The Challenge of Inequality in University Education: Is the District Quota Scheme Addressing Socio-Economic Disparities in Access to University Education in Uganda?
Bibliographic record
Abstract
Access to university education is one of the fundamental educational questions in contemporary educational debates. This is because university education is seen as having an array of benefits to individuals, their households, and their nations. However, the challenge of inequality in terms of gender, income, location, and socio-economic status has constrained some individuals and households to access quality university education. In 2005 the government of Uganda introduced the District Quota Scheme to address the social inequalities in accessing university education. This study examined how the District Quota Scheme is addressing the rural-urban divide in access to university; how the District Quota Scheme has increased access to university education for children with parents who have low levels of education; and whether the District Quota Scheme is improving access to university education for children from low-income families. Following the social constructivist research paradigm and integrating both quantitative and qualitative research methods, the study found a change in access to university education by students from rural areas, students whose parents have lower levels of education, and those from low-income families as a result of introducing the District Quota Scheme. The study recommends that the government of Uganda and other stakeholders in the higher education sector should address the structural challenges to ensure that mainly the socially disadvantaged students take the biggest advantage of this scheme.
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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 | 0.010 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".