This article employs a cybernetic approach to explore the scope of what constitutes anthropological and ethnographic research and the potential to utilize data science techniques to broaden what constitutes ethnography. Four types of relationships anthropologists historically have tended to seek out with data science as a discipline: anthropology of data science, anthropology over data science, anthropology with data science and, the least developed of the four, anthropology by data science. I relate potential insights data scientists have cultivated on abductive, bottom-up (...) quantitative research that might be useful for anthropologists in particular and cybernetically minded thinkers in general. Grounded Nick Seaver’s concept of bastard disciplines and methodologies, an anthropology by data science relationship provides a beneficial way to ground such strategic incorporations within anthropological research and helpful food for thought for cybernetic scholars in other disciplinary contexts. (shrink)

Technology takes an unprecedented position in contemporary society. In particular, it has become part and parcel of governmental attempts to manufacture life in new ways. Such ideas concerning the governance of life organize around the same contention: that technology and life are, in fact, highly interconnectable. This is surprising because if one enters the sites of techno-scientific experimentation, those visions turn out to be much frailer and by no means “in place” yet. Rather, they afford or enforce constant interfacing work, (...) a particular mode of manufacturing life, rendering disparate, sturdy, and often surprisingly incompatible things available for one another. Here, we contend that both of those aspects, pervasive rationalities of interconnectability and practices of interfacing mark the cornerstones of what we call a new techno-bio-politics of life. In order to grasp the government of life under the technological condition, we must understand how both human and non-human entities are being rendered interconnectable and re-worked through practices of interfacing. We take neuro-technology and care robotics as two illustrative cases. Our analysis shows that the contemporary government of life is not primarily concerned with life itself in its biological re-constitution but rather with life as it is interfaced with and through technology. (shrink)

Is Big Data science a whole new way of doing research? And what difference does data quantity make to knowledge production strategies and their outputs? I argue that the novelty of Big Data science does not lie in the sheer quantity of data involved, but rather in the prominence and status acquired by data as commodity and recognised output, both within and outside of the scientific community and the methods, infrastructures, technologies, skills and knowledge developed to handle data. These developments (...) generate the impression that data-intensive research is a new mode of doing science, with its own epistemology and norms. To assess this claim, one needs to consider the ways in which data are actually disseminated and used to generate knowledge. Accordingly, this article reviews the development of sophisticated ways to disseminate, integrate and re-use data acquired on model organisms over the last three decades of work in experimental biology. I focus on online databases as prominent infrastructures set up to organise and interpret such data and examine the wealth and diversity of expertise, resources and conceptual scaffolding that such databases draw upon. This illuminates some of the conditions under which Big Data needs to be curated to support processes of discovery across biological subfields, which in turn highlights the difficulties caused by the lack of adequate curation for the vast majority of data in the life sciences. In closing, I reflect on the difference that data quantity is making to contemporary biology, the methodological and epistemic challenges of identifying and analysing data given these developments, and the opportunities and worries associated with Big Data discourse and methods. (shrink)

In science and technology studies today, there is a troubling tendency to portray actors in the biosciences as “cultural dopes” and technology as having monolithic qualities with predetermined outcomes. To remedy this analytical impasse, this article introduces the concept styles of valuation to analyze how actors struggle with valuing technology in practice. Empirically, this article examines how actors in a bioscientific laboratory struggle with valuing the properties and qualities of algorithms in a high-throughput setting and identifies the copresence of several (...) different styles. The question that the actors struggle with is what different configurations of algorithms, devices, and humans are “good bioscience,” that is, what do the actors perform as a good distribution of agency between algorithms and humans? A key finding is that algorithms, robots, and humans are valued in multiple ways in the same setting. For the actors, it is not apparent which configuration of agency and devices is more authoritative nor is it obvious which skills and functions should be redistributed to the algorithms. Thus, rather than tying algorithms to one set of values, such as “speed,” “precision,” or “automation,” this article demonstrates the broad utility of attending to the multivalence of algorithms and technology in practice. (shrink)

Over the past two decades urban social life has undergone a rapid and pervasive geocoding, becoming mediated, augmented and anticipated by location-sensitive technologies and services that generate and utilise big, personal, locative data. The production of these data has prompted the development of exploratory data-driven computing experiments that seek to find ways to extract value and insight from them. These projects often start from the data, rather than from a question or theory, and try to imagine and identify their potential (...) utility. In this paper, we explore the desires and mechanics of data-driven computing experiments. We demonstrate how both locative media data and computing experiments are ‘staged’ to create new values and computing techniques, which in turn are used to try and derive possible futures that are ridden with unintended consequences. We argue that using computing experiments to imagine potential urban futures produces effects that often have little to do with creating new urban practices. Instead, these experiments promote Big Data science and the prospect that data produced for one purpose can be recast for another and act as alternative mechanisms of envisioning urban futures. (shrink)