Innocence Mortality Tax: The Impact of Wrongful Conviction on Lifespan
Bibliographic record
Abstract
The wrongful conviction of innocent individuals is a growing problem for those unjustly convicted and the integrity of our legal system, with exonerees often struggling post-exoneration. Yet, too little is known about the long-term impact of wrongful convictions on those unjustly convicted. Thus, we investigated the effect of wrongful conviction on mortality and lifespan—that is, we tested for the possibility of an “innocence mortality tax.” We found that more exonerees have passed than expected when compared to U.S. death rates, and that exonerees died 13.24 years earlier than expected, given their age, gender, race/ethnicity, and incarceration length. Finally, those exonerees whose cases involved a false confession or mistaken eyewitness identification died significantly sooner than their counterparts. Our results highlight the need for researchers, practitioners, and policymakers to continue to find ways to mitigate the harm done to innocent individuals unjustly convicted.
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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 | 0.007 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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; both teacher heads agree on what is shown here.
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".