Challenges in monitoring and evaluation : an opportunity to institutionalize M&E systems
Bibliographic record
Abstract
The objective of the Fifth Monitoring and Evaluation (M&E) Conference was to discuss challenges in institutionalizing M&E systems and using M&E information to support planning and budgeting decisions as well as to enhance government transparency and accountability. The structure of the present publication follows the structure of the fifth M&E conference agenda, which included seven sessions. The first session was devoted to the challenges facing evidence-based decision making: the role of M&E. The goal was to show experiences in which M&E has influenced resource allocation and the modification, strengthening, or elimination of policies or programs. The second session focused on institutional arrangements for M&E systems at the international level, and enabled participants to know, examine, and identify the advantages and difficulties of different institutional arrangements used in government management in Canada, Sri Lanka, Spain, and South Africa. The third session addressed the institutional M&E arrangements in Latin America and discussed experiences in the region, considering the characteristics, advantages, and disadvantages of M&E systems depending on whether they fall under or outside the executive, and how central M&E systems relate to and complement their sectoral counterparts. The special session discussed the achievements and challenges of the Colombian National System of Evaluation of Results-based Management (SINERGIA) in its 15 years of operation. The fifth session analyzed the development of M&E capacities and the alliances between the government, academia, and civil society. The sixth session dealt with the institutional arrangements and policies of M&E systems to ensure quality, access, and use of information. The seventh session of the conference focused on the exchange of information about the experiences and challenges that surround the creation of a national chapter of the M&E Network. Brazil presented their progress in establishing a national network.
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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 | 0.513 | 0.364 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.017 | 0.039 |
| Scholarly communication | 0.046 | 0.053 |
| Open science | 0.007 | 0.039 |
| Research integrity | 0.019 | 0.032 |
| Insufficient payload (model declined to judge) | 0.004 | 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".