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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".