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Record W4295991109 · doi:10.1177/00027162221099291

Barred: Labor Market Dynamics and Human Capital Development among People on Probation and Parole

2022· article· en· W4295991109 on OpenAlexaboutno aff
Bryan L. Sykes, Meghan Ballard, Daniela Kaiser, Vicente Celestino Mata, J. Amanda Sharry, Justin L. Sola

Bibliographic record

VenueThe Annals of the American Academy of Political and Social Science · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicCriminal Justice and Corrections Analysis
Canadian institutionsnot available
FundersFederal Student Aid
KeywordsHuman capitalLabour economicsStigma (botany)WageQuarter (Canadian coin)State (computer science)Work (physics)Criminal recordEconomicsBusinessCriminologySociologyMarket economyPsychology

Abstract

fetched live from OpenAlex

Obtaining employment is a major barrier to social reintegration for people on probation or parole. Research on the reentry process identifies several mechanisms that accentuate difficulties in locating work, including human capital development, structural changes in the labor market, and onerous probation and parole conditions. In this article, we review theories that explain low labor market participation rates among people reentering society, and we draw on multiple sources of data to identify the types of jobs that are available to people with low human capital. We find that nearly a quarter of people in America’s state and federal prisons had permanently removed themselves from the formal labor market before their most recent arrest; however, exclusionary hiring practices in the formal labor market often push those carrying the stigma of a criminal record into underground or informal labor markets, where wage rates are markedly higher than the federal minimum wage. Our findings demonstrate that severe and chronic employment struggles often predate and follow incarceration. We provide a detailed discussion of policy reform proposals that could help to remedy this harmful dynamic.

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 categoriesScience and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.906
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0030.005
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.032
GPT teacher head0.355
Teacher spread0.323 · 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; both teacher heads agree on what is shown here.

Study designTheoretical or conceptual
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
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueThe Annals of the American Academy of Political and Social ScienceSame topicCriminal Justice and Corrections AnalysisFrench-language works237,207