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Record W2319278231 · doi:10.1086/ahr.111.3.807

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

2006· article· en· W2319278231 on OpenAlexaboutno aff
Roger Lane

Bibliographic record

VenueThe American Historical Review · 2006
Typearticle
Languageen
FieldSocial Sciences
TopicAmerican Constitutional Law and Politics
Canadian institutionsnot available
Fundersnot available
KeywordsPopulationLawHistoryPolitical scienceClassicsSociologyEnvironmental ethicsPhilosophyDemography

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.393
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.005
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.047
GPT teacher head0.336
Teacher spread0.289 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations0
Published2006
Admission routes1
Has abstractyes

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