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Record W3082927092 · doi:10.1111/hojo.12385

Unpacking Harm: Correctional Officer Framing of Sex Offenders and Protective Custody

2020· article· en· W3082927092 on OpenAlexafffundabout
Rosemary Ricciardelli

Bibliographic record

VenueThe Howard Journal of Crime and Justice · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicCriminal Justice and Corrections Analysis
Canadian institutionsMemorial University of Newfoundland
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsFraming (construction)PrisonHarmCriminologyOfficerSex offenderPsychologyPopulationPolitical scienceSocial psychologySociologyLawEngineering

Abstract

fetched live from OpenAlex

Abstract The experiences of persons convicted or charged with sex‐related offences are informed by trends in how the sex offender population in society is defined and understood. I draw on data from in‐depth semi‐structured interviews with 100 Canadian provincial correctional officers to explore the harms tied to the framing of sex offenders in prison, including those embedded in institutional structures. I conceptualise how correctional officers understand sex offenders and how the structures in place to ‘protect’ those labelled as sex offenders are, unintentionally, harmful in their own right. I argue that officers rely on evolving strategies of risk mitigation that they must understand, develop, and learn in the prison context. Emphasis is placed on possible policy or needs that may assist in recognising how ‘protective custody’ may simply be lip service to further stigmatise an already marginalised population.

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)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.619
Threshold uncertainty score0.765

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.014
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0240.029
Scholarly communication0.0070.003
Open science0.0020.009
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0040.000

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 head0.048
GPT teacher head0.317
Teacher spread0.269 · 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 designObservational
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

Citations1
Published2020
Admission routes3
Has abstractyes

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