Monitoring and Evaluating Social Learning: A Framework for Cross-Initiative Application
Bibliographic record
Abstract
The Climate Change and Social Learning Initiative is a cross-organisation group working to build a body of evidence on how social learning methodologies and approaches contribute towards development targets. Together with a select number of participating initiatives from a variety of organisations, we are working towards establishing a common monitoring and evaluation (M&E) framework for new projects and programmes using a social learning-oriented approach. The aim is to more systematically collect evidence, analyse results and share learning on when and how research initiatives and beneficiaries may benefit from a social learning-oriented approach in the context of climate change adaptation and food security. This working paper presents an M&E framework consisting of a theory of change and 30 primary indicators across four key areas: iterative learning, capacity development, engagement, and challenging institutions. This framework will be accompanied by a forthcoming implementation guide for participating initiatives, as well as a strategy for peer assist, data collection and analysis by the CCSL Initiative.
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How this classification was reachedexpand
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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".