Assisted dying for prison populations: Lessons from and for abroad
Bibliographic record
Abstract
Canadian federal legislation setting out the framework for medical assistance in dying (MAiD) in Canada came into effect in June 2016. Because of section 86(1) of the Corrections and Conditional Release Act, as soon as MAiD became available in the community, it also needed to be made available to federal prisoners. There are some good reasons to be concerned about MAiD in the Canadian corrections system based on logistical, legal, and moral considerations. Fortunately, Canada is not the first country to decriminalize assisted dying and so Canadian policies and practices can be compared to others and take some lessons from their experiences. Thus, by reviewing the legal status of assisted dying in prisons internationally, the regulation of assisted dying, demand for assisted dying from prisoners, and the process for prisoners accessing assisted dying, this article offers a comparative overview of assisted dying for prisoners around the world in an effort to inform Canadian and other jurisdictions’ law, policy, and practice.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.014 | 0.012 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 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".