Opinion Versus Reality: How Should Wrongfully Convicted Individuals be Compensated Versus How They Are Actually Compensated
Bibliographic record
Abstract
Securing compensation following exoneration is an important step for wrongfully convicted individuals in getting some semblance of a normal life post-release. This study seeks to determine what the public believes to be fair compensation for individuals who were wrongfully incarcerated for ten years prior to exoneration, as compared to how much compensation a state would offer the same exoneree. Prior research has tracked what compensation is offered to exonerees through state statutes and detailed difficulties in securing compensation at trial, yet little is known about how statutory compensation compares to what the public believes exonerees should receive. Through two experimental surveys, the current study surveys over 200 students and online respondents to determine how much compensation is fair to individuals, and compares these amounts to what states give to qualifying exonerees. Results indicate that individuals give more compensation on average to a fictional exoneree than do state governments; though the dollar amounts were not statistically significantly different, respondents gave millions more to exonerees than did state statutes. The significance of these findings and avenues for future research are examined.
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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 | 0.015 | 0.067 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".