Monitoring, evaluation and learning requirements for climate-resilient development pathways
Bibliographic record
Abstract
For today’s decisions to be sustainable, they need to include choices and actions that reduce poverty and improve livelihoods, counteract climate change and are equitable towards the vulnerable. Climate-resilient development pathways are a practice that aims to achieve these goals, enabling decision-makers to identify, consolidate and implement climate action and development decisions towards sustainable development. To date, there is little evidence regarding how the practice can be navigated in real-world situations. Guidance on monitoring, evaluating and learning from experience specifically for climate-resilient development pathways is largely lacking. For this article, we reviewed the literature and held reflexive sessions with experts, synthesising different perspectives to present seven process-based monitoring, evaluation and learning requirements for climate-resilient development pathways. We close with discussing the applicability of the requirements and where further research is needed. In doing so, we address an important but underrepresented topic in the expanding body of literature on climate-resilient development pathways.
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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 | 0.179 | 0.264 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".