Work-integrated learning for students with disabilities: time for meaningful change
Bibliographic record
Abstract
The global push towards widening participation for equity cohorts, including students with disabilities, is promising, but it is yet to translate into improved employment experiences. In this commentary, we highlight what higher education institutions must now do to drive meaningful change and better support students with disabilities’ workforce transitions. In doing so, we advocate for a much-needed change towards inclusive work-integrated learning practices that enable students with disabilities to leverage these opportunities to trial career pathways, build networks, and develop their future career goals. Necessary in this is the adoption of co-designed work-integrated learning, that brings together students with disabilities, industry and community, and academics to ensure non-ableist inclusive practices and a culture which understands the strength in diversity.
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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 | 0.029 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.019 |
| Scholarly communication | 0.017 | 0.025 |
| Open science | 0.005 | 0.015 |
| Research integrity | 0.032 | 0.055 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".