“Student Disadvantage”: Key University Stakeholders’ Perspectives in South Africa
Bibliographic record
Abstract
Universities in South Africa seem to be struggling to create inclusive conditions for black students to succeed in their studies. The persistence of inequality in universities could be partly blamed on the use of the term ‘historically disadvantaged’, which is not defined in policy documents, and this has resulted in universities being unclear on what exactly to address in their transformation. Using the capability approach in this study, it is argued that policy should address the structural, institutional and environmental factors that contribute to student disadvantage, which prevent the development of opportunities and agency among students. Seven semi-structured interviews were conducted to collect qualitative data from key stakeholders who dealt with student affairs (university staff and student representative council [SRC] members) at one South African university with the aim of developing an understanding of student disadvantage from their perspective. The findings revealed that student disadvantage manifests through structural and institutional factors, namely a culture of racism, alienating university campuses, student poverty, university teaching, and gender inequality. The study recommends that universities consider addressing these factors in their transformation.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.025 | 0.009 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.015 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".