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| Participatory Evaluation× | Most Significant Change× | |
|---|---|---|
| Područje | Public Policy | Public Policy |
| Obitelj | Process / pipeline | Process / pipeline |
| Godina nastanka≠ | 1998 | 2005 |
| Tvorac≠ | J. Bradley Cousins & Elizabeth Whitmore | Rick Davies & Jess Dart |
| Vrsta≠ | Collaborative, stakeholder-engaged evaluation approach | Participatory, story-based monitoring and evaluation technique |
| Temeljni izvor≠ | Cousins, J. B., & Whitmore, E. (1998). Framing participatory evaluation. New Directions for Evaluation, 1998(80), 5–23. DOI ↗ | Davies, R., & Dart, J. (2005). The 'Most Significant Change' (MSC) Technique: A Guide to Its Use. link ↗ |
| Drugi nazivi≠ | Collaborative Evaluation, Stakeholder-Based Evaluation, Practical Participatory Evaluation | MSC, MSC Technique, Story-Based Monitoring, Davies-Dart Most Significant Change |
| Srodne | 4 | 4 |
| Sažetak≠ | Participatory evaluation is a family of approaches in which stakeholders — program staff, beneficiaries, community members — are engaged as active partners in conducting the evaluation rather than as passive subjects of it. In their influential 1998 framing, J. Bradley Cousins and Elizabeth Whitmore distinguished two streams: practical participatory evaluation, oriented to improving program decisions and use, and transformative participatory evaluation, oriented to empowerment and social justice. What unites them is shared control of the inquiry, but they vary along dimensions of who participates, how much control they hold, and how deeply they are involved. | 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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