3.9 Article

Beyond Indicators and Success Stories: An Emerging Method to Assess Social Learning in Large-Scale Transdisciplinary Research Programs

期刊

FRONTIERS IN SOCIOLOGY
卷 6, 期 -, 页码 -

出版社

FRONTIERS MEDIA SA
DOI: 10.3389/fsoc.2021.649946

关键词

social learning; climate adaptation; large-scale programs; participatory evaluation; generative causality; contribution analysis method

资金

  1. Canada's Social Sciences and Humanities Research Council (SSHRC) [435-20191094]
  2. Fonds de recherche du Quebec-Societe et culture (FRQSC) [2019-NP-253637]

向作者/读者索取更多资源

Facilitated learning methods are increasingly utilized to improve climate and sustainability collaborations across different disciplines and scales. This paper introduces a new method for studying social learning in large research programs, aiming to enhance our understanding of learning and change processes for pursuing sustainability transformations.
Facilitated learning approaches are increasingly being used as a means to enhance climate and sustainability collaborations working across disciplines, regions, and scales. With investments into promoting and supporting inter- and transdisciplinary learning in major programs on complex global challenges like climate change on the rise, scholars and practitioners are calling for a more grounded and empirical understanding of learning processes and their outcomes. Yet, methodologies for studying the interplay between learning and change in these initiatives remain scarce, owing to both the hard to measure nature of learning and the complexity of large-scale program implementation and evaluation. This paper proposes a new method for studying social learning in the context of large research programs. It aims to analyze the social learning of researchers and practitioners engaged in these programs and assess the contributions of this learning to the resilience of the natural and social systems that these programs seek to influence. We detail the theoretical basis for this new approach and set out six steps for developing multi-layered contribution pathways and contribution stories with stakeholders to document both the process and outcomes of social learning. The proposed method, we argue, can strengthen our analytical capacity to uncover the structural drivers and barriers to social learning that are often masked by the complexity of large-scale programs. An illustrative example, drawn from a large-scale climate adaptation research program, provides evidence on how this method might advance our methodological strategies for studying learning in these programs. We conclude by highlighting two key methodological contributions brought about through this approach, and by reflecting on opportunities for further methodological development. Enriching our understanding of learning and change processes, we argue, is an important avenue for understanding how we can pursue transformations for sustainability.

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