Bibliographic record
Abstract
This article finds that Indigenous people are over-represented among the wrongfully convicted in relation to their representation in the population in both Australia and Canada. At the same time, there are likely many undiscovered wrongful convictions of Indigenous persons especially when the over-representation of Indigenous men and women in prison is considered. A factor in this likely under-representation of Indigenous people among remedied wrongful convictions may be the incentives that accused, especially Indigenous women, face to plead guilty even if they are not guilty. This finding underlines some of the dangers of limiting wrongful convictions to cases of proven factual innocence and not including among the wrongfully convicted those who may have valid defences such as self-defence. The immediate causes of the wrongful convictions of Indigenous people examined in this article include false confessions, mistaken eyewitness identification, lying witnesses, lack of disclosure and forensic error. Underlying and deeper causes include disadvantages that Indigenous people suffer in the criminal justice system including language and translation difficulties, inadequate and insensitive defence representation, pressures to plead guilty and racist stereotypes that associate Aboriginal people with crime.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".