A MULTIDISCIPLINARY LEARNING EXPERIENCE FOR EDUCATION IN ACCESSIBILITY
Bibliographic record
Abstract
Abstract – This paper argues that accessibility is both a complex issue and one in which engineering students would deeply benefit from learning more about due to mounting legislation and demand. Engineers can play an important role in creating a more inclusive society, yet questions remain as to how to instill knowledge of accessibility effectively in the classroom. 
 To answer these questions, the authors explore and draw preliminary conclusions from a multidisciplinary learning experience they designed by which two upper level undergraduate courses from engineering and history were joined on separate occasions to provide some education in accessibility.
 Although crafting and delivering this experience posed some challenges, the authors believe that this multidisciplinary approach generally enriched the learning experience for students, exposing them to a broader perspective, and in particular the historical context for accessibility initiatives and the lived experience of people with disabilities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.008 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.029 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.017 | 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".