Monitoring, evaluation and learning requirements for climate-resilient development pathways
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
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.
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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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