Releasing hope—Women’s stories of transition from prison to community
Bibliographic record
Abstract
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 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 | 0.009 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.025 | 0.028 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.004 | 0.010 |
| 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".