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Dangerous Offenders

2024· book-chapter· en· W4403634026 on OpenAlexaff
Susan Dimock

Bibliographic record

VenueOxford University Press eBooks · 2024
Typebook-chapter
Languageen
FieldNeuroscience
TopicFree Will and Agency
Canadian institutionsYork University
Fundersnot available
KeywordsPsychology

Abstract

fetched live from OpenAlex

Abstract This chapter examines the question: “How should polities respond to repeat offenders whose demonstrated willingness to use violence makes then dangerous to the public?” It critiques three themes commonly seen in discussions of dangerous offenders and how societies may respond to them, especially using criminal law: (1) that “dangerousness” should be understood as a prediction of future violence, (2) that polities have a positive “duty to protect” their members from criminal violence, a duty they may fulfill by imprisoning dangerous offenders so long as they continue to be dangerous, and (3) that the right to self-defense can justify the indefinite detention of dangerous offenders It is argued that the incarceration of individuals on purely preventive grounds—to prevent future violence—cannot be reconciled with our concept of punishment. Nor can it comply with multiple constraints states must adhere to if their systems of criminal punishment are to be morally acceptable (e.g., proportionality between the harshness of punishments and the seriousness of crimes for which they are imposed, parity, double jeopardy, and fair notice). Finally, the chapter suggests an alternative to extant legal practices and philosophical theories of punishment that may reconcile the incarceration of dangerous offenders with justice: it requires that we (1) reconceptualize dangerousness as a current dispositional property rather than a prediction of future behavior and (2) show that this property can be within the control of an offender. When dangerousness is seen as a controlled disposition we may criminalize “being a persistent violent dangerous offender” as a status crime. Some of the contours of this new crime are then described.

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: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.985
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
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.052
GPT teacher head0.209
Teacher spread0.157 · 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
GenreOther

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
Published2024
Admission routes1
Has abstractyes

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