Developing an Ecological Approach to the Strategic Implementation of UDL in Higher Education
Bibliographic record
Abstract
This paper argues that, as Canadian Higher Education campuses embark on large scale Universal Design for Learning (UDL) implementation, it is essential for them to take the time to strategically consider inherent institutional challenges before pushing ahead. As a result, it is argued that ecological theory will represent a unique and powerful lens in this process of implementation. The first section of the paper examines two inherent dangers being perpetuated in current UDL drives on the vast majority of Canadian campuses that have embarked on this adventure: (i) overreliance on disability service providers, and (ii) a conceptualization of UDL work in silos. The second half of the paper focuses on solutions, and on the idea of developing a strategic approach to UDL integration framed around ecological theory. The paper draws on an analysis of phenomenological data emerging from the author’s own lived experience as a consultant responding regularly to the needs of post-secondary campuses with regards to the institutional adoption of UDL.
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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 | 0.019 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.019 | 0.055 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.002 | 0.003 |
| 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 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".