A Duoethnography on Disability and Allyship Within a Vision Science Doctoral Program: Perspectives on Inclusion, Diversity, Equity, and Accessibility
Bibliographic record
Abstract
People with visual impairments (those who are blind or who have low vision) continue to experience an unemployment rate of 70% or higher across all sectors but remain especially under-represented within higher education and the research ecosystem. Among the barriers emphasized by people with visual impairments are those related to accessibility and inclusion. It is within this socio-historical context that we began our interactions as a blind graduate student (Martiniello) and a sighted PhD. supervisor (Wittich) in the process of completing a doctoral program in vision science. Utilizing duoethnography as a methodological approach, we juxtapose two perspectives on a shared experience. Over a period of five years, we explored the ways in which our interactions as a trainee with lived experience and sighted ally have shaped our perspectives on disability inclusion in (and while doing) disability research and the role of allyship in the context of academia. We use examples from our lived experiences to illustrate the ways in which we negotiated the role of allyship throughout the research process, including the impact of accessibility and inclusion while completing a scoping review, semi-structured interviews and thematic content analysis. These collective experiences set the stage for new forms of advocacy and allyship to emerge. To the best of our knowledge, Dr Martiniello remains the first and only blind person to graduate in Canada with a PhD in Vision Science. Hopefully, she will not be the last.
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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 | 0.030 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".