South African Higher Education 30 Years into Democracy (1994–2024): Challenges, Opportunities, and Future Prospects
Bibliographic record
Abstract
Most sub-Saharan African (SAA) states have massive populations, which bring many social dynamics and challenges for domestic policy in many sectors. This stems from reforms that, in most cases, require governmental intervention. In the education sector, most youth attend government schools in large numbers. Some of the challenges these numbers create include overcrowding, lack of technology, lack of qualified teachers, high student-to-teacher ratios, poverty in some households, and inequality. South Africa has 26 universities, Technical and Vocational Education and Training (TVET) colleges, and private colleges. These institutions, especially those the government runs, typically see a massification of local and international students studying at them. This raises the question of whether it is a population or government failure. This question stems from the fact that 26 public institutions cannot cater to the many students transitioning from secondary to higher education (HE). Using a qualitative research methodology, I contend that the South African government should invest more in HE to solve the transition problem as the population grows with limited university space to accommodate everyone.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".