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Record W2903952246 · doi:10.1111/hojo.12291

The Challenge of Redefining the Imprisoned Self as an Artist: The Pedagogical Rituals of a Prison Arts Instructor

2018· article· en· W2903952246 on OpenAlexaff
Joseph H. Michalski

Bibliographic record

VenueThe Howard Journal of Crime and Justice · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicArt Therapy and Mental Health
Canadian institutionsThe King's University
Fundersnot available
KeywordsPrisonRecidivismThe artsIdentity (music)Value (mathematics)Intervention (counseling)SociologyPsychologyCriminologyTask (project management)Visual artsAestheticsArtEngineeringComputer sciencePsychiatry

Abstract

fetched live from OpenAlex

Abstract While some advocate the use of arts programmes to help improve inmates’ lives and reduce recidivism, the process of how such programmes can have therapeutic and practical value in prison's hostile environs requires further study. This project investigates how one prison arts instructor approaches the task of developing the inmates’ creative potential and unleashing their ‘inner artist’. The article describes the pedagogical approach aimed at helping prison inmates redefine themselves as artists via art classroom rituals. The imprisoned self as the artist, however, emerges mainly as a temporary identity that must be submerged upon return to the daily routines of hypermasculine prison environments. Thus inmates experience a profound duality of their identities, split between ‘artist’ and ‘inmate’, which constrains the long‐term therapeutic and rehabilitative value of the intervention.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0100.015
Scholarly communication0.0060.002
Open science0.0010.007
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.130
GPT teacher head0.361
Teacher spread0.231 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations2
Published2018
Admission routes1
Has abstractyes

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