Emma Uprichard
I started at the Centre of Interdisciplinary Methodologies, University of Warwick in August 2012. Previously, I held posts at Goldsmiths, University of London (2011-2012), University of York (2007-2010), and Durham University (2004-2006).
I have a long-standing interest in the methodological challenge of applying complexity theory in the social sciences. I am especially concerned with issues of *time and temporality* and the ways in which different scales of time impact on change and continuity in the world.
Address: E.Uprichard@warwick.ac.uk
I have a long-standing interest in the methodological challenge of applying complexity theory in the social sciences. I am especially concerned with issues of *time and temporality* and the ways in which different scales of time impact on change and continuity in the world.
Address: E.Uprichard@warwick.ac.uk
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Papers by Emma Uprichard
Discover Society. Focus: Volume 1, Issue 1.
Discover Society. Focus: Volume 1, Issue 1.
Imagine sailing across the ocean. The sun is shining, vastness all around you. And suddenly [BOOM] you’ve hit an invisible wall. Welcome to the Truman Show! Ever since Eli Pariser published his thoughts on a potential filter bubble,1 this movie scenario seems to have become reality, just with slight changes: it’s not the ocean, it’s the internet we’re talking about, and it’s not a TV show producer, but algorithms that constitute a sort of invisible wall.2 Building on this assumption, most research is trying to ‘tame the algorithmic tiger’.3 While this is a valuable and often inspiring approach, we would like to emphasize another side to the algorithmic everyday life. We argue that algorithms can instigate and facilitate imagination, creativity, and frivolity, while saying something
that is simultaneously old and new, always almost repeating what was before but never quite returning. We show this by threading together stimulating quotes and screenshots from Google’s autocomplete algorithms. In doing so, we invite the reader to re-explore Google’s autocomplete algorithms in a creative, playful, and reflexive way, thereby rendering more visible some of the excitement and frivolity that comes from being and becoming part of the riddling rhythm of the algorithmic everyday life.
This chapter introduces cluster analysis as case based method. Its aim is twofold. Firstly, it introduces two key issues that are key to the practice of cluster analysis, and are extensively referred to within the related literature: a) the aims and uses of cluster analysis, and b) the types of cluster analysis. Secondly, I ask a more general question about the notion of the case: What can cluster analysis, as a case based method, teach us about the case? What questions are raised in thinking about some of the key issues that are encountered in cluster analysis? To what extent are some of the difficulties associated with the method in fact a reflection of the difficulties of conceptualising the ‘case’ in the first place? And to what extent might we learn about ways of thinking about the case, and groups of cases, through reflexively considering the difficulties involved in sorting out multiple cases?
On the one hand, then, the chapter acts as a brief introduction to cluster analysis as a case based method and some key aspects involved in its practice. On the other hand, and forming the larger part of the chapter, it reflexively argues that the method itself reflects, to some extent at least, the ontology of the ‘case’ more generally. Here, I ask about cluster analysis and the particular points of contention and unresolved difficulties associated with this method. From these particular issues, I explore what it is about the case that might help to explain some of the on-going, seemingly constant, irresolvable issues associated with cluster analysis. Whilst I do not claim to fully answer this grand ontological question about the nature of the case, I nevertheless consider it in relation to this particular methodological approach that is used to learn more about cases and their properties more generally. Overall, then, this chapter touches on the epistemological issues involved in knowing the case through the use of a particular case-based method, i.e. cluster analysis, and asks what the ontological issues of the case might be, given these epistemological issues. A good starting point, then, towards achieving this goal is to simply think about the aims and uses of the method in question