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Record W4389192079 · doi:10.22215/etd/2023-15653

Quasi-Experimental Evaluation of Women's Re-Entry in New Jersey - Through a Black Intersectional Lens

2023· dissertation· en· W4389192079 on OpenAlexaff
Mikaelia Joy Alexandra Miller

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldSocial Sciences
TopicCriminal Justice and Corrections Analysis
Canadian institutionsCarleton University
Fundersnot available
KeywordsPrisonBlack womenRace (biology)Economic JusticeSample (material)Matching (statistics)Black femaleIntersectionalityDemographyCriminologyPsychologyDemographic economicsGender studiesPolitical scienceMedicineSociologyEconomicsLaw

Abstract

fetched live from OpenAlex

Justice-impacted women are understudied and in the state of New Jersey, Black people are overrepresented in the justice system (Nellis, 2021).This outcome evaluation examined if Residential Community Release Programs (RCRPs) are effective at reducing returns to prison compared to women who were released directly from prison with race as a key study variable.A total of 885 women's technical violation data using a three-year fixed follow-up period was retrieved from the New Jersey's Department of Corrections.The RCRP released women were compared to the prison released women and were matched on several covariates using a coarsened exact matching (CEM) procedure.RCRPs reduced technical violation returns but only reached significance for White women, potentially due to the Black women sample being statistically underpowered.This study calls to invest more into gender responsive re-entry programs in New Jersey and to investigate further how Black women can be better served during re-entry. Keywords: race, re-

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)0.010
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.016
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0020.002
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0090.001

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 head0.072
GPT teacher head0.404
Teacher spread0.331 · 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 designObservational
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

Citations0
Published2023
Admission routes1
Has abstractyes

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