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Systematising Policy Learning: From Monolith to Dimensions

2012/09/28 by Claire A. Dunlop, Claudio M. Radaelli · 558 citations
Decision Sciences · Environmental Science · Mathematics · Social Sciences · #Artificial intelligence #Biology #Computer science #Data science #Economics #Epistemology #Evaluation and Performance Assessment #Field (mathematics) #Knowledge management #Machine learning #Management science #Mathematics #Monolith #Policy Transfer and Learning #Policy learning #Process (computing) #Sociology #Sustainability and Climate Change Governance #Typology

paper · open access · doi:10.1111/j.1467-9248.2012.00982.x

published in Political Studies 61(3), 599-619 (SAGE Publishing)

openalex publication_date 2012/09/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06

Abstract

The field of policy learning is characterised by concept stretching and a lack of systematic findings. To systematise them, we combine the classic Sartorian approach to classification with the more recent insights on explanatory typologies, distinguishing between the genus and the different species within it. By drawing on the technique of explanatory typologies to introduce a basic model of policy learning, we identify four major genera in the literature. We then generate variation within each cell by using rigorous concepts drawn from adult education research. By looking at learning through the lenses of knowledge utilisation, we show that the basic model can be expanded to reveal sixteen different species. These types are all conceptually possible, but are not all empirically established in the literature. Our reconstruction of the field sheds light on mechanisms and relations associated with alternative operationalisations of learning and the role of actors in the process of knowledge construction and utilisation. By providing a comprehensive typology, we mitigate concept-stretching problems and lay the foundations for the systematic comparison across and within cases of policy learning.

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