Participatory Impact Pathways Analysis: A Practical Application of Program Theory in Research-for-Development
Bibliographic record
Abstract
Abstract: The Challenge Program on Water and Food pursues food security and poverty alleviation through the efforts of some 50 research-for-development projects. These involve almost 200 organizations working in nine river basins around the world. An approach was developed to enhance the developmental impact of the program through better impact assessment, to provide a framework for monitoring and evaluation, to permit stakeholders to derive strategic and programmatic lessons for future initiatives, and to provide information that can be used to inform public awareness efforts. The approach makes explicit a project’s program theory by describing its impact pathways in terms of a logic model and network maps. A narrative combines the logic model and the network maps into a single explanatory account and adds to overall plausibility by explaining the steps in the logic model and the key risks and assumptions. Participatory Impact Pathways Analysis is based on concepts related to program theory drawn from the fields of evaluation, organizational learning, and social network analysis.
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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 | 0.054 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 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".