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Record W2997206074 · doi:10.35502/jcswb.113

Releasing hope—Women’s stories of transition from prison to community

2019· article· en· W2997206074 on OpenAlexvenueno aff
Lynn Fels, Mo Korchinski, Ruth Elwood Martin

Bibliographic record

VenueJournal of Community Safety and Well-Being · 2019
Typearticle
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsnot available
Fundersnot available
KeywordsNarrativeAgency (philosophy)Resistance (ecology)Participatory action researchPrisonCitizen journalismSociologyFace (sociological concept)Lived experiencePublic relationsGender studiesIdentity (music)Narrative inquiryMedia studiesPsychologyCriminologyPolitical scienceAestheticsSocial sciencePsychoanalysisArtLaw

Abstract

fetched live from OpenAlex

This article embodies two key narratives among many that have emerged from a 14-year research project. The first narrative is of a community-engaged solution, a peer health mentor program, which was imagined during a prison participatory health and university research project, as described in Arresting Hope. The second is the narrative of Releasing Hope, a collection of writings by women with incarceration experience sharing their experiences, their challenges, and the barriers they face as they seek to heal from fractured and interrupted lives. A unique form of collaboration, innovation, research creation, and knowledge dissemination, Releasing Hope invites readers to reconsider communal perceptions, attitudes, and resistance towards those with incarceration experience, who struggle each day to be seen, not as former criminals, but as women capable of reimagining and enacting new lives. These two narratives illustrate the possibilities present when women are empowered with voice and agency. In the article, we aim to capture the spirit of both projects, in the interspersing of text and image, a collage of voices that speak to the experiences and learning that emerged through these two research ventures.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.206
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0000.000

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 head0.072
GPT teacher head0.367
Teacher spread0.295 · 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 teacher head, not a consensus.

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

Citations1
Published2019
Admission routes1
Has abstractyes

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