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Record W4310076038 · doi:10.1111/hojo.12497

Anticipating prison face work: Dramaturgical risks anticipated by correctional officer recruits

2022· article· en· W4310076038 on OpenAlexaffabout
Michael Adorjan, Rosemary Ricciardelli

Bibliographic record

VenueThe Howard Journal of Crime and Justice · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicCriminal Justice and Corrections Analysis
Canadian institutionsMemorial University of NewfoundlandUniversity of CalgarySt. Michael's Hospital
Fundersnot available
KeywordsOfficerPrisonDiscretionFace (sociological concept)Work (physics)InstitutionSociologyCriminologyPublic relationsPower (physics)PsychologySocial psychologyPolitical scienceLawEngineeringSocial science

Abstract

fetched live from OpenAlex

Abstract There is increasing recognition that correctional officers (COs) serve a crucial role in their work in relation to communications underpinning discretion, especially regarding interactions with prisoners. This article examines attitudes and perceptions among Canadian federal correctional officer recruits (CORs) regarding what they anticipate are the greatest challenges they will face as new COs. We examine these discussions through the framework of Goffman's dramaturgical model of face work, especially face work within the ‘total institution’ of prisons. Our findings centre on anticipated challenges of building rapport with prisoners, including the requirements to monitor one's demeanour and ‘face work’. Characterisations of prisoners as inherently manipulative factor into CO anticipations of interactional challenges. We also consider the role that ‘soft power’ has in facilitating CO‐prisoner rapport and trust, which, we argue, ultimately undergirds opportunities in prisons to facilitate prisoner social change and successful community reintegration and desistance from crime.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.471
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.086
GPT teacher head0.373
Teacher spread0.287 · 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 teacher head, not a consensus.

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

Citations6
Published2022
Admission routes2
Has abstractyes

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