Equality in higher education opportunities: Practitioners’ perspectives from global, rural, post-colonial disability
Bibliographic record
Abstract
This paper gathers practitioner perspectives on tuition-free online courses and their potential to improve equality in higher education. Through an intersectional lens of race, gender, income, and indigeneity, this paper focuses on the experience of people living with disabilities (PLWD) as a further marginalized sub-population within diverse marginalized populations. Of note, disability-knowledge held by PLWD and by their family members can position them as sensitive and effective healthcare or disability-care providers. At the same time, society often does not grant an easy pathway to this education and licensure. The existing landscape of massive open online courses (MOOCs) may present tuition-free learning, but accreditation can rest upon payment and other complex structures. Even after PLWDs gather financial resources for official accreditation, prospective employers have the autonomy to determine whether this learning is valid. In a global context, low-income families may experience internal competition for financing between PLWD and non-disabled siblings. Securing a future in which payment models and disability-needs are accommodated for in MOOCs can alter multiple life trajectories in the families of PLWD and ensure that the intersectionally marginalized may equally benefit from open education.
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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 | 0.017 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.024 | 0.021 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.001 | 0.019 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 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".