South Africa’s Vice Chancellors’ Historical and Future Salary Predictors from 2016 to 2026
Bibliographic record
Abstract
This article aims to create insights concerning the remuneration of executives (also known as vice chancellors (VCs)) in higher education in South Africa. Their remuneration is a trending and contentious topic in the media and literature within the South African context. The motivation for conducting this study is that there are no clear indicators, norms, or standards to measure salaries. Therefore, this study is grounded in agency and institutional theories. Moreover, prior to this study, there were no longitudinal studies in the South African context that have analysed VCs’ salaries, using predictors like student enrolment, return on assets, debt ratio, and revenue. The research design was longitudinal, while the research approach was quantitative. The universities that did not meet the requirements for 2016 to 2023 were excluded from the analysis, which was conducted using Python, version 3.11.7, Python Software Foundation: Wilmington, DE, USA, 2025. Since the data points were small (n = 8), bootstrapping was used to resample 1000 samples. The correlation results showed a significant relationship with the fixed salary, whereas the regression results were not significant. It was found that the VCs’ salary is a larger portion of the fixed salary, and the historical data (2013 to 2016) showed an upward trend; the forecast from 2024 to 2026 showed a flat trend. The forecasts are salient and create insights that will assist remuneration practitioners to budget for VCs’ salaries in order to attract, motivate, and retain them.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".