AARE Symp paper. Pre Nov

The submitted abstract

Title: Theory games: from monstrous puppetry to productive stupidity

Machine learning, an umbrella term to describe a wide range of statistically-based algorithms used to make predictions from huge data sets, often being collected in real time, is being deployed across an increasing range of fields. Sometimes simply called big data, these practices have had significant impacts on many aspects of human activity.  The volume, velocity and variety of data, coupled with techniques from neural networks in the field of artificial intelligence has produced what might be regarded as a perfect storm for education and social science research in general. This paper is based upon the theory-data entanglement that can be located in these developments. The rise and rise of “the digital” is intimately articulated with thinking about the posthuman, and the role of theory (Braidotti, 2013). The inter-relationship is necessarily open-ended and will likely pose interesting challenges for the generation of new prefixes to be associated with human, theory and method. For big data is only a harbinger of what is to come and how quickly it will arrive.  The empirical laws that describe the rate of improvement in digital and related technologies suggest an ever rich field in which data, theory, method and the ways we think about their entanglements shuffle between the certainty of long standing intellectual positions and the uncertainty of not knowing.

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