Bibliographic record
Abstract
Introduction Part I: Physician-Assisted Suicide, Euthanasia, and the Law Chapter 1: Washington v. Glucksberg US Supreme Court Chapter 2: Vacco v. Quill US Supreme Court Chapter 3: Sue Rodriguez v. British Columbia Canadian Supreme Court Chapter 4: Judge Noble's Ruling Part II: The Slippery Slope Argument and Nonvoluntary Euthanasia Chapter 5: Excerpts from the Nuffield Council Report on Critical Care Decisions in Fetal and Neonatal Medicine Chapter 6: The Groningen Protocol -- Euthanasia in Severely III Newborns Eduard Verhagen and Pieter J.J. Sauer Chapter 7: Life, Death and Slippery Slopes John Woods Chapter 8: Voluntary and Nonvoluntary Euthanasia: Is There Really a Slippery Slope? Michael Sting! Part III: Individual and Social Aspects of Voluntary and Nonvoluntary Euthanasia Chapter 9: Robert Latimer's Choice Bryson Brown Chapter 10: Hard End of Life Decisions for Physicians and Family Members John Baker Chapter 11: Feminist Reflections on Tracy Latimer and Sue Rodriguez Kira Tomsons and Susan Sherwin Part IV: Assisted Suicide, Voluntary Euthanasia and Palliative Care Chapter 12: Attitudes of People with Disabilities toward Physician-Assisted Suicide Legislation: Broadening the Dialogue Pamela Fadem, Meredith Minkler, Martha Perry, Klaus Blum, Leroy F. Moore Jr., Judi Rogers, and Lee Williams Chapter 13: Oregon's Experience: Evaluating the Record Ronald A. Lindsay Chapter 14: Palliative Sedation: An Essential Place for Clinical Excellence Philip C. Higgins and Terry Altilio.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.023 | 0.001 |
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".