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Record W4404192120 · doi:10.1177/14624745241296536

Life without parole and euthanasia: The future unintended consequences of current sentencing policies

2024· article· en· W4404192120 on OpenAlexaboutno aff
Joshua Long, James W. Marquart

Bibliographic record

VenuePunishment & Society · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicCriminal Justice and Corrections Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsUnintended consequencesCriminologyCurrent (fluid)Political scienceAssisted suicideCriminal justicePsychologySociologyLaw and economicsLaw

Abstract

fetched live from OpenAlex

Canada and some European nations have authorized different forms of medically assisted death (euthanasia) for their citizens. Naturally, these rights have been extended to incarcerated people as well. It is possible that jurisdictions in the United States will authorize euthanasia for prisoners in states where medically assisted death is permissible for nonincarcerated citizens. It is important for criminal justice scholars to prepare for the controversies that will follow. The United States incarcerates nearly 56,000 people who are serving life without the possibility of parole, and, in total, more than 200,000 people are sentenced to some form of life imprisonment. We use data from the National Corrections Reporting Program, 2000–2019 to examine trends in life sentencing, examine the characteristics of 18,285 life without parole (LWOP) cases. These “lifers” accumulate inside our correctional system and most never leave alive. In the brave new world of euthanasia, what conditions will they need to meet before they can seek release through death? It is argued that LWOP sentences are unethical and unconstitutional, just as euthanasia policies are argued on similar grounds. This paper seeks to provoke a discussion of the ethical and legal aspects of these two controversial policies by asking which is worse: “life” or death.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.163
Threshold uncertainty score0.999

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 studies0.0010.001
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 head0.041
GPT teacher head0.348
Teacher spread0.307 · 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 designQualitative
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
Published2024
Admission routes1
Has abstractyes

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