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Record W4389796106 · doi:10.21275/sr231107231117

Exploring the Right to Die with Dignity: A Legal Analysis of Indian Legislation and Global Perspectives

2023· article· en· W4389796106 on OpenAlexaboutno aff
Suhani Gautam

Bibliographic record

VenueInternational Journal of Science and Research (IJSR) · 2023
Typearticle
Languageen
FieldMedicine
TopicPatient Dignity and Privacy
Canadian institutionsnot available
Fundersnot available
KeywordsLegislationDignityRight to diePolitical scienceLawSociology

Abstract

fetched live from OpenAlex

Every human life has value.Every precaution needs to be taken to protect an individual's life.The question of whether someone should be allowed to take their own life is more complex than just logic.The right to die has been a major public discussion point and has gradually gained significance in recent years.The freedom to choose to continue or discontinue medical treatment or a life support system for a terminally ill person, or for someone who has tragically fallen into a comatose or persistent vegetative state, is known as the right to die, or the right to end life in one's own terms. As it should be, active euthanasia is not recognized in India. The Indian Supreme Court has declared that the right to passive euthanasia or to die with dignity is a fundamental right. By legalizing passive euthanasia in 2018, the Hon'ble Supreme Court of India, composed of a Constitutional Bench of five judges, appropriately acknowledged and upheld the right to die with dignity as an essential component of the right to life. Note that the Indian Constitution only specifies the parameters and authority of the right to life inPart III of the Indian Constitution.It is interesting to note that euthanasia is legal and accepted in a number of countries, including the Netherlands, Belgium, Luxembourg, Switzerland, Germany, Japan, Colombia, Albania, and Canada, where it is openly practiced.The author of this paper attempts to analyze the current Indian legislation regarding the right to die with dignity.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.259
Threshold uncertainty score0.329

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.192
GPT teacher head0.439
Teacher spread0.247 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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