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Record W2512888067 · doi:10.1093/jlb/lsw037

Public attitudes toward legally coerced biological treatments of criminals

2016· article· en· W2512888067 on OpenAlexaff
Colleen M. Berryessa, Jennifer A. Chandler, Peter B. Reiner

Bibliographic record

VenueJournal of Law and the Biosciences · 2016
Typearticle
Languageen
FieldNeuroscience
TopicPsychology of Moral and Emotional Judgment
Canadian institutionsNeuroDevNetUniversity of British ColumbiaUniversity of Ottawa
Fundersnot available
KeywordsCriminologyContext (archaeology)PsychologyVignetteJurisprudenceIntervention (counseling)Retributive justicePrisonPersonalityCriminal lawPolitical scienceLawSocial psychologyPsychiatryEconomic Justice

Abstract

fetched live from OpenAlex

How does the public view the offer of a biological treatment in lieu of prison for criminal offenders? Using the contrastive vignette technique, we explored this issue, using mixed-methods analysis to measure concerns regarding changing the criminal's personality, the coercive nature of the offer, and the safety of the proposed treatment. Overall, we found that of the three variables, the safety of the pill had the strongest effect on public acceptance of a biological intervention. Indeed, it was notable that the public was relatively sanguine about coercive offers of biological agents, as well as changing the personality of criminals. While respondents did not fully endorse such coercive offers, neither were they outraged by the use of biological treatments of criminals in lieu of incarceration. These results are discussed in the context of the retributive and rehabilitative sentiments of the public, and legal jurisprudence in the arena of human rights law.

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)0.001
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.323
Threshold uncertainty score0.799

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
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 head0.394
GPT teacher head0.344
Teacher spread0.051 · 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 designBench or experimental
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

Citations20
Published2016
Admission routes1
Has abstractyes

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