Exploring Euthanasia: A Comparative Legal Analysis of India’s Constitutional Approach and Global Practices
Bibliographic record
Abstract
Objectives: Euthanasia, which is the act of intentionally ending a life to relieve suffering, is still a controversial issue around the world with big legal, moral, and cultural effects. This study looks at the laws around euthanasia in India, with a focus on how they have changed throughout time in the Constitution and the courts. India allows passive euthanasia with tight rules, but it does not allow active euthanasia. The study uses a doctrinal approach and compares India's approach to those of other countries, including as the Netherlands, Belgium, Canada, and the United States, where euthanasia laws are less strict. This article looks at the ethical, legal, and medical issues that come up while trying to put euthanasia legislation into place by looking at important Indian Supreme Court cases including Aruna Shanbaug v. Union of India (2011) and Common Cause v. Union of India (2018). It also looks into the roles of judicial monitoring, medical ethics, and keeping weak people safe. The report calls for a more comprehensive set of laws in India, using the best practices from throughout the world and taking into account India's own social and cultural situation. This study adds to the continuing discussions about euthanasia by recommending a balanced strategy that protects people from possible abuse while also respecting their freedom
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.008 | 0.015 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.006 |
| 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".