I can’t wait to talk about feminist research methods, big data and self-tracking in Berlin.

On Thursday, October 6 between 16:00 – 17:30, I will be presenting work in the Panel ‘Bodies’, chaired by Gina Neff. My paper is about Feminism in the era of the Quantified Self: Agency, labour and future markets. I examine dominant discourses of empowerment in apps targeting women (reproductive health and well being), and I discuss how far data collection has the potential to make the voices of women heard, beyond the articulation of consumer demands about digital health. This is new work in progress. 

On Friday, October 7, between 11:00 – 12:30, we’re discussing feminist approaches to big data culture with Helen Kennedy (Sheffield), Jean Burgess, Kate Crawford, Rosemary Lucy Hill, & Kate O’Riordan, in the first roundtable session. The plenary session focuses on visions and imagining of feminist big data futures. The key question is: what would feminist big data, data studies and datavis look like? And as the organiser Helen K. put it in the abstract for the session: ‘How can and should feminists respond to the rise of big data? Given that critique of the assumed objectivity and neutrality of big data and related methods has a feminist history, feminist scholars are well-placed to respond to the problems that big data usher forth. One outcome of objectivity critique is a heavy reliance on qualitative methods in feminist research, yet it is precisely because of the types of problems that feminist scholarship has been so good at identifying that there is a need not just for feminist critiques of quantitative methods, data and assumptions of objectivity, but for feminism to do big data and data visualisation. In other words, we need feminist data studies which is active in creating, representing and communicating data. How do we move forward from critiques of data as not really objective, but cooked, to understanding how and why it matters to feminists and feminism? How do we respond to Haraway’s proposal that encoding and visualisation are inherently patriarchal projects? What might feminist big data, data studies and datavis look like?’