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Record W2938144953 · doi:10.1111/1745-9133.12445

Persistent puzzles

2019· article· en· W2938144953 on OpenAlexaff
Richard Sparks, James Gacek

Bibliographic record

VenueCriminology & Public Policy · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicCriminal Justice and Corrections Analysis
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsPunishment (psychology)NormativeNegotiationPrisonContext (archaeology)DignitySanctionsSociologyLaw and economicsRescissionPolitical scienceCriminologyPositive economicsLawEconomicsSocial psychologyPsychology

Abstract

fetched live from OpenAlex

Research Summary In our article, we attend to the implied outlooks (“philosophies” in the sense of operative practical discourses and assumptions) and the competing ethical concerns that animate differing views on privatizing corrections. We consider some normative arguments and empirical observations that have been mobilized for and against privatization since the inception of the modern version of this debate in the late 1980s, and we seek to place these in the context of accounts of penal problems over that contentious period. We argue that a multidimensional approach to understanding the sociology of punishment and in particular how certain forms of punishment persist, survive, and thrive is required when considering the privatization of corrections. In using such an approach, we raise quizzical questions regarding the pairing together of punishment and privatization, and as a result, we seek to sharpen the discussion about future prospects. Policy Implications Greater attention must be paid toward public involvement, knowledge, and understanding about penal policies. With particular regard to the involvement of private‐sector actors and interests, this has implications both at the initial contract negotiation stages of expanding correctional privatization as well as at the rescission of such contracts. The impact of penal arrangements on the dignity and integrity of offenders—especially but not only prison inmates—their loved ones and communities, and wider considerations of public interest are abiding and unresolved concerns, and privatization policies must be evaluated in light of these.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.700
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.002

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 head0.085
GPT teacher head0.342
Teacher spread0.257 · 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 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

Citations6
Published2019
Admission routes1
Has abstractyes

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