The Burden of Innocence: Coping with a Wrongful Imprisonment
Bibliographic record
Abstract
There has been a recent surge of interest in the topic of wrongful conviction in Canada. Most of the research, however, has focused on the many factors that contribute to the problem. Those most affected by these miscarriages of justice - the wrongly convicted themselves - have been largely ignored. This study sought to reveal, through in-depth interviews, the voices and experiences of five wrongly convicted Canadians, as they spoke about wrongful arrest, imprisonment, and release. The respondents reported that during arrest they were victims of tunnel vision and institutional misconduct. They made use of several highly adaptive coping strategies while wrongly imprisoned, including cooperation, withdrawal, preoccupation with exoneration, and rejection of the label criminal. Maintaining innocence while incarcerated entailed notable consequences, which included being perceived by the prison administration to be at high risk of recidivism. Furthermore, given their continual affirmation of their innocence, respondents suffered uncertainty over their release date. Finally, they reported problems following their release, including intolerance of injustice and a desire for compensation. These findings point to the importance of including the experiences of the wrongly convicted in future criminal justice policy and practice considerations.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.014 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".