Critical data studies
Laura Noren (New York University)
Stuart Geiger (UC-Berkeley)
Gretchen Gano (University of California Berkeley)
Massimo Mazzotti (University of California, Berkeley)
Charlotte Mazel-Cabasse (University of California, Berkeley)
Brittany Fiore-Gartland (University of Washington)
Start time:
3 September, 2016 at 9:00
Session slots:

Short abstract:

We invite papers investigating data­driven techniques in academic research and analytic industries and the consequences of implementing data­driven products and processes. Papers utilizing computational methods or ethnography with theorization of technology, social power, or politics are encouraged.

Long abstract:

Computational methods with large datasets are becoming more common across disciplines in academia (including social sciences) and analytic industries, but the sprawling and ambiguous boundaries of "big data" makes it difficult to research. In this track we investigate the relationship between theories, instruments, methods and practices in data science research and implementation. How are such practices transforming the processes of knowledge creation and validation, as well as our understanding of empiricism and the scientific method? Beyond case studies, we invite connective explorations of emerging theory, machinery, methods, and practices. Papers may examine data collection instruments, software, inscription devices, packages, algorithms and their interaction in sociotechnical systems used to produce, analyse, share, and validate knowledge. Looking at the way these knowledges are objectified, classified, imagined and contested, the aim is to reflect critically on the maturing practices of quantification and their historical, social, cultural, political, ideological, economical, scientific and ecological impacts.   We welcome papers tackling a variety of questions and cases studies such as: - What does it mean to study quantification (including big data) as myth, narrative, ideology, discourse, and power? - How is instrumentation is being used to connect  data and theory? - How well do we understand which domains are being reshaped by these techniques, and what are the consequences of their adoption in those domains and beyond? Is data science linking up to domains that have previously been distinct or dividing fields that had been unified? SESSIONS: 5/5/4/4