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Evolutionary Ethics in the Twentieth Century: Julian Sorell Huxley and George Gaylord Simpson

2010· book-chapter· en· W362239202 on OpenAlexaff
Michael Ruse

Bibliographic record

VenueCambridge University Press eBooks · 2010
Typebook-chapter
Languageen
FieldArts and Humanities
TopicEvolution and Science Education
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsGeorge (robot)HistoryArt history

Abstract

fetched live from OpenAlex

If Julian Huxley is the Herbert Spencer of twentieth-century Darwinism, George Gaylord Simpson may in some ways be considered its Thomas Henry Huxley. Greene (1981, p. 168) Philosophers usually think that evolutionary ethics – the attempt to locate and ground morality in our biological origins – met its Waterloo in the crucial year 1903. It was then that the English philosopher G. E. Moore published his devastating critique of the ideas of the prominent nineteenth-century evolutionary ethicist Herbert Spencer (1851, 1868, 1892). In a definitive manner, Moore's Principia Ethica (1903) showed that Spencer and all who thought like him were guilty of that gross conceptual mistake that Moore labeled the “naturalistic fallacy.” Before Moore, evolutionary ethics had flourished like the rank weed that it was. After Moore, evolutionary ethics lay smoldering on the bonfire of discarded ideas. And a good thing, too, thinks the philosopher, for there have been few excesses of nineteenth-century capitalism or twentieth-century militarism and fascism that have not had their biologyoriented partisans. Choose your vileness, and there has been someone prepared to defend it in the name of evolution. Those whose inquiries have taken them beyond philosophical folklore will know that today there is little need to spend much time on that latter charge (Russett 1976; Kelley 1981; Ruse 1986, 1996; Richards 1987; Pittenger 1993; Crook 1994). For years, historians have been looking at the claims of the evolutionary ethicists – “social Darwinians” as they are often called – and it is clear that although some pretty dreadful things have been suggested and sometimes even perpetrated in the name of evolution, the picture is by no means uniformly black.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.021
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.012
Scholarly communication0.0030.004
Open science0.0010.002
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.052
GPT teacher head0.226
Teacher spread0.174 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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

Citations9
Published2010
Admission routes1
Has abstractyes

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