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Archetype analysis in sustainability research: meanings, motivations, and evidence-based policy making
Archetypes are increasingly used as a methodological approach to understand recurrent patterns in variables and processes that shape the sustainability of social-ecological systems. The rapid growth and diversification of archetype analyses has generated variations, inconsistencies, and confusion ab...
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Published in: | Ecology and society 2019-07, Vol.24 (2), p.26, Article art26 |
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creator | Oberlack, Christoph Sietz, Diana Bürgi Bonanomi, Elisabeth de Bremond, Ariane Dell'Angelo, Jampel Eisenack, Klaus Ellis, Erle C. Epstein, Graham Giger, Markus Heinimann, Andreas Kimmich, Christian Kok, Marcel TJ Manuel-Navarrete, David Messerli, Peter Meyfroidt, Patrick Václavík, Tomá Villamayor-Tomas, Sergio |
description | Archetypes are increasingly used as a methodological approach to understand recurrent patterns in variables and processes that shape the sustainability of social-ecological systems. The rapid growth and diversification of archetype analyses has generated variations, inconsistencies, and confusion about the meanings, potential, and limitations of archetypes. Based on a systematic review, a survey, and a workshop series, we provide a consolidated perspective on the core features and diverse meanings of archetype analysis in sustainability research, the motivations behind it, and its policy relevance. We identify three core features of archetype analysis: recurrent patterns, multiple models, and intermediate abstraction. Two gradients help to apprehend the variety of meanings of archetype analysis that sustainability researchers have developed: (1) understanding archetypes as building blocks or as case typologies and (2) using archetypes for pattern recognition, diagnosis, or scenario development. We demonstrate how archetype analysis has been used to synthesize results from case studies, bridge the gap between global narratives and local realities, foster methodological interplay, and transfer knowledge about sustainability strategies across cases. We also critically examine the potential and limitations of archetype analysis in supporting evidence-based policy making through context-sensitive generalizations with case-level empirical validity. Finally, we identify future priorities, with a view to leveraging the full potential of archetype analysis for supporting sustainable development. |
doi_str_mv | 10.5751/ES-10747-240226 |
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The rapid growth and diversification of archetype analyses has generated variations, inconsistencies, and confusion about the meanings, potential, and limitations of archetypes. Based on a systematic review, a survey, and a workshop series, we provide a consolidated perspective on the core features and diverse meanings of archetype analysis in sustainability research, the motivations behind it, and its policy relevance. We identify three core features of archetype analysis: recurrent patterns, multiple models, and intermediate abstraction. Two gradients help to apprehend the variety of meanings of archetype analysis that sustainability researchers have developed: (1) understanding archetypes as building blocks or as case typologies and (2) using archetypes for pattern recognition, diagnosis, or scenario development. We demonstrate how archetype analysis has been used to synthesize results from case studies, bridge the gap between global narratives and local realities, foster methodological interplay, and transfer knowledge about sustainability strategies across cases. We also critically examine the potential and limitations of archetype analysis in supporting evidence-based policy making through context-sensitive generalizations with case-level empirical validity. 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The rapid growth and diversification of archetype analyses has generated variations, inconsistencies, and confusion about the meanings, potential, and limitations of archetypes. Based on a systematic review, a survey, and a workshop series, we provide a consolidated perspective on the core features and diverse meanings of archetype analysis in sustainability research, the motivations behind it, and its policy relevance. We identify three core features of archetype analysis: recurrent patterns, multiple models, and intermediate abstraction. Two gradients help to apprehend the variety of meanings of archetype analysis that sustainability researchers have developed: (1) understanding archetypes as building blocks or as case typologies and (2) using archetypes for pattern recognition, diagnosis, or scenario development. We demonstrate how archetype analysis has been used to synthesize results from case studies, bridge the gap between global narratives and local realities, foster methodological interplay, and transfer knowledge about sustainability strategies across cases. We also critically examine the potential and limitations of archetype analysis in supporting evidence-based policy making through context-sensitive generalizations with case-level empirical validity. 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We demonstrate how archetype analysis has been used to synthesize results from case studies, bridge the gap between global narratives and local realities, foster methodological interplay, and transfer knowledge about sustainability strategies across cases. We also critically examine the potential and limitations of archetype analysis in supporting evidence-based policy making through context-sensitive generalizations with case-level empirical validity. Finally, we identify future priorities, with a view to leveraging the full potential of archetype analysis for supporting sustainable development.</abstract><cop>Ottawa</cop><pub>Resilience Alliance</pub><doi>10.5751/ES-10747-240226</doi><oa>free_for_read</oa></addata></record> |
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subjects | archetype Archetypes Case studies Ecological monitoring Empirical analysis Knowledge management land systems Methodological approaches Narratives Pattern recognition Policy making social-ecological system Social-ecological systems Sustainability Sustainable development vulnerability |
title | Archetype analysis in sustainability research: meanings, motivations, and evidence-based policy making |
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