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Policy Feedback Analysis×Realist Evaluation×
Lĩnh vựcPublic AdministrationPublic Policy
HọProcess / pipelineProcess / pipeline
Năm ra đời19931997
Người khởi xướngPaul PiersonRay Pawson & Nick Tilley
LoạiTheoretical-analytical framework for policy effects on politicsTheory-driven, generative evaluation approach
Công trình gốcPierson, P. (1993). When Effect Becomes Cause: Policy Feedback and Political Change. World Politics, 45(4), 595–628. DOI ↗Pawson, R., & Tilley, N. (1997). Realistic Evaluation. London: SAGE Publications. ISBN: 9780761950097
Tên gọi khácPolicy Feedback Theory Analysis, Feedback Effects Analysis, Policy-as-Cause Analysis, Self-Reinforcing Policy AnalysisRealistic Evaluation, Theory-Driven Realist Evaluation, CMO Configuration Analysis, Pawson-Tilley Evaluation
Liên quan44
Tóm tắtPolicy feedback analysis examines how policies, once enacted, reshape the politics that follow — turning yesterday's policy effects into today's political causes. Drawing on Paul Pierson's foundational 1993 article 'When Effect Becomes Cause,' it holds that policies are not just outputs of politics but powerful forces that create resources and incentives for groups, build administrative capacities, and shape how citizens understand their interests and their government. By tracing these resource and interpretive feedback effects over time, the method explains why some policies become self-reinforcing and politically durable, why others undermine their own support, and why policy change is often path-dependent and hard to reverse.Realist evaluation is a theory-driven approach to evaluating programs and policies that asks not simply 'does it work?' but 'what works, for whom, in what circumstances, and why?'. Developed by Ray Pawson and Nick Tilley in their 1997 book Realistic Evaluation, it treats interventions as theories incarnate: programs offer resources or opportunities that trigger underlying mechanisms of reasoning and response in participants, and those mechanisms only fire in particular contexts. The unit of analysis is the Context-Mechanism-Outcome (CMO) configuration, and the goal is to build and refine middle-range theory that explains differential outcomes across settings.
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