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| Most Significant Change× | Outcome Mapping× | |
|---|---|---|
| Πεδίο | Public Policy | Public Policy |
| Οικογένεια | Process / pipeline | Process / pipeline |
| Έτος προέλευσης≠ | 2005 | 2001 |
| Δημιουργός≠ | Rick Davies & Jess Dart | Sarah Earl, Fred Carden & Terry Smutylo (IDRC) |
| Τύπος≠ | Participatory, story-based monitoring and evaluation technique | Actor-centred planning, monitoring and evaluation approach |
| Θεμελιώδης πηγή≠ | Davies, R., & Dart, J. (2005). The 'Most Significant Change' (MSC) Technique: A Guide to Its Use. link ↗ | Earl, S., Carden, F., & Smutylo, T. (2001). Outcome Mapping: Building Learning and Reflection into Development Programs. Ottawa: International Development Research Centre (IDRC). ISBN: 9780889369597 |
| Εναλλακτικές ονομασίες≠ | MSC, MSC Technique, Story-Based Monitoring, Davies-Dart Most Significant Change | OM, IDRC Outcome Mapping, Behavioural Change Mapping |
| Συναφείς | 4 | 4 |
| Σύνοψη≠ | The Most Significant Change (MSC) technique is a participatory, story-based approach to monitoring and evaluation developed by Rick Davies and refined with Jess Dart. It involves the systematic collection of stories of significant change from the field and the deliberative selection of the most significant of these by panels of stakeholders. There are no predefined indicators; instead, value judgements about what change matters most are made transparently by those involved, making MSC especially suited to capturing unexpected and qualitative outcomes in complex programs. | Outcome Mapping is a planning, monitoring and evaluation methodology developed by the International Development Research Centre (IDRC) and set out by Sarah Earl, Fred Carden and Terry Smutylo in 2001. It redefines results as changes in the behaviour, relationships, activities and actions of the people and organisations a program works with directly — its 'boundary partners' — rather than as downstream development impacts. By focusing on the behavioural changes a program can plausibly influence, Outcome Mapping addresses the attribution problem head-on and shifts evaluation toward learning and contribution. |
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