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Record W7034900175

From Word to Practice: Eugenic Language in Sterilization Legislation in North America (1905-1945)
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2015· other· en· W7034900175 on OpenAlexaboutno aff

Bibliographic record

VenuePhilSci-Archive (University of Pittsburgh) · 2015
Typeother
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetics, Bioinformatics, and Biomedical Research
Canadian institutionsnot available
Fundersnot available
KeywordsEugenicsSterilization (economics)LegislationStatuteScholarship
DOInot available

Abstract

fetched live from OpenAlex

Between 1905 and 1945, 31 states in the Untied States and 2 provinces in Canada enacted sterilization legislation. Over 70 statutes and amendments were enacted to guide, oversee and regulate sterilization practice, while over 24 distinct conditions were offered as grounds for sterilization. Although excellent legal, historical, and philosophical scholarship has investigated the motivations, causes and consequences of this legislation (Paul, 1995; Dowbiggin, 1997; Lombardo, 2008), little work has been done to explicitly systematic analyse the language used in sterilization legislation. 
\nThis brief study attempts to fill some of the gap by attending to a number of questions that arise in the context of sterilization legislation. Five questions are addressed: Are there any patterns to the eugenic language in sterilization legislation? Does the eugenic sterilization language reflect what is found on other eugenic lists? What can sterilization language tell us about the mechanics of eugenics? What can sterilization legislation reveal about the role of feeble-mindedness or mental deficiency in eugenic history? And finally, what might sterilization language tell us about eugenic thought more generally? In answering these questions, we look to add one more piece to the puzzle that is eugenic history in North America. 
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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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.889
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.013
GPT teacher head0.260
Teacher spread0.248 · 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
GenreMethods

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
Published2015
Admission routes1
Has abstractyes

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