Bibliographic record
Abstract
Medical assistance in dying (MAiD) remains a controversial topic in Canada despite its legalization in 2015. Opponents of MAiD legislation often cite ‘pro-life’ or ‘pro-choice’ arguments which emphasize the value of human life. While all eligible adults are currently able to request MAiD, scholars, citizens, and religious organizations have expressed concerns that women, as a marginalized group, are at risk to request assisted dying due to gendered circumstances rather than personal choice. My research investigates the claim that women’s lives are threatened by MAiD legislation and analyzes the ways in which MAiD is a gendered issue. Drawing from seventeen academic, government, and grey literature sources, I identify and challenge three key discursive categories used to present women as vulnerable under MAiD legislation. I argue that opponents of MAiD legislation co-opt feminist discourses to make normative claims which resonate with the values of individualism in Canadian liberal democratic society. In doing so, opponents of MAiD reproduce the same gender issues they claim to oppose and risk endangering women’s access to MAiD in Canada. I conclude with recommendations relevant to the next stage of MAiD legislation in Canada, which will debate whether other populations considered to be vulnerable, including mature minors and people with mental illness, will have access to MAiD.
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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.044 | 0.024 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.007 |
| 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".