Bibliographic record
Abstract
New technologies are both making life more efficient and productive, and more complex and precarious. Medical technologies in particular are proving to be a double-edged sword, increasing options and expectations while exciting important ethical concerns. Advances have now extended our life-preserving capability well beyond our healing or vitalityrestoring capability. This reality often results in great infirmity at the end of life, and it means that knowledge and care around end-of-life decisions is essential. This paper examines the consent model in this setting, exploring concepts of ‘capacity’ and ‘best interests’, and the function of Advance Directives, drawing on legislative and judicial authorities from across Canada. In the course of the analysis, it exposes the ethical values which nourish these concepts and practices, and concludes with some practical suggestions for medical law practitioners giving advice in this highly emotive setting.
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 | 0.118 | 0.139 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.136 |
| Scholarly communication | 0.015 | 0.020 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.023 | 0.015 |
| 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".