Opportunities and Challenges for Professionalizing Monitoring and Evaluation Practice: A Global Overview and Perspectives
Bibliographic record
Abstract
Organizations and countries have, in the last one decade or so, been undertaking different efforts and strategies to professionalize monitoring and evaluation as a strategy to improve the quality of its services, and to defend and protect the welfare of commissioners and professionals. This article provides the global trend of professionalization of the field of monitoring and evaluation. The article has generally shown that professionalization of monitoring and evaluation is a necessary strategy for improving the quality of services and products and wider monitoring and evaluation. The adoption of professionalization is determined by a number of factors; and particularly political will, institutionalization, level of maturity of monitoring and evaluation associations and networks, results-based culture, and the maturity of capacity building programmes. Comparatively, North American countries have recorded tremendous achievements in the professionalization of monitoring and evaluation; with Canada reaching the stage of accreditation. Generally in Africa, South Africa, and to a certain extent Ghana, have recorded significant achievements in professionalizing monitoring and evaluation due to the factors just mentioned. In East Africa, the level of monitoring and evaluation professionalization and institutionalization is relatively higher in Uganda and Kenya compared to Tanzania where, despite a few obstacles, there has been a combined effort from the government, TANEA, members of parliament and training institutions to professionalize and institutionalize monitoring and evaluation. Substantial achievement has been made, including -- but not limited to -- improvement of lobbying and support from both the parliament and the president’s office, increased number of short- and long-term training programmes and atheist partial institutionalization of monitoring and evaluation within the government systems.
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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 | 0.046 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".