IAN DOWBIGGIN. A Concise History of Euthanasia: Life, Death, God, and Medicine. (Critical Issues in History.) Lanham, Md.: Rowman and Littlefield. 2005. Pp. vii, 163. $22.95
Bibliographic record
Abstract
Ian Dowbiggin here covers much the same ground as he does in his more extended and scholarly A Merciful End: The Euthanasia Movement in Modern America (2003). This book, as part of Rowman and Littlefield's “Critical Issues in History,” certainly lives up to the first two words in that series' title. The author is an avowed partisan as an outspoken radio and television commentator and an active member of Canada's Pro-Life movement. His book is clearly intended to influence the debate that will surely intensify in the United States. Academic readers may find little new in this study, largely confined to the Western world, but despite its bias and certain flaws it does serve as a useful, clearly written primer on an issue of increasing import. “Euthanasia” in Greek means simply “good death,” and in this original sense it seems ever harder to achieve naturally, as an aging population, increasingly prone to illnesses such as cancer, inevitably multiplies demands to short-circuit the pain. But while this demographic fact, plus advances in medical technology, have made assisted suicide an issue as never before, Dowbiggin amply demonstrates his second most important point (following only the need to condemn it on moral grounds), which is that most of the debating points are ancient ones, and that history is essential to understanding them.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.028 | 0.013 |
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".