Bibliographic record
Abstract
This chapter serves as an introduction to the volume. It situates universal design for learning (UDL) historically as a framework and examines how it has come to be explored and embraced by higher education. The chapter first reviews the literature on implementation and use of UDL in post-secondary education, and does so in a way that will avoid all other authors in the volume having to revisit the same basic sources. The second section of the chapter uses the phenomenological data amassed by the author in terms of lived experience as a UDL consultant interacting with various post-secondary institutions—both domestically and internationally—to identify key areas that are likely to become crucial in the coming years. Explicit connections are made to chapters that appear further in the volume and develop some of the themes raised in this introductory chapter. The third and final section of chapter examines the global landscape and discusses differences that may exist in relation to UDL implementation between Global North and Global South.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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 teacher head, 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".