Paved Trails: Crip Poetics as an approach towards decolonizing accessibility
Bibliographic record
Abstract
Poetry is a gentle but relentless coach, a lover, personal benchmark, and record for growth. She shifts beliefs, practices, and emotions, tracking pitfalls, steps back, steps around, stillness, like a smooth laketop or slow-streaming river. In this Research-Creation thesis, I develop my version of ‘Crip Poetics’ through autoethnographic methods including video poems and hybrid prose-poetry writing. Drawing on Critical Disability Studies, Indigenous Studies, and Mobility Studies, I bring questions of white supremacy and settler colonialism into conversation with accessibility in Canada. I interview Indigenous people with varying relationships to disability and disabled people of multiple settler cultures, using qualitative methods including Hangout as Method and Wheeling Interviews. Engaging with interview transcripts as text, to continue conversation and exchange (with interviewees), this study offers reflections on interviewing as a method. Reflecting on the limits of participant-action research and representation, I interrogate the role of researchers in marginalized knowledge production, engaging with the limits and possibilities of ‘unsettling research’. I aim to redirect eugenic trends in disability discourse and history towards prioritizing the telling of our own stories. It's my hope that these conversations and the intersections of these struggles are brought to the fore—this thesis being one avenue among many to further this work. Come with me as I play with mainstream, heteronormative, settler framings of dichotomies between accessibility and nature. Dance with me between words and beyond political affiliation, witness my searching for ancestors in the words of an earlier generation of People with Disabilities, on waves actuated by water taxi, towards my interviews.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.024 | 0.062 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".