Power dynamics in transdisciplinary research for sustainable urban transitions

Transdisciplinary Research (TDR) fosters collaboration amongst stakeholders from different academic disciplines and sectors of society, to jointly search for solutions to complex challenges including rapid urbanization. In order to enhance collaboration and ensure reciprocity of expertize amongst stakeholders, flattening the hierarchy of power is critical for working together to develop research agendas and experiment solutions. However, context dependent research on power dynamics in TDR is still limited and yet it is one of the conditions for understanding how to optimize the value and relevance of TDR in transitions to sustainable cities. This paper sheds light on power dynamics in TDR, using a qualitative analysis of case studies in the cities of Accra (Ghana), Durban (South Africa) and Nairobi (Kenya). Learnings from the case studies suggest that researchers should not overstate expert terminologies and illuminate the controversies that stem from stark differences in their methodological domains, during interfaces with stakeholders from policy and local communities, as this can dent the societal credibility and inclusiveness of the TDR process. Secondly, it is important to seize the potential of creating joint learning platforms using micro-level solutions in informal settlements, since it empowers local community representatives to assert themselves as co-researchers with legitimate perspectives on how the research agenda can be reshaped. Local-level solutions also provide scientific and policy windows for discerning expert biases in approaches used by academics and policy-makers to presuppose what can be done about the situation of under-privileged communities, which is key in building consensus when setting the research agenda.

Automated summary

Transdisciplinary Research (TDR) fosters collaboration among stakeholders from different disciplines and sectors to address complex challenges. This paper highlights the importance of flattening power hierarchies, avoiding expert terminology, utilizing local-level solutions, and recognizing expert biases in TDR.