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Record W6947610875 · doi:10.3886/icpsr38003.v1

Understanding Incarceration and Re-Entry Experiences of Female Inmates and Their Children: The Women's Prison Inmate Networks Study (WO-PINS), Pennsylvania, 2017-2018

2023· dataset· en· W6947610875 on OpenAlexaff

Bibliographic record

VenueICPSR Data Holdings · 2023
Typedataset
Languageen
FieldEnvironmental Science
TopicSpecies Distribution and Climate Change
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsPrisonRecidivismMisconductConstruct (python library)Social capitalSocial network (sociolinguistics)Work (physics)Survey data collectionPhase (matter)

Abstract

fetched live from OpenAlex

This study advances the understanding of incarceration and reentry, and their consequences for women by focusing on prison social systems and their informal network structures. The data for this project are aimed at four research questions: (1) What is the informal social structure within prison? (2) How are inmates' positions within the informal structure correlated with their health? (3) What are the consequences of informal social structure and inmates' positions within it for inmate-level and prison-level outcomes? and (4) How does in-prison and out-of-prison social capital correspond with community reentry and family reintegration? In phase 1, network data were collected for "get along with best" and "power and influence" nominations along with survey data to contextualize the measured networks. In phase 2, semi-structured interviews were conducted with eligible respondents to gather expectations for re-entry and anticipated egocentric support networks. Phase 3 followed paroled inmates for two subsequent interviews, and also gathered interviews with their children, and the children's caretakers. Administrative records were used to construct a recidivism supplement that is appropriate for modeling the hazard of recidivism following release. Behavioral data are combined from multiple sources, including inmate surveys, prison work records, misconduct records, drug tests, visitation lists, and gang classification data.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Dataset · Consensus signal: none
Teacher disagreement score0.878
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.003
Research integrity0.0000.000
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 head0.114
GPT teacher head0.290
Teacher spread0.176 · 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 designNot applicable
Domainnot available
GenreDataset

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
Published2023
Admission routes1
Has abstractyes

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