手法を比較

	Collaborative Governance Assessment	Realist Evaluation
分野	Public Administration	Public Policy
系統	Process / pipeline	Process / pipeline
提唱年	2008	1997
提唱者	Chris Ansell & Alison Gash	Ray Pawson & Nick Tilley
種類	Process-based governance assessment framework	Theory-driven, generative evaluation approach
原典	Ansell, C., & Gash, A. (2008). Collaborative Governance in Theory and Practice. Journal of Public Administration Research and Theory, 18(4), 543–571.	Pawson, R., & Tilley, N. (1997). Realistic Evaluation. London: SAGE Publications. ISBN: 9780761950097
別名	Collaborative Governance Analysis, Ansell-Gash Governance Framework, Multi-Stakeholder Governance Assessment	Realistic Evaluation, Theory-Driven Realist Evaluation, CMO Configuration Analysis, Pawson-Tilley Evaluation
関連	4	4
概要	Collaborative governance assessment is a framework for analysing arrangements in which public agencies and non-state stakeholders deliberate together to make or implement public policy by consensus. Synthesised by Chris Ansell and Alison Gash in their 2008 article from a meta-analysis of over a hundred cases, it identifies the starting conditions, institutional design and facilitative leadership that feed into an iterative collaborative process and ultimately shape outcomes. The framework treats collaboration not as a single event but as a cycle of face-to-face dialogue, trust-building, shared understanding and intermediate commitments. Its purpose is to explain why some multi-stakeholder partnerships succeed while others stall or collapse.	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.