Results-Based Monitoring and Evaluation and Knowledge Management Approaches in Government to Government Partnerships: The Case of the Shandong and WCG Partnership
Bibliographic record
Abstract
Government to Government (G2G) partnerships between countries in the BRICS partnerships have significantly increased and with it, the need for more effective evidence-based decision-making. In this process, improved M&E and KM has become prominent. In this context, the study investigated the need for Monitoring & Evaluation (M&E), as well as knowledge management (KM) systems in partnerships. This study focused on the development management aspects of such partnerships and the article is based on research information obtained through the PhD study by Dr Ivy Chen as well as updated research perspectives. The article concluded that a need existed to establish more advanced M&E and KM systems in G2G partnerships. The Readiness Assessment conducted regarding M&E showed that a need existed for Results-Based M&E that can be used to ensure evidence-based decision-making in the G2G partnerships. The Readiness Assessment showed that a definite need existed for Communities of Practice (COPs) beyond the formal meetings and that professionals and practitioners on both sides needed to exchange explicit and implicit knowledge. A need also existed for improved ICTs based-systems including dedicated portals where policy documentation, programme information and data, as well as M&E results, can be loaded and shared by Governments.
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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 | 0.059 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.012 | 0.012 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".