Miscarriages of Justice, Wrongful Convictions and Proven Innocence as Means of Rationing Justice
Bibliographic record
Abstract
This chapter defines the different terms “miscarriage of justice,” “wrongful convictions” and “proven innocence.” Although these terms are often used interchangeably with differences ascribed to customs and semantics, there are critical differences between them. Miscarriages of justice is the broadest term. In some definitions, it can include any violation of rights. In the criminal context, miscarriages of justice can include unfair trials and unwarranted pre-trial detentions. A wrongful conviction is a narrower term that requires a conviction that is subsequently overturned. As measured in recently developed registries, wrongful convictions are convictions overturned on the basis of new evidence relevant to guilt or innocence. Finally, the narrowest term is proven innocence. This approach is most popular in the United States, where it is also called factual or actual innocence. It was pioneered by Edwin Borchard and used by innocence projects. Formalistic arguments that proven innocence does not violate the presumption of innocence are critiqued. Consistent with Guido Calabresi’s and Phillip Bobbitt’s tragic choice theory, the use of the different terms differs over time and place, and they are used to ration justice.
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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 | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.036 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".