Bibliographic record
Abstract
This study compared the causes of wrongful convictions in Canada, the United States, the United Kingdom, Australia, and New Zealand to a) determine the main causes of wrongful convictions in different countries, b) determine if the cause(s) of wrongful convictions significantly differ between each country, c) determine what, if any, recommendations arose from these cases, and d) if any of these recommendations could be implemented in a Canadian setting. The main causes were witness perjury, forensic error, and procedural error (Canada), witness perjury (US), witness perjury and police misconduct (UK), police misconduct (Australia), and procedural error (New Zealand). Kruskal-Wallis tests indicated significant differences in distribution between these countries for medicolegal death investigations, bitemark analysis, procedural error, police misconduct, inadequate legal defence, eyewitness misidentification, and witness perjury. Objectives c and d were addressed through a content analysis resulting in the following five themes emerging: lack of accountability, education, accessibility, discrimination, and post exoneration.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".