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Record W4398144122 · doi:10.1111/cag.12931

Examining the intersection of carceral space and well‐being: Correctional officers' perspectives on old and new prison design

2024· article· en· W4398144122 on OpenAlexaffvenueabout
Brittany Mario, Rosemary Ricciardelli, Matthew S. Johnston, Marcus A. Sibley

Bibliographic record

VenueCanadian Geographies / Géographies canadiennes · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicCriminal Justice and Corrections Analysis
Canadian institutionsSaint Mary's UniversityMemorial University of Newfoundland
Fundersnot available
KeywordsPrisonIntersection (aeronautics)CriminologySpace (punctuation)SociologyPsychologyGender studiesGeographyCartographyComputer science

Abstract

fetched live from OpenAlex

Abstract In the current study, we explored the prison design and infrastructural changes that Canadian correctional officers consider to be essential in the construction of a new provincial correctional institution intended to replace Her Majesty's Penitentiary (HMP) in the Canadian province of Newfoundland and Labrador. Analyzing 28 semi‐structured interviews conducted with correctional officers employed at HMP, we found the poor working conditions within HMP are, at least in part, related to the physical design of the prison, including inadequate lighting, poor air quality and temperature, high sound levels, and other spatial limitations. Building on the prison design literature, findings suggest that while prison design requires attention to physical security at the forefront, there are ways to improve the space, recognizing how an uncomfortable workplace and living conditions also pose a potential threat to the well‐being and safety of correctional officers and people who are incarcerated.

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)0.004
metaresearch head score (Gemma)0.006
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 score0.345
Threshold uncertainty score0.693

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0150.014
Scholarly communication0.0060.002
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.019
GPT teacher head0.238
Teacher spread0.218 · 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
Published2024
Admission routes3
Has abstractyes

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Same venueCanadian Geographies / Géographies canadiennesSame topicCriminal Justice and Corrections AnalysisFrench-language works237,207