Good Deaths: Perspectives on Dying Well and on Medical Assistance in Dying at Thrangu Monastery Canada
Bibliographic record
Abstract
Anthropological, sociological, and bioethical research suggest that various agencies affect one’s relationship with the dying process and end-of-life decisions. Agencies include the media, medical professionals, culture, and religion. Observing the prevalence of meditations and rituals relating to death at Thrangu Monastery Canada, I wanted to investigate how the latter two agencies in particular, namely culture and religion, impacted the monastery members’ views on the dying process. During 2018 interviews, I asked their opinions on the meaning of dying well, and on Medical Assistance in Dying (MAID), which was legalized in Canada in 2016. Although some scriptural examinations have suggested that voluntary euthanasia is contrary to Buddhist teachings, the majority of the monastery’s respondents support MAID to some degree and in some circumstances. Moral absolutes were not valued as much as autonomy, noninterference, wisdom, and compassion.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.046 | 0.020 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".