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Record W2607881040 · doi:10.1177/1748895817706719

Searching prison cells and prisoner bodies: Redacting carceral power and glimpsing gendered resistance in women’s prisons

2017· article· en· W2607881040 on OpenAlexaffabout
Gillian Balfour

Bibliographic record

VenueCriminology & Criminal Justice · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicCriminal Justice and Corrections Analysis
Canadian institutionsTrent University
Fundersnot available
KeywordsPrisonCriminologyConfiscationAgency (philosophy)Corporate governanceResistance (ecology)PopulationPrinciple of legalityPolitical scienceLawEntitlement (fair division)ImprisonmentSociologyBusiness

Abstract

fetched live from OpenAlex

In this article, I explore the routinized practices of prisoner discipline: searching bodies and cells in four Canadian federal women’s prisons. Through an analysis of post-search reports as well as reported incidents of use of force, I discuss three key findings: searching and confiscation patterns across institutions are not dictated by size of the inmate population or security level of the institution; the redaction of information by prison authorities is an increasing and pervasive tactic of penal governance legitimated through an inter-legality of privacy and security; and that the searching of prisoner bodies and cells suggests a highly discretionary use of searching authority across women’s federal prisons that produces a gendered organizational logic. The text of the reports implies how women prisoners continue to be censured for their errant behaviour through the confiscation of personal items deemed to be unauthorized. These data also illustrate the ways in which women prisoners seek to achieve agency and self-determination within limited means.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science 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 score0.059
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0040.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.071
GPT teacher head0.352
Teacher spread0.281 · 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

Citations22
Published2017
Admission routes2
Has abstractyes

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