Bibliographic record
Abstract
The discovery of wrongful convictions in Anglo-American systems over the last twenty years has shaken confidence in the adversarial system of criminal justice. The first part of this article will assess the main identified causes of wrongful convictions in Anglo-American systems through the lens of what they reveal about the limits of the adversary system. Six main causes will be discussed, namely mistaken eyewitness identification, lying witnesses, false confessions and false guilty pleas, faulty forensic evidence, tunnel vision or confirmation bias, and inadequate defense representation. The second part of this article will assess possible remedies for wrongful convictions in Anglo-American systems through the lens of the extent to which they attempt to improve the adversarial system and the extent to which they adopt practices that use inquisitorial methods of investigation.The third part of the article will discuss reform proposals for preventing and remedying wrongful convictions that explicitly or implicitly draw on inquisitorial ideals. It will be suggested that many adversarial systems can easily accommodate inquisitorially inspired reforms. Finally, this article will draw some conclusions about what wrongful convictions can tell us about adversarial and inquisitorial systems. The weaknesses and blind spots of each system will be examined as a prelude to suggesting that combining aspects of adversarial and inquisitorial systems can best prevent and remedy wrongful convictions. Each system can and should learn from the other in order to better prevent and remedy wrongful convictions.
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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 | 0.020 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.014 | 0.056 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.008 |
| 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".