Bibliographic record
Abstract
Canadian ethicists Jocelyn Downie and Udo Schuklenk seek to assess the effect of Canada’s decriminalisation of ‘medical assistance in dying’ (MAID) ‘to inform Canada’s ongoing discussions and because other countries will confront the same questions if they contemplate changing their assisted dying law.’1 Their assessment focuses on two arguments earlier levied against expansion of these procedures. The first is that of a ‘slippery slope’ and the second is what they disingenuously call, ‘social determinants of health’. They conclude that, in both cases, the effect of legislation permitting first limited and then expanded options for medical termination has been benign. The argument in this brief commentary is that the ‘slippery slope’ is clearly evident and support for those with chronic, progressive diagnoses a clear contributor to the ever increasing number of persons seeking MAID. Downie and Schuklenk first review the legal and legislative history of MAID. In Carter v Canada (Attorney General), the courts found …
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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 | 0.013 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.024 | 0.019 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.007 | 0.003 |
| Research integrity | 0.059 | 0.071 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".