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Record W2531587836 · doi:10.1177/0734016816669981

Shifting Grounds

2016· article· en· W2531587836 on OpenAlexaffabout
Hayley Crichton, Rosemary Ricciardelli

Bibliographic record

VenueCriminal Justice Review · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicCriminal Justice and Corrections Analysis
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsPenologyPunitive damagesOfficerLegislationAgency (philosophy)CriminologyPolitical scienceAsideWork (physics)Face (sociological concept)SociologyPublic relationsPublic administrationPrisonLawEngineeringSocial science

Abstract

fetched live from OpenAlex

The framework of the new penology will be applied to reveal how the contemporary objectives of incarceration have functioned to alter the role of Canadian provincial correctional officers (COs). Specifically, changing policy and legislation toward a more punitive agenda shape the daily operations of correctional facilities and how COs interact with those in their custody. Rehabilitative initiatives of any kind appear to be pushed aside, as the new or intensifying challenges associated with the growing prisoner populations and changing penal discourses are addressed. Semistructured face-to-face interviews were conducted with Canadian provincial COs and data from these interviews were analyzed to explicate the ways in which the new penology has reshaped COs’ employment in part due to their obligatory adherence to increasingly punitive managerial directives including an increased reliance on using disciplinary segregation. Findings suggest officer strain is impacted by their lack of agency and decision-making capabilities in light of these occupational changes. Our findings also evince that although COs work in a too often negative environment, many believe in the rehabilitative potential of incarceration and, further, oppose the use of segregation.

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)0.002
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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.969
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.067
GPT teacher head0.378
Teacher spread0.311 · 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 designOther design
Domainnot available
GenreReview

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

Citations16
Published2016
Admission routes2
Has abstractyes

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