A Day Late and a Dollar Short: Examining Perceptions of Which Exonerees Deserve Compensation
Bibliographic record
Abstract
Many exonerees do not receive compensation from the state after they are released (43%) because most states have exclusionary laws that bar exonerees from receiving compensation (n = 16 states) (Compensation Primer, 2022). This study examined public perceptions of exoneree compensation, exclusionary laws and addressed the broader question of who deserves compensation (according to community members). Online participants (n = 225) read an article about a fictional exoneree who either pleaded guilty or was convicted by a jury trial and who either did or did not have a subsequent conviction. An exoneree with a subsequent conviction was perceived as less deserving of financial compensation (roughly $8,000 less annually) and less deserving of support services. It was rated less positively than an exoneree who did not have a subsequent conviction. No differences were found between an exoneree who pleaded guilty and an exoneree who was convicted by a jury trial, demonstrating little impact of this common exclusionary rule on community members’ perceptions and decisions. Overall, participants overwhelmingly supported exoneree compensation (only 6.7% disagreed). However, there are caveats. Community members are less supportive of compensating exonerees who have subsequent involvement with the justice system. These results illustrate possible biases the public has against an already marginalized population that has experienced a miscarriage of justice. Because public opinion can affect policy change, these results significantly affect state exclusionary rules and exoneree compensation policies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".