Thirty Years of Innocence
Bibliographic record
Abstract
Systematic reporting of data about wrongful conviction cases in the United States typically begins with 1989, the year of the country’s first post-conviction, DNA-based exonerations. Year-end 2018 thus concludes a full thirty years of information and marks a propitious time to take stock. In this article, we provide an overview of known exonerations, innocence advocacy, and wrongful conviction-related policy reforms in the U.S. during these three decades. First, we provide a brief history of wrongful convictions in the U.S. before turning to the modern era of innocence. We describe the key sources of data pertaining to wrongful convictions and exonerations. Then, using case data from the National Registry of Exonerations, we offer a detailed analysis of national and state-by-state trends in exonerations, including annual totals, DNA- and non-DNA-exonerations, and capital case exonerations. Our examination includes factors corresponding to sources of error, state death-penalty status, and regional differences. We then discuss innocence advocacy organizations, with a particular focus on Centurion Ministries and members of the Innocence Network. This is followed by an examination of state-by-state trends in innocence-related policy reforms on key issues as identified by the Innocence Project. The final section of the article discusses the many important matters we do not yet know about wrongful convictions and poses thoughts, questions, and ideas for continued scholarship focusing on miscarriages of justice. The Appendix provides state-by-state summaries of select information relating to wrongful convictions and innocence reforms.
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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 | 0.010 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".