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Prisoner Experiences

2017· reference-entry· en· W4252916666 on OpenAlexaff
Dawn Moore

Bibliographic record

VenueOxford Research Encyclopedia of Criminology and Criminal Justice · 2017
Typereference-entry
Languageen
FieldSocial Sciences
TopicCriminal Justice and Corrections Analysis
Canadian institutionsCarleton University
Fundersnot available
KeywordsImprisonmentPrisonFeelingSadnessLawPsychologyCriminologySociologyMass incarcerationPsychoanalysisHistoryPolitical scienceSocial psychology

Abstract

fetched live from OpenAlex

Abstract The variables impacting how one experiences imprisonment are far ranging. George Jackson (1994), a pivotal character in American penal history, wrote that, “[b]lackmen born in the [United States] and fortunate enough to live past the age of eighteen are conditioned to accept the inevitability of prison” (p. 4). Ruth Wyner (2002), incarcerated 40 years after Jackson under vastly different circumstances, describes a very different sort of bleakness associated with her incarceration: One evening, just before I settled down to try to sleep, I allowed myself to remember my daughter in a way that I usually suppressed: remembering and feeling all the love that I had for her, every bit. A huge chasm grew inside me, dark and raw, and my throat constricted as I felt enveloped by the sadness. This was what was inside of me when I allowed myself to touch it.(p. 156) Such personal experiences of incarceration offer a window into how prisons function, or often more correctly, fail to function, from the point of view of the prisoner. These perspectives are vitally important to a fulsome understanding of incarceration because prisoners and their experiences paint a picture of confinement that is patently different from those described by penal officials and governments. There are numerous issues that shape the experiences of confinement, both historically and in the present day, a list longer than can be adequately addressed in this entry. Still, there are key concerns that recur in the literature and in ongoing debates about incarceration. Included here are human rights abuses, overcrowding, the overuse of solitary confinement, the situation of women prisoners, the incarceration of indigenous peoples, and health, especially mental health concerns.

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)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.786
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0040.006
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.138
GPT teacher head0.422
Teacher spread0.284 · 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; both teacher heads agree on what is shown here.

Study designOther design
Domainnot available
GenreOther

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
Published2017
Admission routes1
Has abstractyes

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