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Record W4414950366 · doi:10.1111/nin.70058

Accelerated Aging, Debilitation, Slow Death, and the Multiple Temporalities of Aging Incarcerated Persons: A Philip K. Dickian Science Fiction Examination

2025· article· en· W4414950366 on OpenAlexafffundabout
Jim A. Johansson, Dave Holmes, Étienne Paradis-Gagné

Bibliographic record

VenueNursing Inquiry · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicCriminal Justice and Corrections Analysis
Canadian institutionsUniversité de MontréalUniversity of AlbertaUniversity of Ottawa
FundersCanadian Nurses Foundation
KeywordsTemporalitiesPrisonPerspective (graphical)TemporalityBricolageLived experience

Abstract

fetched live from OpenAlex

Prisons are debilitating and disorienting spaces. Temporal realities themselves are altered from those of the outside world. This paper builds upon a study of prison staff work with aging incarcerated persons in Canadian federal penitentiaries, wherein the prison environment itself contributes to accelerated aging and slow death. Simultaneously, temporal experiences in prison both slow and become cyclic, rhythmic. These multiple temporalities flow independent of the "outside" world, which, from the perspective of incarcerated persons, accelerates. To reconcile these multiple, conflicting simultaneous temporalities, we turn to science fiction, specifically the works of Philip K. Dick, to build a conceptual world where such paradoxical temporalities coexist. A bricolage approach is adopted to interrogate these temporalities, and their effects on incarcerated persons, including debilitation and institutionalization, and the production of persons often incapable of transitioning back to the "real" world outside of prison. Proposals for both nursing practice and future research possibilities are provided.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.141
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0020.002
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 head0.062
GPT teacher head0.370
Teacher spread0.308 · 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.

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
Published2025
Admission routes3
Has abstractyes

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