Equity in Admissions Policies of Undergraduate Students in Post Democracy in Selected South African Universities
Bibliographic record
Abstract
This paper investigates the policy pathways that inform and regulate student selection and admission at three selected universities in South Africa, namely the University of the Witwatersrand, the University of Cape Town and the University of KwaZulu-Natal. We argue that these universities have progressed a long way in addressing the race problem in their enrolment strategies. However, their main target group remains students from rich or affluent communities, to the exclusion of potentially good students from marginalised groups, particularly those from under-resourced township and rural schools. As a result, their main challenge in the context of formal access to higher education in South Africa has largely shifted from a race problem to one of social class. This is due to an overemphasis on narrow conceptions of merit that cannot be reconciled with equity and social justice concerns. The paper suggests that current notions of merit warrant reconceptualization in order to embrace these missing dimensions. While there is plenty of evidence that most institutions agree on the need to embrace a particular form of affirmative action to address current social imbalances, given the fierce contestation of redress policies within the South African higher education sector, they find it difficult to develop and implement adequate admission strategies in practice.
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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 | 0.008 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.010 | 0.002 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".