Closer than they look at first glance: A systematic review and a research agenda regarding measurement practices for policy learning
Learning is a cognitive and social dynamic through which diverse types of actors involved in policy processes acquire, translate and disseminate new information and knowledge about public problems and solutions. In turn, they maintain, strengthen or revise their policy beliefs and preferences. Despi...
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| Autores principales: | , , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
OpenEdition
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/366d45f4b2d34caba2fe2b6e51f0f7ff |
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| Sumario: | Learning is a cognitive and social dynamic through which diverse types of actors involved in policy processes acquire, translate and disseminate new information and knowledge about public problems and solutions. In turn, they maintain, strengthen or revise their policy beliefs and preferences. Despite the conceptual and theoretical developments over the last years, concerns about the measurement of policy learning remain persistent. Based on the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) approach, this article reports the results of a systematic review of the existing practices for measuring policy learning in the public administration and policy research. In addition to operationalizations, data sources, methods of analysis and levels of analysis, we examine how the reviewed articles deal with the processual nature of policy learning. We show that the existing measurement practices transcend the research streams on policy learning for the most part, which extends the argument developed by Dunlop and Radaelli (2018) that policy learning is an analytical framework of the policy process. Based on these results, we argue for more transparent operationalizations, discuss the strengths and weaknesses of direct and indirect measurement approaches, and call for more creativity in designing measurement methods that recognize the multilevel nature of policy learning. |
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