Bibliographic record
Abstract
The article investigates that due to the development of scientific and technological progress human rights are becoming increasingly relevant. Today, a new dimension of human rights is emerging - the fourth generation of human rights, providing for the human right to gender reassignment, organ transplantation, same-sex marriage, euthanasia, and so on. The problem of euthanasia attracts special attention of scientists. For the theory of law, the problem of euthanasia and its relationship with the law are of particular interest. In legal science, scholars have studied various aspects of the legalization of euthanasia, including countries such as Austria, Azerbaijan, Belgium, Greece, Georgia, Denmark, India, Iceland, Spain, Italy, the Netherlands, Germany, Poland, Portugal, Switzerland and Japan, but it seems is little studied is euthanasia in the legal systems of the United States and Canada. Today in the United States, the legal regulation of euthanasia is determined by the federal territorial structure of the state, where states can establish their own laws, this also applies to euthanasia. Euthanasia is allowed in 9 states, including Washington, Vermont, Hawaii, California, Colorado, Maine, Montana, New Jersey, Oregon and the District of Columbia. In Canada, a prerequisite for acceptance. The Law "On Amendments to the Criminal Code and Amendments to Other Laws (Regarding Medical Care at Death)" became the case of Carter v. Canada. The Law “On Amendments to the Criminal Code and Appropriate Amendments to Other Laws (Concerning Medical Care at Death)” at the federal level establishes the basis for the provision of medical care at death throughout the country. As for the provinces and territories of Canada, they have the power to legislate on death care, provided that they do not conflict with federal law. Most provinces and territories have adopted guidelines and policies for death care. Quebec is the only province that has passed a law, which regulates medical care at death.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".