“Using the Language of Christian Love and Charity”: What Liberal Religion Offers Higher Education in Prison
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.
Bibliographic record
Abstract
This article explores what religious frameworks and institutions have to contribute to college-in-prison. We first provide an historical overview of higher education programs in American prisons. Then, we limn the role religion can play in motivating people to commit themselves to educating incarcerated people. Because this work is so thorny, we document some of the generic challenges programs must face and show how religious languages can be an asset in navigating these challenges. Next, we present the pedagogical practices and educational philosophies expressed among the programs in our study. We conclude with some broader reflections about teaching incarcerated people, and, after wrestling with objections, we encourage our colleagues in religious studies—those with faith commitments as well as those without them—to get involved.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it