Life without parole and euthanasia: The future unintended consequences of current sentencing policies
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".