Strategies to Improve Capacity for Policy Monitoring and Evaluation in the Public Sector
Bibliographic record
Abstract
Scholars around the globe have contested the inadequate infrastructure and tools used by public sector agencies to monitor and evaluate public policies and programmes.Some of the urgent issues of concern deal with the inadequate human capacity in public agencies and departments to conduct fair and credible evaluations in the public sector.South Africa is not the only country that has adopted a government wide monitoring and evaluation system, other countries like Ghana, Kenya, Benin and Uganda have also endorsed formal monitoring and evaluation practice in the public sector.This article argues that monitoring and evaluation must not just measure the effectiveness and efficiency of public programmes and processes, but it must create a sustainable process whereby participants and evaluators can learn from the process.Capacity building in monitoring and evaluation must be fairly and continuous conducted to offer credible and valid information and knowledge on M&E by training agencies and institutions like universities.This theoretical paper adopted document analysis strategy to review and evaluate documents used as data source.Lessons learnt from this article contribute towards the existing strategies to enhance monitoring and evaluation.
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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 | 0.524 | 0.553 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.036 | 0.023 |
| Science and technology studies | 0.008 | 0.021 |
| Scholarly communication | 0.039 | 0.056 |
| Open science | 0.013 | 0.032 |
| Research integrity | 0.013 | 0.019 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".