‘I made it home and i’m flourishing’: qualitative interviews investigating post-traumatic growth among the Ohio innocence project’s freed clients & exonerees
Bibliographic record
Abstract
Wrongfully convicted individuals spend decades fighting for their freedom, and once released, they must reintegrate and adapt to society while dealing with collateral processes (e.g. legal issues). Despite research investigating the many issues that wrongfully convicted individuals experience post-release, little is known about how they might redefine their traumatic experiences to cope with their new routines. To that end, we interviewed 13 of the Ohio Innocence Project’s freed clients and exonerees using questions that were conceptualized based on the Post-traumatic Growth (PTG) cognitive model. The results showed that the interviewed clients did experience the five factors of PTG: New Possibilities, Relating to Others, Spiritual Change, Appreciation for Life, and Personal Strength. Moreover, the interviewees shared advice for new and future freed clients and exonerees about how to navigate reintegration and how to overcome common difficulties. Based on these interviews, individual-level positive outlets (e.g. maintaining and developing a community), innocence organization-level opportunities (e.g. conducting speaking engagements), and criminal legal system-level reforms (e.g. less expensive prison phone calls) that could improve incarceration, exoneration, and reintegration experiences, are discussed.
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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 | 0.013 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.018 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.007 |
| 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".