The rainbow bridge metaphor as a tool for developing accessible e-learning practices in higher education
Bibliographic record
Abstract
This paper explores the extent to which existing accessibility metaphors can help to develop our conceptualizations of accessible e-learning practice in higher education and outlines a proposal for a new rainbow bridge metaphor for accessible e-learning practice. The need for a metaphor that reflects in more depth what we are beginning to understand about how to how to bring about that change, who should bring about that change, and what the result of such a change might be is identified. One such metaphor that could help us do this is the metaphor of a rainbow bridge. The stakeholders of accessible e-learning within higher education may understand the rainbow bridge as a useful metaphor in that the colours of the rainbow can represent all the main stakeholders in accessibility; the different views that different people can have of the same rainbow can represent different but related views of accessibility; and crossing the rainbow bridge to higher awareness can represent the awareness that is required in order to develop accessible e-learning practice.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.004 | 0.015 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".