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Research often explores the role of scientific expertise in policymaking from an externalised perspective, mostly focusing on how policymakers use and abuse scientific expertise through political learning. However, very little is known about political learning by scientific experts. What strategies do they use to maintain and advance their access to, and influence on policymaking? Using process tracing, we illustrate how scientific experts' access to policymaking is challenged as a policy issue develops. We explore how this nudges scientific experts to engage in political learning and employ political advocacy strategies to enhance science's role in policy making, corresponding to evolving political opportunity structures. We empirically trace this using the case of EU climate policy development between 1990 and 2022. We identify three main sets of advocacy strategies used by scientific experts: Narrative and semantic (policy issue-oriented), Socialisation (Actors-oriented), and Governance (systems and structures-oriented). In doing so, this article illustrates the political actorness and agency of scientific experts and provides a supplementary understanding to the role of science in public policy and policy change, not only as a function of policymaker's instrumentalization of science, but also as a function of how scientific experts actively advocate for science's role in public policy. [ABSTRACT FROM AUTHOR]

Over the years, policy learning has emerged as a theoretical lens to provide an alternative understanding of powering-based approaches to the policy process and policy outcomes. This has been a success story, with policy learning gaining presence among theoretical frameworks on the policy process. However, the puzzling-powering dichotomy is reductive. In real-world policy processes, we find both puzzling and powering. Also, we cannot reduce the value of learning to what is not explained by power alone. This Introduction shows how puzzling and powering interact and with what consequences on policy processes, moving beyond the dichotomy. We present how the four contributions of this Special Issue explore this territory and suggest how to approach the design of governance architectures for learning. [ABSTRACT FROM AUTHOR]