Evaluating Capacity Development in Planning, Monitoring, and Evaluation: A Case from Agricultural Research
Bibliographic record
Abstract
Capacity development has moved to center stage on the agendas of development organizations. As technologies and institutions are changing fast and budgets for overseas development assistance are declining, strengthening the capabilities of individuals, organizations, and institutions is essential to ensure that development efforts are sustainable and poverty is eradicated. Substantial sums are being invested in the development of organizational and institutional capacities. Yet, the design and management of capacity development efforts leaves much to be desired. Few capacity development programs have been systematically and thoroughly evaluated to test their underlying theories and assumptions, document their results, and draw lessons for improving future programs. This report describes the concepts and methods used to evaluate a regional capacity development project in Latin America. The project under study aims to strengthen planning, monitoring, and evaluation (PM&E) in agricultural research organizations in the region. The report outlines the procedures employed in five evaluation studies and summarizes the results of each study. It then presents consolidated findings in response to three evaluation questions: What were the main contributions of the project to agricultural research management? What lessons can be learned to improve the design of future capacity development programs? What lessons can be learned to improve future evaluations of capacity development?
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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 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.007 | 0.016 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".