The Unrealized Promise of College-in-prison: Financial Hurdles to Reenrollment and Completion in the Era of Pell Reinstatement
Bibliographic record
Abstract
College-in-prison programs are positioned to expand substantially under the reinstatement of Pell Grant eligibility for people in prison. While this change will enable more students who have been systemically excluded from higher education to attend college, degree completion is rare during incarceration and post-release. Student perspectives can shed light on both the value of college-in-prison and the financial barriers to realizing its value. This study analyzes data from 12 focus groups with 105 total college-in-prison student participants, 114 student survey responses, and 45 stakeholder interviews. The data were collected between 2018-2022 during a process evaluation of the College-in-Prison Reentry Initiative, which provided funding to college-in-prison programs in New York State as part of the Manhattan District Attorney’s Office Criminal Justice Investment Initiative. The findings demonstrate that students value college-in-prison, describing how it fostered self-reflection and personal growth and provided them with a skillset that may help them gain employment upon release. However, students also raised concerns about reenrolling and completing their degrees following release. Intentional, holistic reentry support could address the largely financial barriers to reenrollment. In so doing, students will be more likely to earn their college degrees after incarceration and experience the full value of a college education.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".