Aging Prisoners: A Brief Report of Key Legal and Policy Dilemmas
Bibliographic record
Abstract
Background: The social phenomenon of the aging of the prison population has raised various legal and policy challenges. Objective: The goal of this brief report is to describe the current key legal-policy dilemmas in this field. Methods: A computerized search for legal documents, articles and studies using relevant key words was conducted in computerized databases. Results: Five key dilemmas were found: (1) Early and compassionate release of older prisoners; (2) Segregation or integration of older prisoners; (3) Heaven or hell? The meaning of imprisonment in old age; (4) Fixed v. tailored sentences to older offenders; and (5) Is prison the right place to send older offenders? Conclusion: Evidence regarding the unique socio-medical needs of older prisoners does not provide easy or simple answers to the legal-policy dilemmas in this field. Hence, as of today, the scholarly discussions in this field seem to be more normative (what "should" be the solution) rather than empirical (what "is" the evidence-based solution). Therefore, more empirical evidence is needed in order to design old-age based legal-policies towards older prisoners.
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 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 | 0.010 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.015 | 0.012 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| 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".