방법 비교

	Most Significant Change for Development	Participatory Video
분야	Development Studies	Development Studies
계열	Process / pipeline	Process / pipeline
기원 연도	2005	2006
창시자	Rick Davies & Jess Dart	Don Snowden (Fogo process, 1960s); codified by Nick and Chris Lunch (InsightShare) and Shirley White
유형	Participatory, story-based monitoring and evaluation technique	Participatory visual research and communication method
원전	Davies, R., & Dart, J. (2005). The 'Most Significant Change' (MSC) Technique: A Guide to Its Use. CARE International, Oxfam, et al.	Lunch, N., & Lunch, C. (2006). Insights into Participatory Video: A Handbook for the Field. Oxford: InsightShare. ISBN: 9782940290086
별칭	MSC technique, Story-based monitoring, Most significant change stories, Monitoring without indicators	PV, Community Video, Video for Development, Participatory Filmmaking
관련	4	4
요약	The Most Significant Change (MSC) technique is a participatory, story-based approach to monitoring and evaluating development programmes that dispenses with predefined indicators. Developed by Rick Davies and elaborated with Jess Dart in their widely used 2005 guide, it works by systematically collecting stories of significant change from those closest to a programme and then filtering and selecting the most significant of them through deliberative panels at successive levels of the organisational hierarchy. The result is a structured, dialogical account of what stakeholders themselves judge to be the most important outcomes of an intervention.	Participatory Video (PV) is a set of techniques through which a group or community creates its own films to explore issues, voice concerns, communicate with each other, and advocate to outsiders. Rooted in the 1960s Fogo Island process and codified for development practice by Nick and Chris Lunch of InsightShare and by Shirley White, PV treats the camera not as the property of an outside researcher but as a tool placed in the hands of community members, so that the process of making the video — as much as the film itself — builds confidence, analysis, and collective agency.