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Record W4212970260 · doi:10.1093/jhmas/jrac003

Change Your Face, Change Your Life? Prison Plastic Surgery as a Way to Reduce Recidivism

2022· article· en· W4212970260 on OpenAlexaboutno aff
S.A. Pearl

Bibliographic record

VenueJournal of the History of Medicine and Allied Sciences · 2022
Typearticle
Languageen
FieldHealth Professions
TopicMedical Malpractice and Liability Issues
Canadian institutionsnot available
Fundersnot available
KeywordsPrisonRecidivismEthosFace (sociological concept)CriminologyUnit (ring theory)Punishment (psychology)RehabilitationPrison reformPsychologyMedicineSociologyPolitical scienceLawSocial psychologyPhysical therapy

Abstract

fetched live from OpenAlex

The paper explores the history and ethics of prison plastic surgery programs, which ran from the 1950s through as late as 1988 in the UK, the US, and Canada. I focus in particular on the Oakalla Prison, the Haney Young Offenders Correctional Unit, and the Kingston Penitentiary in Canada; the Huntsville Penitentiary in Texas; the Camp Hill Borstal in England; and the collaboration between Montifiore Hospital and Sing-Sing Prison in New York. Sometimes federally funded, these programs were designed to reduce rates of recidivism, operating under the notion that a changed face could lead to a changed character. The surgeries were rooted in a commitment to rehabilitation through medicine, offering participants access to surgery in exchange for good behavior, participation in an experimental protocol, and in some cases, providing training for medical students and residents. As I show, these programs were consonant with prevailing experimental and ethical ethos, and maintain deep continuity with the idea that changes in appearance could lead to changes in behavior.

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)0.008
metaresearch head score (Gemma)0.012
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.202
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.366
GPT teacher head0.439
Teacher spread0.073 · 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 designNot applicable
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

Citations1
Published2022
Admission routes1
Has abstractyes

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