Medically assisted dying in Canada and unjust social conditions: a response to Wiebe and Mullin
Bibliographic record
Abstract
In the paper, titled 'Choosing death in unjust conditions: hope, autonomy and harm reduction,' Wiebe and Mullin argue that people living in unjust social conditions are sufficiently autonomous to request medical assistance in dying (MAiD). The ethical issue is that some people may request MAiD primarily because of unjust social conditions, not their illness, disease, disability or decline in capability. It is easily agreed that people living in unjust social conditions can be autonomous. Nevertheless, Wiebe and Mullin fail to appreciate that autonomy is only a necessary condition for MAiD. In addition to autonomy, one must decide that providing assisted dying to a patient because they are living in unjust social conditions is ethical. Central to making this ethical decision is the principle of non-maleficence, famously articulated as 'do no harm.' The authors admit that performing MAiD in response to unjust social circumstances is harmful, but they justify this harmful action by appealing to the principle of harm reduction. A fundamental flaw of their approach is that it relies on the legislative definition of intolerable suffering, which is based on circular reasoning and given that 99.2% of patients that have applied for MAiD satisfied this criterion, it is essentially equivalent to no standard/criterion. Canadian society is struggling with the ethical implications of its permissive MAiD programme, and, fundamental to this debate, will be determining the proper balance between autonomy and non-maleficence for people living in unjust social conditions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".