Comparative Reflections on Miscarriages of Justice in Australia and Canada
Bibliographic record
Abstract
This article identifies comparative scholarship as a promising way to understand the causes of and remedies for wrongful convictions. The article starts by suggesting that a string of high-profile DNA exonerations and public inquiries examining their systemic causes have led to Canadian judges and prosecutors accepting the reality of wrongful convictions more readily than most of their Australian counterparts. The next part of this article suggests that Australian legislatures have been more active than the Canadian Parliament in regulating police and prosecutorial behavior that contributes to wrongful convictions. In turn, the Canadian judiciary has been more creative in responding to the causes of wrongful convictions than the Australian judiciary. This theme is carried over to the next part which examines Australian legislative innovations such as second appeals based on fresh and compelling evidence and mechanisms for courts to conduct their own inquiries. Except for some 2002 reforms to the petition procedure, most reforms in Canada have come from the courts. They include the Supreme Court of Canada hearing fresh evidence or remitting cases to Courts of Appeal to do so and the granting of bail pending petition decisions by the executive and judicial review of such decisions. Australia and Canada can learn from each other in order to ensure that both legislatures and courts respond to wrongful convictions and that, where appropriate, there be both systemic and individual accountability for 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 | 0.008 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.045 | 0.020 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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".