Public Deliberation for Ethically Complex Policies: The Case of Medical Assistance in Dying in Canada
Bibliographic record
Abstract
Almost 50,000 people in Canada have had a medically assisted death since federal legislation was passed in 2016. Still, the debate about the permissibility of medical assistance in dying (MAiD) continues to rage. The central role of shared values and ethics in public policy making emphasizes the importance of engaging the public, particularly around heavily value-laden issues such as MAiD. Public deliberation, a mode of engagement that fosters sustained and reasoned discussion between participants, is well-suited to addressing such ethically contentious policy issues. In this paper, we review recent efforts to engage the public on assisted dying within and outside Canada and explain how public deliberation could contribute substantively to MAiD policy making.
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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 | 0.031 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.066 | 0.040 |
| Scholarly communication | 0.019 | 0.005 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.014 | 0.016 |
| 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".