Bibliographic record
Abstract
Academic accommodations have become quite commonplace in universities in the Global North. At their best, accommodations support the rights of all students to an education, enabling students with disabilities or those who learn differently to succeed in the university and beyond. But are accommodations truly at their best? Reflecting on his own experiences as a Black student with a disability as well as the experiences of other Black students accessing accommodations at Canada’s premier university, the University of Toronto, Baker examines how Black students who self-identify as having a disability navigate the everyday complexities of Blackness and disability in Canadian higher education. Revealing the often invisible ways Black disabled students negotiate the double bind of disability and anti-Blackness, this book draws attention to the alarming regularity with which students internalize stigmas born from structural forms of anti-Black racism and ableism and demonstrates how this often create devastating barriers to student success and well-being. Timely, thought-provoking, and at times deeply personal, this book encourages us to rethink the accommodations process with the aim of supporting all students to achieve success within the academy and beyond.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.027 |
| Scholarly communication | 0.018 | 0.022 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".