Bibliographic record
Abstract
Euthanasia is generally regarded as killing in order to put an end to the unrelieved pain \nand suffering of a patient. Most terminal diseases are often associated with unrelieved pain \nand suffering, as a result advocates of euthanasia have argued for the legalization of \neuthanasia on the ground of compassion for the patients’ suffering. However advancement \nin medicine has made it possible for modern medicine to reduce pain and suffering to the \nbarest minimum. The questions that arise from this are, given the advancement in \nmedicine; is there any necessity for euthanasia? Is the relief of pain the same as the relief of \nsuffering? Do the physiological treatment of pain and its symptoms treat the psychological \nand emotional effect of pain and suffering? This paper shall attempt to answer these \nquestions and argue that though issues and fears raised by the anti-euthanasia movements \nare very legitimate, the problems are resolvable by a well regulated medical system. I will \nattempt to explain what a well regulated system entails and how it can take care of the \nconcerns of the anti euthanasia movement.
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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 | 0.042 | 0.072 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.047 |
| Scholarly communication | 0.011 | 0.025 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.036 | 0.064 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".