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 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 | 0.379 | 0.315 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.025 | 0.021 |
| Science and technology studies | 0.006 | 0.030 |
| Scholarly communication | 0.028 | 0.028 |
| Open science | 0.011 | 0.026 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".