“I Feel Like I’m About to Walk Out of Prison Blindfolded”: Prison Programming and Reentry
Bibliographic record
Abstract
abstract: People who participate in correctional treatment programming are viewed as making positive steps towards their reentry into society. However, this is often assessed through a simple “yes” or “no” response to whether they are currently participating without much emphasis on how, why, or to what degree that participation is meaningful for reentry preparedness. The present study aims to a) identify to what extent there is variation in the degree to which women participate in programming and are prepared for reentry, b) identify the characteristics that distinguish highly-involved programmers from less involved programmers, c) identify the characteristics that distinguish women who are highly-prepared for reentry from women who are less prepared, and d) assess whether levels of involvement in programming relates to levels of reentry preparedness. The sample comes from interviewer-proctored surveys of 200 incarcerated women in Arizona. Two indices were created: one for the primary independent variable of program involvement and one for the dependent variable of reentry preparedness. Logistic and multivariate regressions were run to determine the indices’ relatedness to each other and the characteristic variables. The two indices did not have a statistically significant relationship with each other. However, variation across them is found. This indicates that programmers may not be a homogenous group and that they may engage with programming to varying degrees based on a multitude of indicators.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".