Social learning and climate change adaptation: evidence for international development practice
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it