Prisons, Older People, and Age-Friendly Cities and Communities: Towards an Inclusive Approach
Bibliographic record
Abstract
This original and ground-breaking interdisciplinary article brings together perspectives from gerontology, criminology, penology, and social policy to explore critically the nature and consequences of the lack of visibility of prisons, prisoners, and ex-prisoners within global research, policy and practice on age-friendly cities and communities (AFCC), at a time when increasing numbers of people are ageing in prison settings in many countries. In addition, the COVID-19 pandemic continues to pose challenges in the contexts both of older peoples' lives, wellbeing, and health, and also within prison settings, and thus it is timely to reflect on the links between older people, prisons, and cities, at a time of ongoing change. Just as there is an extensive body of ongoing research exploring age-friendly cities and communities, there is extensive published research on older people's experiences of imprisonment, and a growing body of research on ageing in the prison setting. However, these two research and policy fields have evolved largely independently and separately, leading to a lack of visibility of prisons and prisoners within AFCC research and policy and, similarly, the omission of consideration of the relevance of AFCC research and policy to older prisoners and ex-prisoners. Existing checklists and tools for assessing and measuring the age-friendliness of cities and communities may be of limited relevance in the context of prisons and prisoners. This article identifies the potential for integration and for cross-disciplinary research in this context, concluding with recommendations for developing inclusive research, policies, and evaluation frameworks which recognise and include prisons and older prisoners, both during and after incarceration.
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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 | 0.023 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.024 | 0.043 |
| Scholarly communication | 0.030 | 0.040 |
| Open science | 0.005 | 0.058 |
| Research integrity | 0.012 | 0.018 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".