Access and politics of higher education for refugees: Comparative contexts from Uganda and Ethiopia
Bibliographic record
Abstract
An intricate mesh of factors hampers students from refugee backgrounds from accessing and having success in higher education (HE). The paper examines HE within a broader framework of refugee education and the future politics of its provision. Much research is done on refugee children and youth in schooling contexts, but less is known about students from refugee backgrounds in HE. According to the United Nations High Commissioner for Refugees (UNHCR), an estimated 65 million people are currently displaced, of whom over 21 million meet refugee status criteria. Nevertheless, only five percent of this group has access to HE. Thus, access to HE and the success of students from refugee backgrounds are central to the discussion on the future of HE. The paper provides a comparative overview of difficulties regarding access to HE for refugee students in Uganda and Ethiopia, highlighting policy and settlement issues in their legislative and political contexts. It also interrogates students’ coping mechanisms, exploring their experiences through interviews. The study uses secondary data, document analysis, and interviews with a total of 30 students from refugee backgrounds, fifteen from Makerere University in Uganda and fifteen from Addis Ababa University in Ethiopia.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| 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".