Evaluation of Results-Based Management in CGIAR
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it