Compara mètodes
Revisa els mètodes seleccionats l'un al costat de l'altre; les files que difereixen es ressalten.
| Outcome Harvesting× | Most Significant Change× | |
|---|---|---|
| Camp | Public Policy | Public Policy |
| Família | Process / pipeline | Process / pipeline |
| Any d'origen≠ | 2012 | 2005 |
| Autor original≠ | Ricardo Wilson-Grau & Heather Britt | Rick Davies & Jess Dart |
| Tipus≠ | Retrospective, outcome-led evaluation approach | Participatory, story-based monitoring and evaluation technique |
| Font seminal≠ | Wilson-Grau, R., & Britt, H. (2012). Outcome Harvesting. Cairo: Ford Foundation MENA Office (revised November 2013). link ↗ | Davies, R., & Dart, J. (2005). The 'Most Significant Change' (MSC) Technique: A Guide to Its Use. link ↗ |
| Àlies≠ | OH, Wilson-Grau Outcome Harvesting | MSC, MSC Technique, Story-Based Monitoring, Davies-Dart Most Significant Change |
| Relacionats | 4 | 4 |
| Resum≠ | Outcome Harvesting is a participatory evaluation approach, developed by Ricardo Wilson-Grau and Heather Britt, that identifies outcomes after they have occurred and then works backward to determine whether and how an intervention contributed to them. Instead of measuring progress against predefined targets, evaluators 'harvest' evidence of observable changes in the behaviour, relationships, actions or policies of social actors, then assess the program's contribution to each. It is designed for complex settings where cause-and-effect relationships are not fully understood in advance and outcomes cannot be specified ahead of time. | 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. |
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