Monitoring and Evaluation Preparedness of Public Sector Institutions in South Africa
Bibliographic record
Abstract
In a bid to improve service delivery in South Africa, the government has created a government-wide \nmonitoring and evaluation (M&E) system that would help gauge performance across all spheres of government. This has \ncompelled public sector institutions to adopt and implement M&E systems mandatorily, even when they are not \nnecessarily ready for it. The unpreparedness inevitably perforates the ability of M&E systems to credibly support \nperformance improvement in public sector institutions and it is problematic. To some extent, the practice of M&E in the \npublic sector seems to be for purposes of compliance rather than the ideal of performance improvement. This qualitative \nstudy investigates the readiness of South African public sector institutions for M&E, through the perspectives of \nManagers primarily in the M&E space. Findings reveal mixed signals of M&E readiness. For instance, the factors \nmotivating the creation of the M&E system and the calibre of staff championing it, seem to suggest M&E readiness. \nConversely, the non-availability of capacity to support the system and the potential response of staff to negative \ninformation generated by M&E signal non-readiness. The import of this is that readiness assessments specific to \ninstitutions have to be conducted as a basis for determining areas where the prerequisites for M&E are lacking. This \nshould then inform remedial efforts that ultimately help to improve the potency of the M&E system.
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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 | 0.011 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".