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Record W4417441721 · doi:10.1111/1745-9125.70031

Correctional officers and drug smuggling: Boundary work, horizontal surveillance, and cultural responses to drug entry

2025· article· en· W4417441721 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueCriminology · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicCriminal Justice and Corrections Analysis
Canadian institutionsUniversity of AlbertaMacEwan University
Fundersnot available
KeywordsPrisonBoundary (topology)NarrativeBoundary objectWork (physics)Boundary-workPrison population

Abstract

fetched live from OpenAlex

Abstract Drug entry into prisons represents a serious issue for both incarcerated people and prison staff. Although substances enter prisons in many ways, staff drug smuggling represents a consistent problem facing correctional institutions globally. We draw on 131 interviews with correctional officers (COs) working in four Western Canadian prisons to analyze how COs understand and respond to drug smuggling. Participants drew on specific cultural narratives to portray coworkers who smuggled drugs, suggesting that CO occupational subcultures played a meaningful role in shaping how they perceived drugs, drug smuggling, and “dirty” correctional staff. Officers further detailed cultural narratives and frames they employed to detect and prevent drug trafficking among their peers. These included the informal social controls of boundary work and horizontal surveillance, which we analyze using Douglas’ concepts of purity and impurity. Participants justified such practices as efforts to reduce drug smuggling but also described how boundary work and horizontal surveillance stratified the CO population in distinctive ways. We conclude by discussing how CO cultures should influence our perceptions of staff drug smuggling.

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.

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationallow
gptno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Qualitativelow
models splitAgreement compares identical category sets and study designs across arms.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.481
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.030
GPT teacher head0.320
Teacher spread0.290 · 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