Identifying and Charging True Perpetrators in Cases of Wrongful Convictions
Bibliographic record
Abstract
True perpetrators—those who commit crimes that others were wrongfully convicted of—are a danger to society. Left unapprehended, these individuals often continue to commit crimes that could have otherwise been avoided. Despite the risk they pose, only about half of true perpetrators in DNA exoneration cases have been identified. Further, only 50% of those who have been identified have been charged with the wrongful conviction crime(s) they committed. Previous research on wrongful convictions, prosecutorial discretion in charging decisions, and prosecutors’ treatment of post-conviction innocence claims provide a starting point for investigating what factors underlie the identification and charging of true perpetrators. To explore these factors, we analyze 367 DNA exoneration cases and the resulting 161 identified true perpetrators. Results revealed that prosecutorial misconduct as a contributor to the wrongful conviction decreased the odds that a true perpetrator would be identified, but the odds increased if the victim was White and the exoneree was Black compared to if both were White. Odds of identification also decreased when, compared to murder, the most severe wrongful conviction crime type was child sex abuse or sexual assault. These factors were not significantly associated with the odds of an identified true perpetrator being charged with a wrongful conviction crime. A qualitative study revealed both definitively prohibitive and potentially influential factors that could influence a prosecutor’s decision not to charge an identified true perpetrator with these crimes. These findings indicate policy solutions that could hold true perpetrators of wrongful convictions crimes responsible for their actions.
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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 | 0.012 | 0.083 |
| 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 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".