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  • Author or Editor: Claudio M Radaelli x
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Policy learning is an attractive proposition, but who learns and for what purposes? Can we learn the wrong lesson? And why do so many attempts to learn what works often fail? In this article, we provide three lessons. First, there are four different modes in which constellations of actors learn. Hence our propositions about learning are conditional on which of the four contexts we refer to. Second, policy learning does not just happen; there are specific hindrances and triggers. Thus, learning can be facilitated by knowing the mechanisms to activate and the likely obstacles. Third, learning itself is a conditional final aim: although the official aspiration of public organisations and politicians is to improve on public policy, policy learning can also be dysfunctional – for an organisation, a policy, a constellation of actors or even democracy.

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The literature on policy learning has generated a huge amount of heat (and some light) producing policy learning taxonomies, concepts and methods, yet the efforts to demonstrate why we should think about policy processes in terms of learning have been rare and mostly in the past (Dunlop, Radaelli and Trein, 2018). Additionally, policy learning has progressed in different sub-fields, such as the study of diffusion, transfer, individual and collective learning, social learning, and knowledge utilisation (see the family tree of learning in Dunlop, Radaelli and Trein, 2018; and the fragmentation in sub-fields portrayed in Goyal and Howlett, 2018). This has discouraged the tasks of communicating, comparing and combining insights that, the editors of this special issue remind us, are fundamental to translate research to a wider audience, avoiding jargon and obfuscation.

We offer this chapter to both an audience of academics and to actors involved in policy-processes, be they elected politicians, public managers, activists or pressure groups. We address the academic audience made up of specialists in policy analysis by arguing that the quality of our findings should be judged in terms of ‘translation reach’. We set out to show how we can combine and integrate research on learning so that it can be translated to a wider audience of social scientists looking for cumulative findings, typical lessons, concepts that travel across fields.

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