Improving Wrongful Conviction Review: Lessons from a Comparative Analysis of Continental Criminal Procedure
Bibliographic record
Abstract
The study of wrongfuil conviction has yielded much evidence outlining that factors such as mistaken identification, false confessions, unsavoury informants, and misconduct on the part of the prosecution, defence, and police, inter alia, are causes of wrongfuil conviction common to most, if not all, criminal justice systems. Despite the resurgence of scholarly and popular interest in the phenomenon of wrongful conviction, there are a number of gaps in our knowledge and there is little scholarship available that addresses the subject of this article.In this article, the author addresses the question posed by Professor and Dean of Social Ecology (University of California — Irvine) C. Ronald Huff: "Are some criminal justice systems more likely to produce wrongful convictions than others?" The author undertakes a comparative study of criminal procedure in France and Germany in order to critique and appraise the Canadian approach to wrongful conviction review. He argues that incorporating specific elements of Continental practice into our domestic procedures would substantially increase and improve the opportunities for correcting miscarriages of justice in Canada.
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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 | 0.143 | 0.340 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.020 | 0.028 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".