Bibliographic record
Abstract
The Canadian Registry of Wrongful Convictions www.wrongfulconviction.ca .like similar registries in the United States and the United Kingdom, was designed to facilitate research on patterns and trends in wrongful convictions. As of its launch in February 2023, 15 of 83 remedied wrongful convictions or 17% were the result of guilty pleas by the accused. This is a similar percentage as found in a UK registry and lower than the 27% of guilty plea wrongful convictions found in the US registry. Forty percent of the guilty plea wrongful convictions were entered by women. Most of these involved the flawed expert testimony of Charles Smith about the cause of baby deaths and the majority of all remedied guilty plea wrongful convictions were for imagined crimes that did not happen. Almost half (7 of 15) of Canada’s false guilty pleas were taken from racialized people including three Indigenous men, one Black and Indigenous man, another Black man and a Brown man who had recently immigrated from India. Two of the fifteen false guilty pleas were taken from accused persons who had diagnosed mental health and cognitive challenges. With the exclusion of one false guilty plea to a mandatory sentence of life imprisonment and ineligibility for parole for 10 years, the average sentence in the remaining 14 cases was 10 months with evidence of “lop-sided” pleas especially in the cases involving Charles Smith and 2 of the 14 received sentences of time already served.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.015 | 0.002 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".