A Critical Analysis of 10 Financial Sustainability Indicators Applicable to Public Universities in South Africa
Bibliographic record
Abstract
I ideated this paper from a realisation that no coherent approach has yet arisen in the analysis of financial sustainability for public universities, particularly in South Africa. The paper originates from a study conducted amongst the 26 public universities in South Africa. The study follows a secondary data analysis approach whereby I analysed annual financial statements of the 26 public universities over the period 2015–2020. I calculated and scrutinised 10 financial sustainability indicators for each of the 26 universities. The main research objective was to determine the impact of funding sources on the financial sustainability of these institutions. Additionally, I determined the impact of university size, location, historic roots and university type on the financial sustainability of the 26 public universities. The findings of the study reveal that government funding, supported by a diverse range of funding sources, plays a positive significant impact on the financial sustainability of South Africa’s public universities.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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".