Fostering Learning in Large Programmes and Portfolios: Emerging Lessons from Climate Change and Sustainable Development
Bibliographic record
Abstract
In fields like climate and development, where the challenges being addressed can be described as “wicked”, learning is key to successful programming. Useful practical and theoretical work is being undertaken to better understand the role of reflexive learning in bringing together different knowledge to address complex problems like climate change. Through a review of practical cases and learning theories commonly used in the areas of resilience, climate change adaptation and environmental management, this article: (i) reviews the theories that have shaped approaches to reflexive learning in large, highly-distributed climate change and resilience-building programmes for development; and (ii) conducts a comparative learning review of key challenges and lessons emerging from early efforts to promote and integrate reflexive learning processes in programmes of this nature. Using a case study approach, the authors focus on early efforts made in four large, inter-related (or nested) programmes to establish, integrate and sustain learning processes and systems. Eight themes emerged from the review and are considered from the perspective of learning programmes as emergent communities of practice. By investigating how these themes play out in the nested programming, the paper contributes to the limited existing body of evidence on learning in large climate change programmes and identifies areas where future efforts might focus.
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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 | 0.018 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".