Evaluation of Results-Based Management in CGIAR
Bibliographic record
Abstract
IEA conducted a System-wide Evaluation of Results Based Management to learn lessons from the experience of introducing and implementing different aspects of RBM in CGIAR. The objectives of the Evaluation were to provide evidence and lessons and recommendations as an input to implementing an RBM framework for CGIAR Research Programs (CRPs), and for increasing the relevance, efficiency, and effectiveness of further RBM iterations. On the basis of international experiences, the evaluation team formulated ten good practice principles for RBM applicable to CGIAR’s context and proposed a Theory of Change for RBM in CGIAR (presented below). Main Findings The Evaluation found that CGIAR lacked a shared conceptual understanding of RBM. At System-level, CGIAR saw RBM mainly in relation to the SRF and results-based reporting to donors; while Centers and CRPs sought to develop performance management systems for their own purposes, and for complex research programs. As a result, there has been confusion about the purpose of RBM for CGIAR. In addition, insufficient consideration was given to the fact that CGIAR is a research for development organization with a mandate to deliver research results. The Evaluation found, however, that many Centers have embraced their own RBM approaches over the years. Following the CGIAR reform, Centers and CRPs have shown significant progress in developing their RBM-related processes, tools, and methods, and a nascent culture shift has taken place towards performance management. Some are notably providing leadership from below to be applied at System level.
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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 | 0.566 | 0.501 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.018 | 0.010 |
| Open science | 0.009 | 0.015 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".