Bibliographic record
Abstract
On June 17th 2016 Canada adopted a law that created regulatory framework for medical assistance in dying. In the middle of September 2016 in Belgium was performed the first child euthanasia. With these events 2016 has ended with ongoing classical debate concerning euthanasia and physician-assisted suicide. At the beginning of 2017 the world started talking about euthanasia once again but this time the debate took an unexpected turn. In January 2017 a citizen’s initiative in Finland submitted 50,000 signatures in the Finnish Parliament (Eduskunta) placing the debate in political field. There were politicians who opposed these ideas. Finnish doctors who are concerned with palliative treatment state that better training in palliative care would decrease all requests for active euthanasia and would improve palliative care services. All of this raises the question “Who has the right to justify euthanasia – physicians, politicians, society or patients?” It seems that the debate has left the field of bioethics and has entered the field of public health ethics. The aim of this report is to reflect on euthanasia as a public health problem.
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 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 | 0.024 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.047 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.023 | 0.019 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".