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Record W4409238291 · doi:10.1080/01639625.2025.2484619

The Language of Authority: Swearing, Prisonization, and Words Behind the Wall for the Correctional Officer

2025· article· en· W4409238291 on OpenAlexafffundabout
Zachary Towns, Micheal Taylor, Marina Carbonell, Rosemary Ricciardelli

Bibliographic record

VenueDeviant Behavior · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicSwearing, Euphemism, Multilingualism
Canadian institutionsMemorial University of Newfoundland
FundersCanadian Institutes of Health Research
KeywordsOfficerPsychologyCriminologyLawEngineeringSocial psychologyPolitical science

Abstract

fetched live from OpenAlex

Words are integral to human communication across social interactions. In the current study, we examine how the language of swearing is used in Canadian federal prisons to understand how correctional officers (COs) socialize and deliver human service. We seek to add to the body of literature suggesting COs experience the effects of prisonization, as we find nuance in how prison swearing for COs is a unique aspect of their work environment, integral to the norms of prison society, but concurrently, socially and professionally purposeful. We highlight how swearing – often dismissed as deviant – functions as an intentional, context-dependent communication strategy embedded within CO relationships in prison. Findings suggest swearing serves to manage stress, signal belonging, assert authority, and express masculinities, while also producing unintended disruptive effects on Canada’s national correctional officer workforce. We argue that swearing, as a normalized yet misunderstood form of profanity, plays a critical role in rapport building and human service delivery in correctional environments.

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)0.002
metaresearch head score (Gemma)0.007
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 score0.390
Threshold uncertainty score0.775

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0130.018
Scholarly communication0.0050.002
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 head0.023
GPT teacher head0.357
Teacher spread0.334 · 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

Citations0
Published2025
Admission routes3
Has abstractyes

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