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Record W2336071881 · doi:10.1177/0887403415623033

Are Inmates With Military Backgrounds “Army Strong?”

2016· article· en· W2336071881 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueCriminal Justice Policy Review · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicCriminal Justice and Corrections Analysis
Canadian institutionsWestern University
Fundersnot available
KeywordsPrisonMilitary servicePsychologyArgument (complex analysis)Military personnelService memberCriminologyClinical psychologyPsychiatrySocial psychologyMedicinePolitical scienceLaw

Abstract

fetched live from OpenAlex

We use data from the Survey of Inmates in State and Federal Correctional Facilities 2004 to examine the relationship between prior military service and misbehavior for a nationally representative sample of incarcerated inmates. Our regression analyses, based on 18,185 respondents across 326 prisons, suggest that inmates with military backgrounds tend to fare better than others across 12 negative prison outcomes. In contrast, we do not find much support for the argument—implied by violentization and other theories—that inmates with military backgrounds fare worse than others, with the exception of high levels of post-traumatic stress disorder (PTSD) and violent victimization. Supplementary analyses also show conditional patterns based on exposure to combat as well as honorable versus dishonorable discharge.

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.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.853
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.069
GPT teacher head0.379
Teacher spread0.310 · 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