Enhanced learning from multi‐stakeholder partnerships: Lessons from the Enabling Rural Innovation in Africa programme
Bibliographic record
Abstract
Abstract Despite increasing interest and support for multi‐stakeholder partnerships, empirical applications of participatory evaluation approaches to enhance learning from partnerships are either uncommon or undocumented. This paper draws lessons on the use of participatory self‐reflective approaches that facilitate structured learning on processes and outcomes of partnerships. Such practice is important to building partnerships, because it helps partners understand how they can develop more collaborative and responsive ways of managing partnerships. The paper is based on experience with the Enabling Rural Innovation (ERI) in Africa programme. Results highlight the dynamic process of partnership formation and the key elements that contribute to success. These include: (i) shared vision and complementarity, (ii) consistent support from senior leadership; (iii) evidence of institutional and individual benefits; (iv) investments in human and social capital; (v) joint resources mobilization. However, key challenges require coping with high staff turnover and over‐commitment, conflicting personalities and institutional differences, high transaction costs, and sustaining partnerships with the private business sector. The paper suggests that institutionalizing multi‐stakeholder partnerships requires participatory reflective practices that help structure and enhance learning, and incrementally help in building the capacity of research and development organisations to partner better and ultimately to innovate.
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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 | 0.016 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".