An analysis of assisted dying and the practical implementation thereof in South African criminal law
Bibliographic record
Abstract
This dissertation will examine the legality of assisted dying procedures performed in the Republic of South Africa. This is due to the rising awareness about terminal patients’ dignity and autonomy at the end of their life. The physician’s liability, who assists such a patient to end their life, will be examined and whether there is any legal recourse available will be explored. Comparisons will also be made between other legal systems, including Canada, the Netherlands, Oregon of the United States of America and England and Wales. These jurisdictions have been chosen to provide a wide variety of perspectives and possible alternatives that South Africa should take into consideration should parliament or the courts decide to argue the matter. Other sources are also considered, such as the influence of the history and development of the common law crime of murder, as well as the role the Health Professions Council of South Africa will play. Possibly most importantly, the material criminal law of South Africa is thoroughly studied with all forms of assisted dying in mind. This is to establish what kind of liability, criminal or otherwise, a physician might incur should they decide to assist a patient in these circumstances. Lastly, recommendations are made based on the research done throughout this dissertation, which would ideally assist in any future arguments made on the topic.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.013 | 0.020 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".