Bibliographic record
Abstract
Parole offers a fiscally responsible mechanism to improve public safety by facilitating the re-entry of Justice-Involved Persons (JIPs) into the community following a period of incarceration.This study sought to establish the effectiveness of parole in reducing post sentence charges in a sample of former parolees (n = 86) as compared with those released at end of sentence (EOS; n = 86) in a matched sample in Iowa.Quality of parole decisions and community supervision were considered and deemed reasonably met.JIPs in each group were matched on risk, sentence type and crime type using Coarsened Exact Matching.Cox proportional hazards survival analyses revealed a non-significant marginal effect of parole on post sentence charge, with former parolees 24% less likely to incur a charge on a given day than JIPs released at EOS (HR = .76,RSE=.24, p = .24).Follow-up analyses revealed an interaction between parole group and sentence type, wherein parolees who had served a sentence for felony offence had better post sentence survival than those who had served a sentence for a misdemeanor.The opposite trend was observed for the EOS group.Further, interactions were observed between group and problem-solving needs and prosocial identities; offering insight into how parole functions.Limitations of the study design and future directions for parole research are discussed, including methods to better account for the need for quality parole decision making and community supervision in parole effectiveness research.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".