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Keynote Lecture by Professor Eva Bendix Petersen

On Monday morning, 28th of November, Professor Eva Bendix Petersen (Roskilde University) gave a keynote lecture titled “Data found us”: a multi-voiced critique of some new materialist empirical research tropes.

New materialist and posthuman research methodologies are quickly gaining traction in many fields of research. According to its proponents it takes us to radically new places of thought and action as it reconfigures central notions such as researcher positioning, critique and data. The keynote lecture considers how the notion of ‘data’ is invoked in a handful of works from the social sciences that seek to make empirical contributions, that is, to create new knowledge of the world using data. Through this critique Prof. Petersen comes to question the extent to which the approach constitutes a reconfiguration or whether, rather, it is blindly continuous with some old and problematic tropes.

You can watch the entire lecture here:

 

Eva Bendix Petersen is Professor in the Department of People and Technology at Roskilde University in Denmark. Her research interests include the production of Homo Academicus; the effects of neoliberal policy reforms on academic work and higher education; social and feminist theory as lived experience, and post-foundational research methodologies. She is the author of several book chapters and journal articles, and her most recent co-edited books include ‘Interrupting the Psy-Disciplines in Education’ (Palgrave) co-edited with Zsuzsa Millei, and ‘Education Policy and Contemporary Theory’ (Routledge) co-edited with Kalervo Gulson and Matthew Clarke.


Eva Bendix Petersen’s talk was co-organised by the University of Helsinki Doctoral Programmes SEDUCE (School, Education, Society and Culture) and SKY (Gender, Culture, and Society) and AGORA for the Study of Social Justice & Equality in Education Research Centre. The lecture was part of the PhD course Cultural and feminist methodologies in education and social sciences: A special focus on data.

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