**1. Introduction**

The urgency and immensity of challenges like climate change and social inequality call for new ways of understanding the world and effecting change. Such "wicked problems" [1] are difficult to solve, as they are complex, contested and ambiguous with respect to their underlying values and causes [2] and display complex interdependencies with prevailing economic, technological and social systems. In confronting these societal challenges, transitions scholars advocate moving beyond incremental improvements, which have proven ineffectual, to find ways of achieving fundamental transitions or transformations in core systems in the direction of sustainability [3]. Such transitions entail "profound changes in dominant institutions, practices, technologies, policies, lifestyles and thinking" [4] (p. 6), at the heart of which are novel processes for knowledge production and social learning [5,6].

One such process is transdisciplinary (TD) coproduction, a knowledge production process in which individuals with different disciplinary, professional and experiential backgrounds combine academic and practice-based knowledges in the shared production, interpretation and ultimate use of scientific knowledge and its products [7–12]. These attributes of TD coproduction suggest optimal conditions for the social learning deemed important for sustainability transitions [2]. In the context of sustainability transitions, social learning generally refers to collective learning that generates collective responses to a shared dilemma or societal challenge. While such learning is clearly important, more work is needed to conceptualize social learning within TD coproduction and to better understand precisely how learning unfolds and knowledge is produced in such configurations. Indeed, the varied use of the

term "social learning" across multiple disciplines and the consequent ambiguity surrounding its causes and effects has resulted in a notable lack of conceptual clarity surrounding the concept. This makes it difficult to assess whether social learning has occurred and, if so, what kind of learning has taken place, to what extent, between whom and how [13].

To address this challenge, this paper develops an analytical framework that applies a social practice theory (SPT) lens to illuminate the constituent elements and dynamics of social learning in the context of TD coproduction. Adopting an SPT approach affords a means of interpreting concrete practices at the local scale and exploring the potential for scaling them up. This framework is then applied to a real-world case in order to illustrate how social learning unfolded in a grassroots TD coproduction process. The process under study took place over 2018–2019 and brought together researchers from the University of Toronto, two funders (The Atmospheric Fund (TAF) and the City of Toronto) and 11 community practitioners who each represented a different climate intervention located in the Greater Toronto and Hamilton Area (GTHA). The aim of this effort was to codevelop an evaluation framework that would enable the assessment of their processes, their short- to medium-term outcomes and any longer or deeper sustainability impacts.

The aim of this paper is to bring coproduction processes for transition into conversation with social learning in order to clarify and yield a deeper understanding of both. Doing so sheds light on social learning's plural and dynamic nature in the context of TD coproduction efforts, in turn potentially strengthening the TD coproduction effort in the process. Ultimately, this forms the basis for a future research agenda that explores and demonstrates how social learning might be operationalized or leveraged in service of sustainability transitions. This paper is structured as follows: a review of the TD coproduction, social learning and social practice theory literatures, which underpin the social learning analytical framework, leads into a discussion of the analytical framework. This is followed by a description of the TD coproduction case at the centre of this study and how it is illustrative of the processes and outcomes of social learning referenced in the framework. The paper concludes with a discussion of the significance of conceptualizing social learning and TD coproduction in this way, particularly as a foundation for future research.
