Righting a Wrongful Conviction: A Mixed Method Approach to Examining Exonerees’ Reintegration
Bibliographic record
Abstract
Whether an individual is rightfully or wrongfully convicted, they experience a range of difficulties upon their release back into society. While rightfully convicted individuals are eligible for programming to assist them with their reintegration, wrongfully convicted individuals are currently ineligible. The goal of this program of research was to examine exonerees’ post-release experience. In Study 1, through qualitative interviews, exonerees (N = 14) shared their stories to raise awareness of the reality of their reintegration needs, with specific attention to their priorities of receiving support, treating deinstitutionalization, and promoting community acceptance upon release. Studies 2 and 3 were designed to examine exonerees’ priority of community acceptance. In Study 2, we experimentally examined mechanisms (i.e., criminal status, factor contributing to the conviction, crime, race) that may influence stigma that exonerees experience. Undergraduate students’ (N = 317) reported negative perceptions of false confessors, however, participants believed all exonerees deserved reintegration services and financial compensation. In Study 3, we surveyed members of the Canadian public (N = 206) to assess their perspectives of exonerees’ reintegration; we found they support the government funding reintegration programming for exonerees, and providing them with financial compensation. Taken together, this program of research has provided evidence to support that exonerees’ ineligibility for reintegration support deserves to be reconsidered.
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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 | 0.050 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".