The Way Forward for Medical Aid in Dying: Protecting Deliberative Autonomy is Not Enough
Bibliographic record
Abstract
Mainstream public and legal debates, decisions, and norms about medical aid in dying (MAiD) have focused primarily on the deliberative facets of autonomy and have paid far less attention to its social components. As a result, safeguards may fail to properly protect the autonomy of vulnerable persons. After introducing the factual and theoretical background of this problem (Section I), I will explain the distinction between deliberative and social dimensions of autonomy, and why a right to autonomy might entail not only protecting an agent’s decisional capacities, but also certain conditions enabling the realization of such capacities (Section II). In Section III, I will explain that legal and bioethical discourses about autonomy have traditionally focused on its deliberative dimensions. This explains, in part, why one should not be surprised that the judicial interpretation of the right to liberty in the Carter decision focused on deliberative capacities (Section IV) and that the legal and regulatory MAiD frameworks set up by our federal and provincial governments similarly focus on deliberative autonomy (Section V). Section VI outlines an alternative interpretation of the right to autonomy that recognizes the necessity of social resources and proposes a principled way of constraining that right. Section VII maps the kinds of social determinants of autonomy in the context of MAiD that our government should monitor and analyze in order to enact proper safeguards to protect socially vulnerable people in the future.
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 | 0.042 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.077 |
| Scholarly communication | 0.014 | 0.019 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.015 | 0.019 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".