Funding and inclusion in higher education institutions for students with disabilities
Bibliographic record
Abstract
Background: Historically, challenges faced by students with disabilities (SWDs) in accessing higher education institutions (HEIs) were attributed to limited public funding. The introduction of progressive funding models such as disability scholarships served to widen access to, and participation in, higher education for SWDs. However, recent years have seen these advances threatened by funding cuts and privatisation in higher education.Objectives: In this article, the funding mechanisms of selected developed and developing democratic countries including the United Kingdom, the United States, Canada, Australia, South Africa and India are described in order to gain an insight into how such mechanisms enhance access, equal participation, retention, success and equality of outcome for SWDs. The countries selected are often spoken about as exemplars of best practices in relation to widening access and opportunities for SWDs through government mandated funding mechanisms. Method: A critical literature review of the sample countries’ funding mechanisms governing SWDs in higher education and other relevant government documents; secondary academic literature on disability funding; online sources including University World News, University Affairs, newspaper articles, newsletters, literature from bodies such as the Organisation for Economic Co-operation and Development, Disabled World and Parliamentary Monitoring Group. Data were analysed using a theoretically derived directed qualitative content analysis.Results: Barriers which place SWDs at a substantial educational disadvantage compared to their non-disabled peers include bureaucratisation of application processes, cuts in disability funding, means-test requirements, minimal scholarships for supporting part-time and distance learning for SWDs and inadequate financial support to meet the day-to-day costs that arise as a result of disability.Conclusion: Although the steady increase of SWDs accessing HEIs of the sampled countries have been attributed to supportive disability funding policies, notable is the fact that these students are still confronted by insurmountable disability funding-oriented barriers. Thus, we recommend the need for these HEIs to address these challenges as a matter of urgency if they are to respect the rights of SWDs as well as provide them with an enabling environment to succeed academically.
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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 | 0.016 | 0.073 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.002 | 0.022 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".