Bibliographic record
Abstract
Discoveries of wrongful convictions have increased substantially over the last several decades. During this period, practitioners and scholars have been advocating for the adoption of policies aimed at reducing the likelihood of convicting a person for a crime they did not commit. Implementing such policies are vitally important not only because they help ensure that the innocent do not receive unwarranted sanctions or that the guilty go unpunished but also because cases of wrongful conviction can erode public confidence in the criminal justice system and trust in the rule of law. To avoid such outcomes, many states have adopted policies through legislation that aim to reduce system errors. It remains unclear, however, why some states appear more willing to provide due process protections against wrongful convictions than others. Findings suggest that dimensions of racial politics may help explain the reluctance of some states to adopt protections against wrongful convictions. Specifically, interaction terms show that states with a Republican governor and a large African American population are the least likely to adopt policies aimed at protecting against wrongful convictions. We thus identify important differences in the political and social context between U.S. states that influence the adoption of criminal justice policies.
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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 | 0.016 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".