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Record W2767180104 · doi:10.1177/1362480617724829

Fracturing the penal state: State actors and the role of conflict in penal change

2017· article· en· W2767180104 on OpenAlex
Ashley T. Rubin, Michelle S. Phelps

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueTheoretical Criminology · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicCriminal Justice and Corrections Analysis
Canadian institutionsUniversity of Toronto
FundersEunice Kennedy Shriver National Institute of Child Health and Human Development
KeywordsState (computer science)CriminologyPolitical scienceSociologyComputer science

Abstract

fetched live from OpenAlex

The concept of a penal or carceral state has quickly become a staple in punishment and criminal justice literatures. However, the concept, which suffers from a proliferation of meanings and is frequently undefined, gives readers the impression that there is a single, unified, and actor-less state responsible for punishment. This contradicts the thrust of recent punishment literature, which emphasizes fragmentation, variegation, and constant conflict across the actors and institutions that shape penal policy and practice. Using a case study of late-century Michigan, this article develops an analytical approach that fractures the penal state. We demonstrate that the penal state represents a messy, often conflicted amalgamation of the various branches and actors in charge of punishment, who resist the aims and policies sought by their fellow state actors. Ultimately, we argue that fracture is itself a variable that scholars must measure empirically and incorporate into their accounts of penal change.

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.

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 score0.309
Threshold uncertainty score0.996

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 studies0.0010.006
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 head0.056
GPT teacher head0.338
Teacher spread0.282 · 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