Bibliographic record
Abstract
The current literature on wrongful convictions documents the legal, psychological, and institutional barriers that prosecutors face in considering post-conviction claims of innocence. However, less is known about how the local court context may relate to prosecutors’ decisions to engage in wrongful conviction investigations. To address this gap, the present study explores how characteristics of the local court community are related to the likelihood of prosecutors assisting, actively opposing, or remaining uninvolved in post-conviction claims of innocence. Specifically, we examine prosecutorial involvement in exonerations from three levels: case-factors, organizational factors, and county-context factors. Using archival data on the exonerations of factually innocent individuals (N = 75), we find that case-related factors are the strongest predictors of prosecutors’ involvement in exonerations. Broadly, our findings suggest that prosecutors are more willing to revisit, assist and even investigate potentially wrongful convictions when the stakes are lower (e.g. the offense is less severe, there is no alleged official misconduct, the district attorney is well-established in the role, etc.). Given the wide range of prosecutorial responses to wrongful conviction claims, we emphasize the importance of specialized conviction review units to help routinize the practice of post-conviction review. Secondly, we suggest that district attorneys explicitly define professional performance metrics to include corrective measures such as assisting in the review of wrongful conviction claims. Finally, we encourage states to adopt formal legal regulations to guide prosecutorial behavior in response to post-conviction claims of innocence.
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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 | 0.008 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".