Teaching at the Intersection of Disability, Race, and Gender: Theorizing the Disability Studies Classroom
Bibliographic record
Abstract
Given the critiques by many Black, Indigenous, and People of Colour (BIPOC) scholars who argue that Disability Studies is really White Disability Studies, this dissertation explores the challenges of teaching critical Disability Studies at the undergraduate level. At the heart of the challenge of teaching Disability Studies is the conflict between disability scholars, some of whom argue against politics of desirability, pointing to the disabling/debilitating processes that make rights-based analyses inadequate. While Canadian university institutions use discourses of Diversity, Equity, and Inclusion, and claim to follow state recommendations for accessibility and reconciliation, universities themselves still often are disabling. Indigenous and Black students, facility, and staff still experience inaccessible study and work spaces, including carceral logics that represent Indigenous and Black knowledges as inherently intellectually inferior. I argue that these logics are not separate from ableist practices that limit disabled participation in university spaces; disability must be examined through an intersectional—and explicitly race-based—lens. Using Critical Discourse Analysis (CDA), this dissertation takes on the problem of teaching Disability Studies in two parts: the first part gives a more theoretical examination of the conflicts within Disability Studies, the problems of accessibility/reconciliation according to university Teaching and Learning websites, and the accessibility issue of anti-Blackness in university. The second part aims to give a more pragmatic and practical examination of the same issues, pointing to a failure-based self-reflexive classroom, and giving two mock assignments for educators and students to consider their place in ableist white supremacist institutions.
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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 | 0.016 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.020 | 0.100 |
| Scholarly communication | 0.016 | 0.019 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.004 | 0.007 |
| 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".