Abstract
Facilitated learning approaches are increasingly being used as a means to enhance climate and sustainability collaborations working across disciplines, regions, and scales. With investments into promoting and supporting inter- and transdisciplinary learning in major programs on complex global challenges like climate change on the rise, scholars and practitioners are calling for a more grounded and empirical understanding of learning processes and their outcomes. Yet, methodologies for studying the interplay between learning and change in these initiatives remain scarce, owing to both the “hard to measure” nature of learning and the complexity of large-scale program implementation and evaluation. This paper proposes a new method for studying social learning in the context of large research programs. It aims to analyze the social learning of researchers and practitioners engaged in these programs and assess the contributions of this learning to the resilience of the natural and social systems that these programs seek to influence. We detail the theoretical basis for this new approach and set out six steps for developing multi-layered contribution pathways and contribution stories with stakeholders to document both the process and outcomes of social learning. The proposed method, we argue, can strengthen our analytical capacity to uncover the structural drivers and barriers to social learning that are often masked by the complexity of large-scale programs. An illustrative example, drawn from a large-scale climate adaptation research program, provides evidence on how this method might advance our methodological strategies for studying learning in these programs. We conclude by highlighting two key methodological contributions brought about through this approach, and by reflecting on opportunities for further methodological development. Enriching our understanding of learning and change processes, we argue, is an important avenue for understanding how we can pursue transformations for sustainability.
Highlights
Global challenges such as climate change and sustainable development are characterized by their complexity
We piloted an adapted model of contribution analysis—an evaluation method used to assess the contribution of an intervention to an observed outcome—to study the influence of Social learning (SL) on program processes and outcomes in large research programs focused on climate change and resilience
Title of the change case: Social learning processes contributed to the sustained engagement of high-level government personnel and ongoing collaboration between the Botswana Government, the University of Botswana (UB) and Oxfam Key actors: James and David (Oxfam-at Scale in Semi-Arid Regions (ASSAR) partners); Thomas; Moteane Contribution claims and associated scenes of the contribution story: (1) Tailored workshops contributed to mutual engagement: The Vulnerability and Risk Assessment (VRA) methodology was adapted by the Oxfam team to strengthen Southern partners’ capacity to engage in user-oriented research-later called RiU, or research-into-use, Cundill (2018)
Summary
Global challenges such as climate change and sustainable development are characterized by their complexity. The development of methods for assessing SL processes and their outcomes within these programs remains slow (Ensor and Harvey, 2015; van Epp and Garside, 2019) This is, in part, due to the complexity of both large multi-partner programs, and of learning and social change (Buffardi et al, 2019). Failure to effectively assess SL and its contributions to wider programmatic outcomes can limit our ability and inclination to invest in the strengths of these approaches, and our understanding of what their limits might be To confront this challenge, we piloted an adapted model of contribution analysis—an evaluation method used to assess the contribution of an intervention to an observed outcome—to study the influence of SL on program processes and outcomes in large research programs focused on climate change and resilience. In doing so we seek to further expand the “methodological toolbox” (Tschakert and Dietrich, 2010) for understanding the interplay between learning and change in large-scale programs; dynamics that are often obscured and difficult to study
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