Bibliographic record
Abstract
When and how forcefully must we intervene to save a life, and when should we respect the will to die? This book presents alternative ethical paradigms to understand contemporary challenges in suicide research, prevention, practices, and policies, including challenges in the expanding legalization of euthanasia and assisted suicide ('medical assistance in dying'). Drawing on case studies and philosophical approaches, analysis focuses on decision-making when we are faced with questions about obligations to help and intervene in suicidal situations. Chapters cover moral dilemmas in rescue policies, ethical challenges in suicide research, civil and legal considerations, and similarities and differences with accessing medical assistance in dying. Discussion is grounded in contemporary debates, addressing important issues such as if we should continue to hospitalize people to protect them from self-harm, or control access to 'dangerous' suicide content online? This book is unique in its focus on the practical concerns of mental health professionals, helplines, researchers, policy makers, and programme planners who are faced with ethical challenges in suicidology and suicide prevention.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.025 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".