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Systematising Policy Learning: From Monolith to Dimensions
Author(s) -
Dunlop Claire A.,
Radaelli Claudio M.
Publication year - 2013
Publication title -
political studies
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.406
H-Index - 83
eISSN - 1467-9248
pISSN - 0032-3217
DOI - 10.1111/j.1467-9248.2012.00982.x
Subject(s) - typology , field (mathematics) , process (computing) , policy learning , monolith , computer science , knowledge management , epistemology , artificial intelligence , management science , sociology , data science , machine learning , mathematics , economics , biology , pure mathematics , philosophy , anthropology , operating system , catalysis , biochemistry
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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