Engaging ignored stakeholders of higher education accessibility practice: analysing the experiences of an international network of practitioners and researchers
Bibliographic record
Abstract
Purpose The purpose of this paper is to examine and critique current approaches of higher education (HE) community concerning stakeholder engagement in the development of information and communications technology (ICT) related accessibility practice. Design/methodology/approach The approach taken to this examination is to draw on presentations, panel discussions and World Café reflections from an international symposium held in Montreal where researchers and practitioners debated two key questions as follows: have all the relevant stakeholders really been identified? Are there some stakeholders that the HE community has ignored? And what factors influence successfully distributed ownership of the accessibility mission within HE institutions? Findings A number of “new” internal and external stakeholders are identified and it is argued that if they are to be successfully engaged, effort needs to be invested in addressing power imbalances and developing opportunities for successful strategic silo-crossing. Originality/value The value of this paper is in critiquing the argument that all stakeholders in the development of accessible ICT in HE need to be involved, identifying a gap in the argument with respect to whether all relevant stakeholders have actually been engaged and offering insights into this omission might be rectified.
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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 | 0.047 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.025 | 0.021 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.003 | 0.025 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".