Bibliographic record
Abstract
The emergence of the digital campus, and the rapid convergence of previously disparate methods of communicating information, presents both a risk and an opportunity for people with disabilities. The imminent risk is that non-inclusive design of the digital campus will irreparably widen the digital divide within higher education, to the detriment of learners and educators with disabilities as well as to society as a whole. The opportunity is to use tools and technologies to create more learner-directed, flexible, multi-modal learning environments, thereby reducing barriers and advancing education for all learners. This paper presents the perspectives of two centers of expertise on inclusive teaching and learning: ATRC (the Adaptive Technology Resource Center) at the University of Toronto, and Project EASI (Equal Access to Software and Information), a core activity of the TLT Group, the Teaching, Learning and Technology affiliate of the American Association for Higher Education. Initiatives that reduce barriers and advance educational practice are discussed, and strategies for harnessing the patterns of converging and emerging trends to create a more accessible education environment are
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.000 | 0.009 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.027 | 0.003 |
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".