Very much looking forward presenting at the ‘Pollution, Environmental Justice, and Citizen Science’ workshop held at the University of Warwick, May 3rd and 4th, 2017. The workshop is part of the Toxic Expertise: Environmental Justice and the Global Petrochemical Industry research project, led by Dr. Alice Mah. The project focuses on debates arising between ‘experts’ and supposed ‘non-experts’ about contaminant-related issues.
The paper I’ll be presenting is co-written with Dr. Max Liboiron, and examines the ways in which our lab, Civic Lab for Environmental Action Research (CLEAR), is incorporating the concept of ‘ethnographic refusal’ into citizen science. CLEAR is an STS-informed, feminist and justice-based marine science lab, directed by Dr. Liboiron, that examines plastic pollution. Abstract below.
Refusal in Citizen science: A Decolonial, Ethical Approach to Data Circulation (or Not)
Ethnographic refusal is an methodological approach originating in anthropology about not disclosing data. At first, settler anthropologists saw refusing to disclose all information as an ethical problem as the right to know was paramount. Over time, Indigenous scholars have rearticulated refusal as a decolonial and deeply ethical method; settler researchers and audiences should not be able to access all information about Indigenous and other groups, and that the refusal to recount some information so it remains locally controlled and related is sometimes the more ethical stance. In this paper, we explore how our citizen science laboratory (Civic Laboratory for Environmental Action Research, or CLEAR) has brought ethnographic refusal and people’s right to refuse into the natural sciences through a case of studying plastic pollution in sustenance food webs in Newfoundland, Canada. Refusal and other decolonizing methods are under-explored in citizen science literature and practices, and offer new avenues through which to think about methodologies and ethics for environmental justice premised on the idea that free and open circulation of contamination data is not inherently good.