Bibliographic record
Abstract
In this paper, I critically examine the expansion of Medical Assistance in Dying (MAiD) in Canada through a disability ethics lens. I argue that the dominant narrative framing MAiD as a compassionate, autonomy-enhancing policy obscures the systemic ableism embedded in its implementation. Drawing on lived experiences, legal decisions, and bioethical debates, I demonstrate how disabled individuals are increasingly driven to seek MAiD not due to their medical conditions, but because of structural neglect, poverty, and lack of support. I critique the “harm reduction” justification for MAiD, showing that it perpetuates a eugenic logic that devalues disabled lives. By foregrounding disability ethics, I propose a redefinition of autonomy that recognises interdependence and challenges the biomedical privileging of individualism. This approach exposes the ethical flaws in current MAiD policy and calls for a shift toward anti-ableist, disability-affirming practices in healthcare and bioethics. Ultimately, I argue that without such a shift, MAiD risks becoming a path of least resistance for structurally vulnerable disabled people, rather than a truly autonomous choice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.075 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.006 | 0.013 |
| 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".