Social learning and climate change adaptation: evidence for international development practice
Bibliographic record
Abstract
The potential for social learning to address complex, interconnected social and environmental challenges, such as climate change adaptation, is receiving increasing attention in research and practice. Social learning approaches vary, but commonly include cycles of knowledge sharing and joint action to co‐create knowledge, relationships, and practices among diverse stakeholders. This results in learning and change that goes beyond the individual into communities, networks, or systems. Many authors have focused on analysis of case studies to better understand the contexts in which such learning occurs. In this paper, we look across this literature to draw out lessons for international development practice. To support those looking to purposively design social learning interventions for adaptation, we focus on four areas: lessons learned and the principles adopted when using a social learning approach, examples of tools and methods used, approaches to evaluating social learning, and examples of its impact. While we identify important lessons for practice within each of these areas, three cross‐cutting themes emerge. These are: the importance of developing a shared view among those initiating learning processes of how change might happen and of how social learning fits within it, linking this locus of desired change to the tools employed; the centrality of skilled facilitation and in particular how practitioners may shift toward being participants in the collective learning process; and the need to attend to social difference, recognizing the complexity of social relations and the potential for less powerful actors to be co‐opted in shared decision making. WIREs Clim Change 2015, 6:509–522. doi: 10.1002/wcc.348 This article is categorized under: Vulnerability and Adaptation to Climate Change > Learning from Cases and Analogies Climate and Development > Knowledge and Action in Development
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.052 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.004 | 0.020 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".