Post-Conviction Disclosure in the Canadian Context
Bibliographic record
Abstract
It is common knowledge that the criminal justice system is fallible and prone to human error. The most egregious of such errors is the conviction of an innocent person. While wrongful convictions have been acknowledged in Canada in the last few decades, they are mostly regarded as rare and extraordinary events.16 In response to this perception, experts have identified the challenge of determining the number of wrongful convictions and their exact causes.17 A 2019 study estimates that at least 85 people have been exonerated in Canada.18 The recent advent of the Canadian Registry of Wrongful Convictions creates a centralized location for documenting identified wrongful convictions in Canada.19 In the few months it has been operating, the overall number has steadily increased. In the US, wrongful conviction scholars have estimated that wrongful convictions may be as high as 1% of all convictions.20 Even if the number in Canada is half of that estimate, with over 140,000 convictions in Canadian criminal courts in 2019-2020 alone,21 one can estimate that only a tiny fraction of wrongful convictions have been identified in Canada. If the error rate for wrongful convictions was an extremely low (such as, 0.05%), this would still result in approximately 70 miscarriages of justice per year.
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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 | 0.006 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.012 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".