Reframing the future: the role of reflexivity in governance networks in sustainability transitions

Abstract

Regional sustainability networks in the Netherlands are rooted in regional culture and have an emphasis on social learning and effective collaboration between multiple actors. The national 'Duurzaam Door' (Moving Forward Sustainably) Policy Programme regards these networks as generative governance arrangements where new knowledge, actions and relations can co-evolve together with new insights in governance and learning within sustainability transitions. In order to understand the dynamics of the learning in these networks we have monitored emergent properties of social learning between 2014 and 2016. Our focus is particularly on the interrelated role of trust, commitment, reframing and reflexivity. Our aim is to better understand the role and the dynamics of these emergent properties and to see which actors and roles can foster the effectiveness of social learning in regional transitions towards more sustainable ways of living. We used a retrospective analysis with Reflexive Monitoring in Action (RMA), which we combined with the Most Significant Change approach. We found that reflexivity in particular is a critical property at moments that can make or break the process.

The German Permaculture Community from a Community of Practice Perspective

“…Even though the perspective of socio-technical transitions emerged as a reaction to path dependencies, it has preserved a prevalent focus on the reproduction of rules and meanings [14]. Little has been done to conceptualise learning processes which essentially lie at the core of transition processes [25]. Consequently, transitions studies still lack analytical approaches that focus on the actual production of knowledge, practices and institutions, truly accounting for the emergence of radical alternatives and the relational and systemic nature of the associated processes of transformation towards sustainable development [11,[24][25][26][27].…”

“…Little has been done to conceptualise learning processes which essentially lie at the core of transition processes [25]. Consequently, transitions studies still lack analytical approaches that focus on the actual production of knowledge, practices and institutions, truly accounting for the emergence of radical alternatives and the relational and systemic nature of the associated processes of transformation towards sustainable development [11,[24][25][26][27]. For a more appropriate analysis of social innovation and the emergence of alternative practices, Howaldt and Schwarz stress the importance of more rigorous analysis of reflexive social learning processes, self-organisation and communication processes [14].…”

Implications of a food system approach for policy agenda-setting design

“…However, directionality is not the only precondition to enable a transformative agenda-setting process, transformative processes is also associated with “reflexivity” ( Weber and Rohracher, 2012 ). While reflexivity has multiple and contested meanings ( Beck et al, 1994 ), in the literature related to societal transition the notion of “reflexivity” relates to system's ability to critically reflect on existing values, practices and policies, coupled with their capacity to adapt, (re)design policy instruments and support alternative options ( Grin et al, 2010 ; Sol et al, 2018 ). The ability of governments to engage all food system actors to deliberate over current values and practices, and a capacity to monitor and evaluate, learn and respond as creatively, efficiently and responsibly as possible.…”

Transformative Outcomes: Assessing and Reorienting Experimentation with Transformative Innovation Policy

“…Such deep learning requires a holistic and context-specific understanding of the sustainability problems faced by the socio-technical systems (Raven et al, 2017). Deep second-order learning as a transformative outcome also enhances reflexivity, trust and retention of new rules among actors and enables translation of the learnings from niche building process into more generalisable forms (Turnheim et al, 2018;Safarzyńska et al, 2012, Borghei andMagnusson, 2018;Sol et al, 2017;van Mierlo et al, 2020). For broad and deep learning to occur, the type and quality of actor-networks is a key factor, which is our next transformative outcome.…”