Justice for Less or Justice for More?
Bibliographic record
Abstract
Miscarriages of justice encompass more injustice than wrongful convictions or proven innocence. Proven innocence is the most severe rationing of justice, but it is popular, especially for non-lawyers and in mass imprisonment societies such as China and the United States. Originally used as a rationale for compensation in the United States, it now also rations post-conviction relief. It has been used to ration compensation in England since 2014 but was rejected in the 2024 Canadian reforms, creating a Miscarriage of Justice Review Commission. Some Australian states have been attracted to it in recent legislation, but the Chamberlain and Folbigg wrongful convictions have properly been corrected because of reasonable doubts about the guilt of the two women. Following Ronald Dworkin, there needs to be greater concern about inequality in the distribution of the risks of injustice. The danger of wrongful conviction reforms providing justice for a few while legitimating injustices for many is most acute in authoritarian societies such as China, but not absent in democracies. Comparative law, legal process and historical analysis can contribute to richer understandings of miscarriages of justice. Two different future scenarios, one that provides justice for less and another that provides justice for more, are outlined.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.019 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".