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Record W1235290971

The Loadstone Rock: The Role of Harm in the Criminalization of Plural Unions

2015· article· en· W1235290971 on OpenAlexaboutno aff
Jonathan Turley

Bibliographic record

VenueeYLS (Yale Law School) · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicLegal Systems and Judicial Processes
Canadian institutionsnot available
Fundersnot available
KeywordsCriminalizationHarmLawContext (archaeology)Supreme courtSociologyMoresCriminologyHistoryPolitical sciencePolitics
DOInot available

Abstract

fetched live from OpenAlex

In this Article, Professor Turley explores the concept of social harm in the context of two recent cases in the United States and Canada over the criminalization of polygamy. The cases not only resulted in sharply divergent conclusions in striking down and upholding such laws respectively, but they offered strikingly different views of the concept of harm in the regulation of private consensual relations. Professor Turley draws comparisons with the debate over morality laws between figures like Lord Patrick Devlin and H.L.A. Hart in the last century. Professor Turley argues that the legal moralism of figures like Devlin have returned in a different form as a type of ¿compulsive liberalism¿ that seeks limitations on speech and consensual conduct to combat sexism and other social ills. The alternative, advocated in this Article, is the adoption of a Millian approach to harm that requires a more concrete form of injury or harm to justify individual choice. In what he calls the ¿Loadstone Rock¿ of constitutional analysis, the definition of harm continues to dictate the outcome of the conflict between individual choice and social mores.

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)0.014
metaresearch head score (Gemma)0.025
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.016
Threshold uncertainty score0.076

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.025
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.001
Science and technology studies0.0160.105
Scholarly communication0.0110.011
Open science0.0020.013
Research integrity0.0110.010
Insufficient payload (model declined to judge)0.0030.000

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 head0.028
GPT teacher head0.299
Teacher spread0.271 · 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 designNot applicable
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

Citations1
Published2015
Admission routes1
Has abstractyes

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