Legal and Ethical Perspectives on Euthanasia: A Comparative Study of India and Canada
Bibliographic record
Abstract
This comparative study explores the legal and ethical perspectives on euthanasia in India and Canada. Euthanasia, also known as assisted dying, is a complex and controversial issue that raises questions about autonomy, dignity, suffering, and the role of healthcare professionals in end-of-life decision-making. In Canada, euthanasia is legal under certain circumstances, following the Supreme Court's decision in the Carter case in 2015. The Medical Assistance in Dying (MAID) Act came into effect in 2016, which allows eligible patients to receive medical assistance in dying with the supervision of a medical practitioner. However, the law has strict criteria for eligibility, and there is ongoing debate about expanding its scope. In India, euthanasia is illegal under the Indian Penal Code. However, there have been several high-profile cases in recent years that have brought the issue to public attention, such as the Aruna Shanbaug case in 2011. The debate in India has focused on the right to die with dignity and the need for a legal framework that addresses end- of-life care and decision-making. This study compares the legal frameworks and ethical perspectives on euthanasia in both countries, examining the cultural and historical factors that shape attitudes towards end-of-life care. It explores the role of religion, family, and social values in shaping public opinion and policy and considers the impact of globalisation and changing demographics on the debate.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.020 | 0.007 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".