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Record W2336587136 · doi:10.1177/1362480616631818

Reconsidering the boundaries of the shadow carceral state: An analysis of the symbiosis between punishment and its memorialization

2016· article· en· W2336587136 on OpenAlexaffabout
Shanisse Kleuskens, Justin Piché, Kevin Walby, A.M. Chen

Bibliographic record

VenueTheoretical Criminology · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicCriminal Justice and Corrections Analysis
Canadian institutionsUniversity of WinnipegUniversity of Ottawa
FundersChina Scholarship Council
KeywordsPrisonMemorializationPenologyPunishment (psychology)CriminologyState (computer science)Punitive damagesShadow (psychology)SociologyLawMass incarcerationPolitical scienceAgency (philosophy)PsychologySocial psychology

Abstract

fetched live from OpenAlex

Beckett and Murakawa conceptualize the ‘shadow carceral state’ as institutions deriving their authority from administrative and civil law that dole out punishment in conjunction with the penal state. This concept enriches criminological inquiry by expanding the boundaries of what punishment work entails. Left unexplored are the contributions of memory institutions such as penitentiary, prison and jail museums intersecting with the penal state that bolster the latter’s power to deprive liberty and inflict pain. Based on an analysis of three Canadian penal history museums, we illustrate how Correctional Service Canada mobilizes federal prison labour and other involuntary prisoner contributions, as well as agency staffing and resources to naturalize punishment. After examining this symbiosis between punishment and its memorialization, we argue for a conception of the shadow carceral state that includes cultural entities and processes which reproduce state control as a dominant way of responding to criminalized conflicts and harms.

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)0.001
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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.513
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.004
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 head0.092
GPT teacher head0.312
Teacher spread0.219 · 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 designTheoretical or conceptual
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

Citations7
Published2016
Admission routes2
Has abstractyes

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