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Record W3165571656 · doi:10.1177/26326663211015852

Prisoner-on-prisoner drug searches in prisons in England and Wales: ‘Business as usual’

2021· article· en· W3165571656 on OpenAlexaboutno aff
Joanne Wilkinson, Jenny Fleming

Bibliographic record

VenueIncarceration · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicCriminal Justice and Corrections Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsPrisonCriminologySexual assaultQuarter (Canadian coin)PsychologyMedicineHistorySuicide preventionPoison controlMedical emergency

Abstract

fetched live from OpenAlex

Prisoner reported drug and contraband searches in adult men’s prisons in England and Wales represented almost a quarter of reported and recorded ‘sexual assaults’ from 2004 to 2014. These searches are more likely to involve multiple perpetrators and weapon use than other types of sexual assaults and are most frequently carried out in the relative privacy of a cell. The research presented here is based on an analysis of Her Majesty’s Prison and Probation Service (formerly the National Offender Management Service) Incident Recording System data, providing insights into the proportion of recorded sexual assaults which are related to drug searches. This analysis enables a distinction to be made between prisoner-on-prisoner drug and contraband searches and other sexual assaults. Analysis shows that prisoner-on-prisoner searches are frequent, often pre-meditated, brutal and appear to be an accepted aspect of everyday prison life.

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)0.001
metaresearch head score (Gemma)0.004
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.207
Threshold uncertainty score0.412

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
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 head0.030
GPT teacher head0.328
Teacher spread0.298 · 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

Citations4
Published2021
Admission routes1
Has abstractyes

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