Building Bridges to an Uncertain Future Lived Now: Lessons from the Use of Participatory Action Research and Theory of Change Towards A Realistic Community-Based Participatory Monitoring and Evaluation System
Bibliographic record
Abstract
Building on experience from the CGIAR Research Program on Aquatic Agricultural Systems implemented by WorldFish in the Visayas and Mindanao regions of the Philippines, known as the VisMin Hub, we describe the development and evolution of a monitoring and evaluation (M&E) system emerging from the facilitated action-reflection cycles of testing and adopting theories of change carried out with community partners through participatory action research (PAR). The former guides our community partners and us, as members of the potentially emergent PAR groups, towards the realization of the community’s vision; the latter facilitates learning to understand what, how and why change is unfolding. Unlike the conventional M&E system where indicators are pre-set at the beginning of program implementation, these processes result in an organically-evolved, communitybased participatory M&E system that is continuously revised according to contexts to guide communities towards realizing their visions. Its ultimate outcome is enhanced people’s capacity to own the product and process, giving rise to an internally-driven change. Towards the end, the paper offers an iterative discussion of learnings from implementing such an approach.
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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 | 0.087 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.019 | 0.077 |
| Scholarly communication | 0.025 | 0.029 |
| Open science | 0.008 | 0.022 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 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".