, and Society Studies as an interdisciplinary area of study. Every week, we read sections of books written by University of Washington faculty members. Some of the sessions even featured guest speakers who were the authors of the books. Even though most of the reviewed books deal with science/technology as a social practice, there is also a variety of rhetorical devices that we were asked to note and analyze. Likewise, in each session, we identified the disciplinary research methodologies and STS concepts being proposed by the authors.

As a result of these readings and class discussions, I was introduced to concepts such as classifications, standards, and categories; information infrastructures; paradigms; frames; rhetoric and means of persuasion; objectivity; and domains.

Final product

Reading Steven Epstein’s book Inclusion. The Politics of Difference in Medical Research drove me to think about “the manifold ways in which science and government come together, conceptually and practically, in the definition and management of bodies, groups, and populations in different societies at different historical movements” (Epstein, 2007, p. 285). Taking into account my own interests in the use of digital data by public entities, this reading led me to grapple with the following questions: Why is it that Big Data and algorithms have become such an accepted and even desirable way of decision-making in public administration? What drew this change? Who were the reformers? How did reformers frame their arguments?

Looking for historical insights to understand those questions, in my “Deep Dive” Final Essay I looked to study the long-lasting relationship between numbers and modern statecraft. The intertwining of technoscientific development and the public sphere can be traced back to the modern State formation. Therefore, in order to comprehend the contemporary rise of Big Data and algorithms as preferred means for public administration, in my essay I developed a literature review that put into conversation the main arguments posed in the following five classical texts:

Carroll, P. (2006). Science, Culture, and Modern State Formation Berkeley: University of California Press.
Jasanoff, S. (1998). Fifth Branch. Science Advisers as Policymakers (oston: Harvard University Press.
Scott, J.C. (1998). Seeing like a State: How Certain Schemes to Improve the Human Condition Have Failed. New Haven: Yale University Press.
Desrosières, A. (1998). The Politics of Large Numbers: A History of Statistical Reasoning. Cambridge, MA: Harvard University Press.
Porter, T.M. (1995). Trust in Numbers: The Pursuit of Objectivity in Science and Public Life. Princeton, NJ: Princeton University Press.

After reviewing and analyzing those seminal pieces, I reached the following conclusion:

“[D]espite the fact that all five authors address different objects of study and their development over different—although overlapping—periods of time, all of them tend to reach to a very similar conclusion: the introduction of big numbers and numbers-based technological developments into the state was done to enhance the state’s capacity to govern. How? By either recovering the citizen’s trust through objectivity (or legitimation), providing a basis or justification for its actions, or enabling the state to know, transform, and intervene reality.”

This essay allowed me to construct a historical framework of the use of big numbers in public administration, providing me with a better understanding of the current popularity of data-driven public decision-making. As I pointed out in the Conclusions section of the essay,

“On the one hand, it seems clearer now that the current data-centric moment of history is more apparent than real; or at least, in what has to do with the state. In a sense, since the mid-17^th century numbers have always been at the center of governments.

On the other hand, after reviewing these classical authors it begins to seem probable that the real disruptiveness of Big Data (as numbers) and algorithms (as numbers-based technological developments) is not in the numbers themselves, but rather in the automated capabilities of the digital age. As documented by Carroll, since the 17th century William Petty had been thinking about reducing “all things under the sun” (Victor Mayer-Schönberger & Kenneth Cukier, 2013, p. 15) to terms of number and data (Carroll, 2006, p. 87). However, only in the digital era did that data become collected and processed in an automated way. And even further, only in the digital age have statistical techniques, mainly controlled by professionals, been replaced by machine learning, where a great part of the process seems to happen in a “black box” (Pasquale, 2015, p. 3).”

You can find here some slides that summarize my essay’s findings.

References

Mayer-Schönberger, V. & Cukier, K. (2013). Big Data. A revolution that will transform how we live, work and think. Nueva York: Mariner Books.
Pasquale, F. (2015). The Blackbox Society. Cambridge: Harvard University Press.

Selected Readings

Epstein, S. (2007). The Politics of Difference in Medical Research. Chicago: The University of Chicago Press.
Oreskes, N. & Conway, E.M. (2010). Merchants of Doubt. New York: Bloomsbury Press.
Kuhn, T.S. (1996). The Structure of Scientific Revolutions. London: University of Chicago Press.
Ribes, D. (2019). How I Learned What a Domain Was. Proceedings of the ACM on Human-Computer Interaction, 3(CSCW), Article No. 38, 1–12.