Post-traumatic growth among exonerees: Exploring transformative experiences after incarceration
Bibliographic record
Abstract
Exonerees face numerous barriers to reintegration following their release from wrongful imprisonment. To cope with the challenges they face after exoneration, they draw support from a wide range of external and internal resources, assisting them on their path towards self-sufficiency and resilience. In a study about life after exoneration through in-depth interviews with 26 exonerees, we explored the challenges of reentry for exonerees as well as strategies for success. Although none of the exonerees in our study reported finding closure (and most felt it was not attainable), some experienced what has been described in the academic literature as post-traumatic growth, indicating that although the damage wrought by the injustice of wrongful conviction and incarceration cannot be fully healed, some individuals have transformed their experiences into positive personal accomplishments. In this paper, we highlight the transformative experiences of exonerees as they re-established their lives post-release and consider how systems can provide the resources necessary to support post-traumatic growth.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".