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DOING HER OWN TIME? WOMEN'S RESPONSES TO PRISON IN THE CONTEXT OF THE OLD AND THE NEW PENOLOGY*

2000· article· en· W1990165124 on OpenAlexaff
Candace Kruttschnitt, Rosemary Gartner, Amy E. Miller

Bibliographic record

VenueCriminology · 2000
Typearticle
Languageen
FieldSocial Sciences
TopicCriminal Justice and Corrections Analysis
Canadian institutionsUniversity of Toronto
FundersNational Science Foundation
KeywordsPenologyPrisonSituational ethicsContext (archaeology)SocializationInstitutionPsychologyKinshipState (computer science)CriminologySociologySocial psychologyGeographySocial science

Abstract

fetched live from OpenAlex

Assumptions about gender role socialization dominated explanations for gender differences in responses to incarceration. We suspend these gender comparisons, which produced the focus on homosexuality and kinship networks in women's prisons, to determine how women's pre‐prison experiences, in the context of two different institutions, influence the way they “do time.” We analyze in‐depth interviews with a diverse sample of 70 female inmates housed in the California Institution for Women (CIW)—the oldest prison for women in the state—and Valley State Prison (VSP)—the newest prison for women. These two institutions differ in structure, size, and management philosophy, and accordingly necessitate the consideration of moderating situational effects. We use qualitative analysis to examine how women do time and to determine whether individual variations in doing time are similar across very different institutions.

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)0.003
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 score0.014
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.007
Scholarly communication0.0040.002
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.032
GPT teacher head0.304
Teacher spread0.272 · 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

Citations124
Published2000
Admission routes1
Has abstractyes

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