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Record W3121400901

Punishment and Permissibility in the Criminal Law

2012· article· en· W3121400901 on OpenAlexaff
Vincent Chiao

Bibliographic record

VenueSSRN Electronic Journal · 2012
Typearticle
Languageen
FieldNeuroscience
TopicFree Will and Agency
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCriminal lawLawPunishment (psychology)LegislationPolitical scienceSupreme courtPunitive damagesLegislatureCriminal procedureDoctrinePsychologySocial psychology
DOInot available

Abstract

fetched live from OpenAlex

The United States Supreme Court has repeatedly insisted that what distinguishes a criminal punishment from a civil penalty is the presence of a punitive legislative intent. Legislative intent has this role, in part, because court and commentators alike conceive of the criminal law as the body of law that administers punishment; and punishment, in turn, is conceived of in intention-sensitive terms. I argue that this understanding of the distinction between civil penalties and criminal punishments depends on a highly controversial proposition in moral theory — namely, that an agent’s intentions bear directly on what it is permissible for that agent to do, a view most closely associated with the doctrine of double effect. Therefore, legal theorists who are skeptical of granting intention this kind of significance owe us an alternative account of the distinctiveness of the criminal law. I sketch the broad outlines of just such an alternative account — one that focuses on the objective impact of legislation on a class of protected interests, regardless of the state’s motivations in enacting the legislation. In other words, even if the concept of punishment is unavoidably intention-sensitive, it does not follow that the boundaries of the criminal law are likewise intention-sensitive, because the boundaries of the criminal law may be drawn without reference to the concept of punishment. I conclude by illustrating the application of this view to a pair of well-known cases, and noting some of its ramifications.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.124
Threshold uncertainty score0.456

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.031
GPT teacher head0.277
Teacher spread0.246 · 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.

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

Citations0
Published2012
Admission routes1
Has abstractyes

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