A Conversation on Feminism, Ableism, and Medical Assistance in Dying
Bibliographic record
Abstract
This article explores the recent expansion of Medical Assistance in Dying (MAiD) in Canada and its negative implications for women with disabilities. In 2021, the government extended MAiD to people with disabilities who are not dying, which the authors contend is a modern form of eugenics. Structured as a conversation and deploying a systemic, equality-based feminist analysis, the article tracks the shifts in scope and justification for MAiD through judicial and legislative developments, the overwhelming opposition by organizations representing people with disabilities, and the failure of feminist organizations to support their disabled sisters. The authors articulate a feminist response to the expansion of MAiD to address this troubling silence. After Isabel Grant sets out the foundations of Track 2 MAiD, Janine Benedet develops a critique of the concepts of autonomy, choice, and privacy as used by MAiD expansionists to justify these premature deaths. Elizabeth Sheehy explores some of the structural issues that affect the impetus for MAiD: women’s poverty, the medical profession, the gendered nature of caregiving, and men’s violence. Isabel Grant demonstrates the particular dangers for women of the extension of MAiD on the basis of mental illness, as evidenced by data from other countries. Catherine Frazee describes what a truly intersectional feminist approach to MAiD demands of more privileged feminists and concludes the conversation with a call for feminist solidarity.
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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 | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.042 | 0.073 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.005 | 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".