Wrongful Convictions and Miscarriages of Justice in Comparative Perspective: Preface and Introduction
Bibliographic record
Abstract
When we published an earlier book1 on wrongful convictions with a crossnational, comparative focus, we noted that the extant literature on wrongful convictions seldom included cross-national perspectives. That book included a number of “nation reports,” describing and analyzing wrongful convictions in the context of an array of different criminal justice systems in the U.S., Canada, a number of European nations, and Israel. It stimulated considerable discussion concerning topics such as the causes of wrongful convictions; the respective advantages and disadvantages of the adversarial and continental/inquisitorial systems of justice; how the incidence of wrongful convictions might best be reduced; and other important topics. In the United States, the International Division of the National Institute of Justice, acting in part in response to the book, held a conference on this important subject, bringing together scholars and policymakers from a number of nations on several continents to discuss the challenges posed by wrongful convictions and how we might learn from each other’s experiences. The conference included a keynote address and resulted in a comprehensive report.2
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.021 | 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; 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".