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Record W2918823523 · doi:10.3390/rel10030169

“Using the Language of Christian Love and Charity”: What Liberal Religion Offers Higher Education in Prison

2019· article· en· W2918823523 on OpenAlexaff
Charles Atkins, Joshua Dubler, Vincent Lloyd, Mel Webb

Bibliographic record

VenueReligions · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicEducation Discipline and Inequality
Canadian institutionsUniversité de Montréal
FundersLouisville InstituteAmerican Academy of Religion
KeywordsCommitPrisonFaithFace (sociological concept)SociologyAsset (computer security)Religious educationWork (physics)Public relationsCriminologyPolitical sciencePedagogySocial scienceEpistemologyPhilosophyEngineeringComputer security

Abstract

fetched live from OpenAlex

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.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0090.018
Scholarly communication0.0050.004
Open science0.0010.005
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.038
GPT teacher head0.384
Teacher spread0.346 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2019
Admission routes1
Has abstractyes

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