“I’m Not Asking for Special Treatment, I’m Asking for Access”: Experiences of University Students with Disabilities in Ghana, Ethiopia and South Africa
Bibliographic record
Abstract
It is often challenging for youth with disabilities to access university education in Africa, and for those who manage to make it to university, while there, their experiences are still not barrier-free. The purpose of this study was to uncover the experiences of the barriers and facilitators to inclusion for youth with disabilities in universities in South Africa, Ghana and Ethiopia. This qualitative project applied a critical, participatory research approach to exploring youth experiences. Youth with disabilities and their colleagues conducted seven focus group discussions, with an average of five students in each focus group, and we used a qualitative descriptive method to analyze data. The findings uncovered similarities and differences in the barriers and facilitators to inclusive education among students with disabilities across all sites. Participants noted limited financial support and university services, and how inaccessible spaces and harmful attitudes are all barriers that hinder their access to education and inclusion. The identified facilitators include support systems and self-directed facilitation. Although the students self-advocated and reported some support to assist in their inclusion in university, it was still insufficient. Notably, some universities are making a concerted effort toward inclusion and accessibility, but more work needs to be done.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.010 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| 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".